412results about How to "Generate efficiently" patented technology

The invention discloses an intelligent supply chain scheduling optimization method and system based on an autonomous controllable large model, and the method comprises the steps: employing a demand prediction agent to predict a future market demand condition according to product sales data; according to supplier supply product data, adopting a supplier management agent to select an optimal supplier as a target supplier; according to the predicted future market demand condition and inventory data, an inventory optimization agent is adopted to generate an optimal inventory replenishment plan; according to the inventory level after the optimal inventory replenishment plan and the logistics transportation data, adopting a logistics scheduling agent to calculate and obtain an optimal transportation path; feedback data of all the agents are collected, performance indexes of all the agents are calculated through the information feedback agents according to the feedback data, and agent parameters are adjusted according to the performance indexes of all the agents; and the strategy output of each agent is integrated into the scheduling strategy of the supply chain. The scheduling strategy can be dynamically scheduled and updated in real time.

The invention discloses a warehouse level code problem solving system based on a large language model, and relates to the technical field of software engineering, a source code file is extracted from a code warehouse, a static analysis tool is utilized to analyze codes, a warehouse level code graph is constructed, and then code entities and dependency relationships thereof are analyzed; based on the warehouse-level code graph, combining historical problem solving data, and constructing a dynamic evolution knowledge graph through continuous learning; through a context retrieval module, according to the context information of the current problem, dynamically retrieving related solution strategies and generating corresponding code patches, complementing or reviewing suggestions; the validity of the generated patch is verified through static analysis, unit testing and integration testing, and a problem solving strategy is optimized according to feedback of a developer. The problem solving efficiency and accuracy can be improved, the development cost is reduced, the solving process is gradually optimized through an intelligent feedback mechanism, a closed-loop feedback mechanism is formed, and therefore the increasingly complex software development requirements are met.

The invention discloses an image generation method based on a mixed quantum classical condition generative adversarial network. The method comprises the following steps: firstly, converting a random condition label and a sample real condition label into one-hot codes, processing input noise and the random condition label by using a quantum circuit, performing measurement to convert a characteristic quantum state into a classical characteristic vector, and obtaining generated image data from the characteristic vector through a classical single-layer neural network. And transmitting the sample real image data and the real condition label into a discriminator to obtain a real image discrimination result, and transmitting the generated image data and the random condition label into the discriminator to obtain a generated image data discrimination result. And calculating discrimination loss based on the two discrimination results, training a discriminator and a quantum condition generator, obtaining a trained mixed quantum classical condition generative adversarial network, and generating a condition image. Based on the above scheme, the problems of unstable classical GAN training and many training parameters are solved, the limitation of the quantum GAN in a condition generation task is broken through, and the application of the quantum generative adversarial network in scenes such as image generation and pattern recognition in the field of real artificial intelligence is further promoted.

The invention discloses a system and a method for security enhancement of generated content information of a large model, and the method comprises the steps: firstly, carrying out the bidirectional LoRA fine tuning technology and knowledge distillation technology on the large model, and enabling the large model to adapt to a factual output task from the illusion dimension and the factual dimension; performing extraction of intermediate layer representation in a fact space on the fine-tuned large model, and obtaining different mappings from fine-grained illusion types to a fact direction through a comparative learning method; and finally, in the reasoning generation process of the large model, extracting the representation of the middle layer of the large model to judge the potential illusion type, then applying the learned mapping corresponding to different illusions to the middle representation of the reasoning process, correcting factual representation, reducing the illusion output probability, and in the pre-training stage and the reasoning generation stage, obtaining the illusion type of the large model. Parameters and generated contents of the large model are controlled, and the factuality and reliability of the generated contents of the large model are greatly improved while the generation capability of the large model is guaranteed.

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention discloses a large model security test method. The method comprises the following steps: generating attack data based on a first model; checking the attack data based on a second model to obtain a first checking result; the first test result represents whether the attack data is valid; under the condition that the first test result represents that the attack data is valid, inputting the attack data into the tested large model to obtain response data of the tested large model; the response data are evaluated based on the second model, an evaluation result is obtained, and the evaluation result represents the safety of the tested large model.

