84results about How to "Achieve automatic optimization" patented technology

The invention provides a cue word template generation method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as natural language processing, large language models and deep learning. According to the specific implementation scheme, a first data set and an initial cue word template corresponding to a target task are obtained, and the first data set comprises sample data, text description corresponding to the sample data and reference answers; determining a first answer corresponding to the sample data according to the initial cue word template, the sample data and the corresponding text description; determining a first evaluation result corresponding to the first answer according to the reference answer and the first answer; and based on the first evaluation result and the text description, optimizing the initial cue word template to obtain a target cue word template.

The invention belongs to the technical field of power robots. The transformer automatic flexible butt joint oil taking system comprises an oil taking box and an oil taking robot, the oil taking robot comprises an oil taking robot chassis, an automatic butt joint device and an oil sample extracting device, and the automatic butt joint device and the oil sample extracting device are installed on the oil taking robot chassis; the oil taking tank comprises a flexible butt joint oil taking opening fixed to an oil taking tank body, a fixed rod and a movable rod assembly, the automatic butt joint device comprises a guiding butt joint mechanism and a flexible butt joint oil taking mechanism, and the guiding butt joint mechanism is used for being matched with the fixed rod and the movable rod assembly for guiding butt joint. The flexible butt joint oil taking mechanism is used for being in flexible butt joint with the flexible butt joint oil taking opening. Manual operation is replaced through automatic cooperation of the oil taking robot and the oil taking tank, the automatic docking device is applied, the operation process is optimized, the operation and maintenance efficiency and safety of the electric power system are improved, and the risk of manual operation is reduced.

The invention discloses a configurable flexible job shop scheduling method based on a multi-agent game model, and the method comprises the steps: mapping a scheduling problem into a dynamic disjunction graph model to describe the relation between a process and equipment, representing process nodes, equipment nodes and the mutual relation through a node set and an edge set, and combining the flexibility of a dynamic disjunction graph to obtain a flexible job shop scheduling model. And matching of equipment and processes is dynamically adjusted according to changes of production requirements, so that the equipment utilization rate is optimized. And a clustering method based on structure entropy is adopted, and process nodes are clustered into sub-graphs, so that the search space of a scheduling problem is reduced, and the calculation complexity is reduced. Construction of an order agent and an equipment agent and a game and cooperation process between the order agent and the equipment agent are covered. And each agent optimizes a decision through a deep Q network, and cooperates or competes according to a respective target. An automatic optimization configurator is used for optimizing hyper-parameters and a network structure of a deep Q network in a multi-agent system, and scheduling performance and adaptability are further improved.

The invention discloses a dust collecting device for producing protective clothing and treating dust, and relates to the technical field of dust collecting devices, the dust collecting device comprises a pre-dust-collecting chamber shell and a second combined flow guiding piece, the upper end of the left side of the pre-dust-collecting chamber shell is connected with an air inlet pipeline, and the lower end of the right side of the pre-dust-collecting chamber shell is connected with an air outlet pipeline; the second combined flow guide part is arranged between the air inlet pipeline and the air outlet pipeline, and the second combined flow guide part is composed of a rectangular outer frame, four second flow guide plates, an angle and distance adjusting mechanism and the like; the dust collector has multiple advantages, the burden of the filter bag can be relieved through the dust pre-collection chamber shell, the first combined flow guide part can flexibly change the included angle between the first flow guide plate and the center axis of the air inlet pipeline by means of the first electric push cylinder according to the flow and concentration of dust-containing air, and the second combined flow guide part can flexibly change the included angle between the first flow guide plate and the center axis of the air inlet pipeline according to the size and concentration of dust particles. The inclination angles of the four second flow guide plates are synchronously adjusted through the angle adjusting mechanism, and the distance between the second flow guide plates is adjusted in a self-adaptive mode through the distance adjusting mechanism.

The invention relates to the technical field of environmental protection, in particular to a garbage incinerator combustion management system which comprises a data acquisition module, a data preprocessing module, a data analysis module, a decision control module and a combustion adjustment module. Wherein the data acquisition module is used for acquiring combustion data in the combustion process of the garbage incinerator; the data preprocessing module is used for preprocessing the collected combustion data; the data analysis module is used for analyzing the preprocessed combustion data in real time and judging the efficiency and the pollutant emission level of the current combustion process; the decision control module is used for generating a combustion adjustment instruction; and the combustion adjusting module is used for adjusting the air flow, the temperature and the fuel input amount in the combustion process. Through real-time data analysis and automatic adjustment and optimization of combustion parameters, efficient control over the combustion process of the garbage incinerator is achieved, the combustion efficiency is remarkably improved, pollutant emission is reduced, and the environmental protection property and economical efficiency are improved.

The invention discloses an unsupervised microseismic denoising method and system based on a multi-channel Kalman filter network, and belongs to the crossing field of earth science and technology and artificial intelligence, and the method comprises the steps: obtaining multi-channel microseismic data, and carrying out the preprocessing; constructing a multi-channel Kalman earthquake denoising network based on the recurrent neural network; constructing a loss function, and carrying out unsupervised training on a multichannel Kalman seismic denoising network based on the preprocessed multichannel microseismic data and the loss function; and denoising is carried out on the real-time micro-seismic data through the trained multi-channel Kalman seismic denoising network. According to the method, the multi-channel Kalman filtering state prediction and update equation is established, the RNN operator is designed, automatic parameter optimization is achieved, and the denoising accuracy and efficiency are improved. The method makes full use of the correlation of microseismic data, improves the filtering effect, is suitable for time-varying signals, and provides support for subsequent data processing.

The invention relates to the technical field of artificial intelligence, in particular to a cue word optimization method and device based on a large model. After the first large model carries out corresponding processing according to the original prompt word to obtain a reply, a feedback text given by a user for the reply is obtained, the feedback text and the first prompt text are input into the second large model, and the second large model analyzes the semantics of the feedback text according to the prompt of the first prompt text; and outputting a target improvement point corresponding to the feedback text according to a set format, namely extracting an adjustment opinion given in the feedback text fed back by a user by using a large model, the target improvement point being used for describing a character to be adjusted in the original cue word and an adjusted target character, and when cue word optimization is performed, according to the target character, performing adjustment on the cue word according to the target character. The to-be-adjusted characters in the original cue word are adjusted, and automatic optimization of the original cue word is achieved.

The invention relates to the field of electrochemical energy storage, and discloses a testing method and a testing system of an all-vanadium redox flow battery, and the method comprises the following steps: describing diffusion, convection and electrochemical reaction processes of electrolyte in a battery flow channel through a partial differential equation, and establishing a dynamic distribution model of concentration gradient and current density; based on the model, an adaptive control algorithm is adopted to dynamically adjust the electrolyte flow velocity and the current density, and stable control of a test environment is realized; acquiring electrochemical impedance spectrum data by applying alternating-current voltage signals with different frequencies, extracting non-linear characteristics in an impedance spectrum by using a long-short-term memory network model, and analyzing and predicting the performance degradation trend of the battery; a multivariable optimization model is established, and parameters such as temperature, flow velocity and current density are dynamically adjusted, so that a test environment is always close to a target value. According to the method, the test precision and the environmental stability can be effectively improved, and reliable technical support is provided for performance evaluation and optimization of the all-vanadium redox flow battery.

The invention belongs to the technical field of design optimization methods of connecting rod jumping mechanisms, and particularly relates to a topology design optimization method and optimization structure of a connecting rod jumping mechanism. According to the method, division of a design domain and a non-design domain is provided based on optimization of the connecting rod jumping mechanism, and jumping mechanism design can be carried out on the premise of ensuring calculation convergence and high efficiency. Meanwhile, after the connecting rod configuration meeting the functional requirements is obtained, a method for optimizing the size parameters of the connecting rod and the number, the rigidity value and the installation position of the energy storage spring based on the angle of energy is provided. Compared with a traditional method for optimizing the size parameters of the connecting rod, the method avoids the situation that the relation between the position of each rod piece and the input angle is established through formula derivation, the position of the rod piece at each moment is obtained based on the energy minimization principle, automatic comprehensive optimization of different connecting rod configurations can be achieved, and the calculation efficiency can be effectively improved.