The invention relates to the technical field of battery management, in particular to a new energy vehicle battery data analysis method and system, and the method comprises the following steps: merging a state feature set of a battery based on the charging and discharging period and use environment data of the new energy vehicle battery, extracting the voltage, current and temperature features of the state, and obtaining a state feature set; and carrying out feature combination on the battery degradation state, classifying and associating feature items and temperature features in the feature combination, and obtaining a state feature merging result. According to the method, through merging the new energy vehicle battery charging and discharging period and the use environment data, the battery state characteristics are accurately extracted, refined classification and correlation analysis are realized, the discrimination capability and adaptability of the state data are improved, and the state transition trend is flexibly captured based on the linkage trend of the state transition frequency and the current observation data; and dynamic evaluation is realized through correlation of periodic temperature and current, an accurate and reliable state change evaluation scheme is provided, and the dynamic response of battery management and the full-life periodic performance regulation and control quality are optimized.

The invention discloses a soil humidity fusion method based on XGB-SHAP-RFE feature optimization and SHAP interpretable analysis, and the method comprises the following steps: (1) collecting and sorting multi-source soil humidity related data, and forming a unified data set; (2) constructing an XGB-SHAP-RFE network, and preferably selecting key features from the data set; (3) training a fusion model by using the optimized key features and evaluating the performance of the fusion model; (4) interpretable analysis is carried out on model output based on an SHAP method, and contribution of each feature is quantified; (5) generating multi-source fused high-quality soil humidity data in combination with a model result; the invention aims to efficiently generate a high-precision and high-resolution soil humidity fusion product.

The invention discloses a game generation method and device, a storage medium and computer equipment. The game generation method comprises the steps of obtaining a game development demand based on natural language interaction; generating game content data based on the game development demand; and driving a game engine based on the game content data to obtain a target game. By means of the method, the game content data can be automatically generated according to the game development requirements, the target game capable of driving the game engine to run is finally generated, efficient and high-quality game generation is achieved, the game development efficiency is improved, the game development threshold is lowered, the game development requirements of more developers are met, and the game development efficiency is improved. The illusion problem occurring in the game code generation task is relieved, the game code capable of well running is generated, and the adverse effect of redundant codes existing in the generated game on the game performance is avoided.

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 provides an image generation method and device, equipment, a medium and a program product. The method comprises the following steps: S101, carrying out autoregressive processing on a pre-filling token and a hidden space token with a plurality of resolutions through an autoregressive Transform model of an image generation network, and generating an input condition; wherein the hidden space token is obtained by encoding an original image by using an encoder; s102, carrying out up-sampling operation on the generated input condition; s103, inputting the up-sampled input condition into a diffusion model of the image generation network, performing diffusion denoising on the input condition through the diffusion model, and generating a hidden space token of the current resolution; s104, judging whether the current resolution reaches the target resolution or not; if yes, executing the step S105, and if not, returning to the step S101; and S105, restoring the hidden space token into an image through a decoder to obtain a target image.

The invention discloses a method and a model for generating a quantum state set, and the method comprises the following steps: adding depolarization noise to any target quantum state through a non-unitary forward process, and converting the target quantum state into a completely mixed state; and de-noising the complete mixed state by using a trainable reverse process, and recovering the complete mixed state to the target quantum state. According to the method, more stable convergence performance is achieved based on the convex optimization method, the pure state and the mixed state can be effectively generated, excellent fidelity is achieved, and the huge potential in the aspect of coping with complex quantum generation tasks is shown.

The invention relates to the technical field of road engineering, in particular to a large language model-driven road change design method, which comprises the following steps of: 1) performing rough selection by using a large language model and a prompt project, and selecting a corridor zone; and 2) performing optimization through an improved artificial potential field MAPE algorithm. According to the method provided by the invention, by applying a natural language processing technology, automatic generation of a preliminary corridor belt design scheme is realized, the manpower and time cost of a route pre-selection stage is saved, a large number of alternative schemes can be produced to assist design, and the optimization effect is also higher than that of a relatively mature genetic algorithm; the method is a successful exploration of a large language model in intelligent road line selection application.