The invention relates to the technical field of medical vending platforms, and discloses a medical vending platform mobile management method, device and equipment and a storage medium, and the method comprises the steps: creating an initial commodity shelving scheme based on the operation data of a vending machine of a medical vending platform, and collecting the image data of medical commodities; carrying out residual learning and two-stage feature extraction to obtain medical commodity feature data; inputting a medical commodity identification model to carry out medical commodity identification to obtain a medical commodity identification result; carrying out inventory detection, generating commodity inventory data, and sending the commodity inventory data to the mobile management terminal to generate a replenishment task instruction; inputting the RFID tag information and the replenishment task instruction into a replenishment execution unit, generating replenishment operation data, and updating real-time inventory information; the initial commodity shelving scheme is adjusted based on the real-time inventory information and the historical sales data, a target commodity shelving scheme is generated, and the method improves the operation efficiency and the service quality of a medical sales platform.

The invention provides a server parameter optimization method and device, and the method comprises the steps: obtaining a state parameter set and an execution action set of a server cluster, and the state parameter set and the execution action set are provided with time labels; based on the state parameter set and the execution action set, a multi-agent reinforcement learning algorithm is utilized to determine local evaluation prediction parameters; based on the state parameter set and the execution action set, global evaluation prediction parameters are determined by using an adaptive dynamic programming model; and optimizing the execution action set based on the local evaluation prediction parameters and the global evaluation prediction parameters. According to the method disclosed by the invention, the local evaluation prediction parameters and the global evaluation prediction parameters corresponding to the server cluster are determined by using the QMIX model and the ADP model, so that automatic optimization of the execution action set of the server cluster is realized, and the stability and the service efficiency of the server cluster are improved.

The invention provides a dam seepage prediction method based on an improved long-short term memory deep learning model. The method comprises the following steps: firstly, mining five major factors, namely upstream water level, downstream water level, upstream and downstream water level difference, temperature and rainfall, which are strong in seepage of the dam from hydrological characteristics, meteorological characteristics and interactive characteristics of the hydrological characteristics, the meteorological characteristics and the interactive characteristics through a principal component analysis (PCA) method; and jointly forming a time sequence data set required by deep learning model training and testing. Secondly, constructing an improved long-short-term memory deep learning model; the innovation points of the model comprise an improved elite genetic algorithm for precise initialization of network model parameters of binary and real number hierarchical hybrid coding, collaborative elite tournament strategy selection and dynamic comprehensive fitness evaluation, and a network back propagation process parameter learning method based on the improved elite genetic algorithm. And thirdly, performing model training based on the constructed model and the data set, and completing seepage prediction. Finally, on the basis of actually monitored data, it is verified that the method provided by the invention has relatively strong advantages in prediction precision and generalization ability, and the method has relatively strong theoretical value for realizing intelligent and accurate prediction of dam seepage.

The application discloses a spunlace non-woven fabric rewinding equipment collaborative control method and system based on the Internet of Things, and relates to the field of electric digital data processing.The method comprises the following steps: obtaining historical data and preprocessing to obtain a plurality of three-dimensional data sequence sets of roll diameter-torque-tension under normal working conditions; dividing the plurality of three-dimensional data sequence sets into a plurality of roll diameter slice sample groups and screening to obtain a non-woven fabric flat data set; performing nonlinear fitting on the non-woven fabric flat data set and constructing non-woven fabric flat upper and lower tracks; based on the non-woven fabric flat upper and lower tracks, real-time marking of an abnormal data interval in the current rewinding process is performed, and abnormality determination is performed in cooperation with an image detection mechanism; after the current rewinding process is completed, the non-woven fabric flat upper and lower tracks are updated and optimized. The application can dynamically adapt to roll diameter changes, accurately mark abnormal intervals and accurately determine abnormal working conditions in cooperation with an image detection mechanism, thereby effectively improving the quality control level of spunlace non-woven fabric rewinding production.