The invention discloses injectable hydrogel for eye drug sustained release and a preparation method and application thereof.The method comprises the steps that a NaIO4 solution is dropwise added into a hyaluronic acid solution, after the reaction is conducted in a dark place at the room temperature, ethylene glycol is added for continuous reaction, and formylated hyaluronic acid is prepared; the preparation method comprises the following steps: adding adipic dihydrazide into a carboxymethyl cellulose solution, uniformly stirring at room temperature, adding 1-hydroxybenzotriazole and 1-ethyl-(3-dimethylaminopropyl) carbodiimide hydrochloride, and stirring to react, so as to prepare aminated carboxymethyl cellulose; and mixing the PBS solution of aminated carboxymethyl cellulose with the PBS solution of formylated hyaluronic acid, uniformly stirring, adding a to-be-carried drug and a FeCl3 solution, and carrying out a reaction to prepare the injectable hydrogel for sustained release of the eye drug. The hydrogel is convenient to use and high in gelation rate, effectively realizes controllable release of drugs, and has a wide application prospect in the field of biological medicines.

The invention relates to the field of computers, and provides an optimal design method, device and equipment for a three-dimensional slope inclined anti-slide pile and a medium. The method comprises the following steps: generating a dynamic library file of a soil constitutive model, and importing the dynamic library file into universal interactive modeling software; setting material attributes for the three-dimensional model, defining boundary conditions, and dividing grids; performing static analysis on the three-dimensional model to generate a visual toolkit file; extracting material point information in the visual toolkit file, and packaging the material point information into a material point file; determining pre-prepared material point method three-dimensional data analysis software; reading the material point file and carrying out dynamic analysis; generating an unstructured grid file of the visual toolkit file in the dynamic analysis process; extracting a slope failure state variable from the unstructured grid file of the visual toolkit file; and by taking the slope failure state variable as an optimization target, adjusting the three-dimensional model to obtain an optimal anti-slide pile design scheme.

According to the train fault remote early warning method and system based on artificial intelligence, the multi-dimensional state path vectors in the target train operation data are accurately extracted and fused to generate the first train state characteristic spectrum, and comprehensive and accurate representation of the train operation state is achieved. In combination with prior early warning guiding knowledge data, the method effectively guides a train fault early warning decision model to perform accurate early warning decision on target train operation data by using an interactive focusing feature analysis technology. And by introducing the first feature flow control parameter to optimize the interactive focusing feature data, a more targeted target guidance knowledge vector is generated, and the accuracy and reliability of fault early warning are further improved. Finally, the train fault early warning knowledge points can be remotely generated in real time, powerful technical guarantee is provided for safe operation of the train, the occurrence rate and the influence range of train faults are remarkably reduced, and the safety and the efficiency of railway transportation are improved.

The embodiment of the invention discloses a power grid information display method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring a real-time transmission, transformation and distribution equipment data set; generating an abnormal operation equipment information set; in response to determining that the set is not the null set, determining abnormal region information; for the abnormal region information, executing a first generation step: determining an operation equipment information set; performing abnormal degree region division on the abnormal region information; determining an equipment problem information set and a corresponding problem probability for each piece of abnormal division region information; screening out the equipment problem information meeting the corresponding problem conditions, and obtaining at least one piece of target equipment problem information; and displaying the abnormal division region information sequence and the problem information of the at least one piece of target equipment in the power digital twin model. According to the embodiment, under the condition that the target power grid region is abnormal, the abnormal information under the region granularity and the equipment granularity can be accurately and effectively generated.

The invention discloses a seamless image restoration method based on regional parallel adaptive pixel consistency generation, and the method comprises the steps: receiving an original image and a shielding mask image, obtaining the features of a multi-scale image through Gaussian pyramid decomposition and an edge detector, and achieving the adaptive region division and priority sorting through a watershed algorithm; extracting structural features by using a feature pyramid network and a non-local self-attention mechanism, and establishing a regional association graph through multi-scale attention calculation; generating a scheduling table based on the region priority and the association relationship, and performing parallel region generation; and calculating pixel continuity and structural consistency between adjacent regions, realizing multi-scale fusion, and generating a final restored image. The visual continuity and the structural integrity of the repairing result can be effectively guaranteed, and meanwhile the processing efficiency is improved.

The invention discloses an equipment test model code generation method based on a large model, and the method comprises the steps: designing an equipment digital test demand analysis module which is used for constructing an equipment digital test identification framework system through combining demand description to extract a digital test demand generated by an equipment test model modeling code; designing a large model training code collecting and processing module, and generating a high-quality code data set; the design equipment test model code library module is used for carrying out structured storage on the high-quality code data set, constructing a knowledge graph based on structured data and designing a vectorization dynamic query function; and designing a large model pre-training and fine tuning module, and performing secondary pre-training and fine tuning on the base code large model by using the high-quality code set and a vectorization dynamic query function to generate a final equipment test model code. According to the method, the code generation speed and accuracy are remarkably improved, and it is ensured that the generated code has wide adaptability and universality.

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 invention belongs to the technical field of indoor scene synthesis, and particularly relates to an open domain indoor scene hierarchy generation method and system based on a large language model. Firstly, cue words are constructed according to user requirements and definition of a layered scene structure by utilizing a layered scene generation module, and a pre-training large language model is guided to output a structured text to describe layered scene representation, so that contradictory placement caused by a complex and dense spatial relationship is avoided. Then, in order to avoid the problem of inaccurate object placement caused by a rough spatial relationship, a fine-grained relative position inference module is adopted to further infer fine-grained relative positions, which are difficult to describe by text phrases, between objects with a text spatial relationship. The module may infer a reasonable relative position in open domain settings. And finally, optimizing each functional area by utilizing a divide-and-conquer layout optimization module, and then arranging the functional areas to form the whole scene, so that a physically feasible scene layout can be effectively generated.

The invention is suitable for the technical field of intelligent production, and provides a processing quality monitoring method and system based on an injection molding production line, and the method comprises the steps: firstly, rapidly obtaining the to-be-detected image information of a target mold cavity based on a camera, then effectively generating the to-be-detected contour information according to the to-be-detected image information and a contour extraction algorithm, and carrying out the detection of the to-be-detected contour information. And according to the to-be-detected contour information and the reference contour information, accurately generating plastic residual area information, and finally, based on the plastic residual area information, timely generating optimized production parameter set information. The processing quality of the injection molding production line can be monitored in real time, when residues are detected in the mold, the injection molding process parameters are intelligently and automatically adjusted, it is guaranteed that the residues in the mold are thoroughly discharged, the reliability of the injection molding production line is effectively improved, the stability of the production process is remarkably improved, and the production efficiency is improved. And the material waste condition and the generation of unqualified plastic products are reduced, and the sustainable development transformation of an injection molding production line is facilitated.

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based enterprise digital management maturity evaluation method, which comprises the following steps of: performing sample generation by adopting a generative adversarial network algorithm based on a momentum optimization variant to realize data expansion; inputting the expanded enterprise management data into a feature extraction model to train the feature extraction model; inputting the enterprise management data after feature extraction into a feature dimension reduction model to train the feature dimension reduction model; inputting the dimension-reduced enterprise management data into a classifier model to train a classifier; and processing new sample data by using the trained model so as to evaluate the digital management maturity of the new sample data.

The invention discloses a model and data collaboratively driven VR panoramic fusion expression method and system, belongs to the technical field of virtual reality, and solves the problem that an existing panoramic image display mode lacks deep coupling with a geographic scene object simulation model and is difficult to accurately describe a complex geographic scene object and a time-space evolution process thereof. The method comprises the steps of performing fusion modeling on each geographic scene object and a corresponding geographic scene object in a real-world panoramic image collected in real time based on geographic scene space semantic constraints to obtain a VR panoramic fusion scene; and performing digital-analog associated enhanced expression on the VR panoramic fusion scene based on an attribute semantic information rule and the user visual region of interest to obtain a VR panoramic scene, and updating the VR panoramic scene in real time by using dynamic data. The method is used for VR panoramic fusion expression.

The invention belongs to the technical field of medical images and deep learning, and particularly relates to a non-paired cross-modal medical image conversion method based on contour guide path regularization, which comprises the following specific steps: constructing a conversion path from a source domain to a target domain; a path regularization method is applied to smooth path transition and ensure the stability in the image conversion process; according to a path regularization technology based on contour guidance, the model can be focused on a key anatomical region, the structural consistency and accuracy of the generated CT image are further improved, and the training time can be remarkably shortened by reducing additional training steps. The invention aims to simplify the unsupervised training normal form and overcome the defects of the traditional GAN-based single-step generation method in the aspect of capturing the complex anatomical structure of the medical image. While the training time and the sampling time are considered, the problem that the quality of the generated medical image is limited is overcome.

The invention discloses a multi-modal semantic topology and constraint reasoning CAD sequence generation method based on a time scale, and belongs to the technical field of crossing of computer aided design and artificial intelligence. According to the method, an end-to-end automatic generation framework from multi-modal input to an editable parameterized CAD model is constructed, and semantic consistency, structural rationality and logic correctness of a generated sequence are remarkably improved; according to the method, multi-source data such as images, sketches and point clouds can be deeply fused, a design intention is accurately captured through feature extraction of time scale perception and dynamic topological graph evolution, and meanwhile, the problems of error accumulation and logic drift in a traditional autoregression model are effectively solved through a sequence synthesis mechanism guided by grammar and geometric constraints; and in combination with a geometric feedback driving optimization engine, efficient and steady generation of the parameterized model is realized, so that systematic breakthrough in the aspects of generation quality, automation degree and cross-scene applicability is realized, and a key technical support is provided for intelligent manufacturing and intelligent design.

The invention relates to the technical field of point cloud data processing, in particular to a Delaunay triangulation network generation method based on point cloud data and computer equipment. According to the method, the triangulation network of each block is generated by numbering, blocking and overlapping the blocks according to a certain proportion and determining a public area and a non-public area (deletion area) of the overlapped part, in the generation process, key parameters are set, and the Delaunay triangulation network is quickly, efficiently and accurately established through reasonable parameter setting. In the triangulation network generation processing of each block, triangles formed by non-public area points at the starting end and the tail end of the current block are removed, and deduplication processing is carried out on triangles in an overlapped area, so that the triangulation network covered by the whole area can be quickly and efficiently generated by using the generation method disclosed by the invention; the Delaunay triangulation network can be generated for continuous (uniform) or discontinuous (non-uniform) point cloud data, and favorable data support is provided for generating a contour line topographic map, calculating the excavation and filling area of the cross section of the roadbed or generating a three-dimensional real scene model.

The invention discloses an electric power unmanned aerial vehicle investigation 3D imaging system and a measurement method, and relates to the field of unmanned aerial vehicle electric power investigation. An unmanned aerial vehicle carrying a visible light and thermal imaging camera is adopted to move in the air so as to investigate and collect visible light image data and thermal imaging image data of an electric power network; identifying network nodes and connecting lines through a deep learning algorithm, and constructing a two-dimensional topological graph; weight distribution is carried out on nodes in combination with temperature data in thermal imaging image data, an inspection path is planned by using an improved genetic algorithm, and the total inspection path is reduced while high-risk nodes are inspected preferentially; in the inspection process according to the inspection path, node three-dimensional coordinates and visible light and thermal imaging images are obtained through an unmanned aerial vehicle sensor, a power network three-dimensional topological graph is constructed, and fault nodes are marked. The method has the advantages that the three-dimensional topological graph is quickly generated, fault nodes are preferentially detected, and the total routing inspection distance is remarkably reduced.

According to the construction scheme multi-modal retrieval enhancement generation method and system based on the large model, intelligent scheme writing support is provided for constructors through a natural language processing technology by means of mass building industry data and knowledge, and the construction efficiency is improved. Comprising but not limited to the steps of assisting constructors in compiling a construction scheme, providing real-time answers to problems possibly encountered in the construction process, providing optimization suggestions according to project characteristics and the like; through the application of the large model, the working efficiency of constructors can be greatly improved, the waste of human resources is reduced, and the accuracy and feasibility of a construction scheme are ensured. Besides, the model can be continuously learned and updated, and the ability of assisting in compiling question answering is continuously optimized along with the accumulation of project experience, so that the model is more widely applied to each link in the building industry in the future.