The invention discloses an isatis root aeroponic planting method and system, and relates to the technical field of negative pressure control, and the method comprises the following steps: determining the optimal position of a planting area, and establishing a system negative pressure area; assembling an adjustable fan for each planting area, automatically adjusting the rotating speed of the fan according to the real-time feedback of the differential pressure sensor, and establishing and maintaining the negative pressure in the planting area; simulating and predicting the air flow direction in the planting area by using CFD software, and dynamically adjusting the air flow direction; the air quality and the pressure difference of each area are monitored in real time, data analysis is carried out, and an alarm is automatically triggered when abnormity occurs. According to the isatis root aerosol culture planting method and system provided by the invention, spatial analysis of the incubator region is accurately carried out, the optimal position of the planting region is determined, the air quality and pressure difference of each region are monitored in real time, the airflow path in the planting region is optimized, the risk of infection diffusion is reduced, and the overall performance and stability of the system are improved.

The present invention belongs to the technical field of connecting rod jump mechanism design optimization methods, and specifically relates to a topological design optimization method and optimized structure of a connecting rod jump mechanism. Based on the optimization of the connecting rod jump mechanism, the present invention proposes the division of design domain and non-design domain, which can ensure the calculation convergence and high efficiency to design the jump mechanism. At the same time, after obtaining the connecting rod configuration that meets the functional requirements, an optimization method for the connecting rod size parameters and the number of energy storage springs, stiffness value and installation position based on the energy angle is proposed. Compared with the traditional method for optimizing the connecting rod size parameters, the method of the present invention avoids establishing the relationship between the position of each rod and the input angle through formula deduction, but obtains the position of the rod at each moment based on the principle of energy minimization, which can realize the automatic comprehensive optimization of different connecting rod configurations and can effectively improve the calculation efficiency.

The present application relates to the technical field of distributed model training, and provides a model distribution parallel training process automatic optimization method, system and device. The method comprises the following steps: defining a task plane according to the communication and computing power of a neural network model, dividing the task plane to obtain a divided task plane; constructing a training time consumption model according to the divided task plane to obtain total time consumption; using a tournament algorithm to design a genetic algorithm to solve the training time consumption model according to the total time consumption to obtain a training optimization result; and embedding the training optimization result into the neural network model for optimization and updating. The present application models the training process in detail under the scene of a mesh pipeline and a heterogeneous network and computing power equipment. According to the established training process-training time consumption model, a meta-heuristic algorithm is applied to search for an optimal solution to realize automatic optimization of the distributed parallel training process.

Embodiments of the present application provide a prompt word generation method and device, equipment, storage medium and program product, relating to the technical field of financial technology and artificial intelligence. The method comprises: collecting first interaction behavior data between a user and a large language model and generating a first interaction feature vector based on the first interaction behavior data; inputting the first interaction feature vector into a first model to obtain a quality score of a first prompt word of the large language model; in the case that the quality score of the first prompt word is lower than a first threshold, generating a rewrite trigger signal and generating a new version of the first prompt word based on the rewrite trigger signal. The method of the present application can improve the efficiency of prompt word generation and the performance of the large language model.

The invention provides a method for jointly optimizing a color imaging system based on a metasurface by using a shunt mode and image reconstruction. The method comprises the following steps of: establishing a micro mathematical model of an image sensor based on a shunt; establishing a deep neural network capable of simultaneously processing a plurality of image processing tasks to realize image reconstruction; the image model based on the splitter is realized as a shunt network; and the parameters of the shunt network and the reconstruction network are jointly trained to realize automatic optimization of the shunt mode. According to the invention, automatic optimization of the shunt mode can be realized, and the overall efficiency and accuracy of image sensing and reconstruction are improved.

The present application provides a method, device, electronic device and storage medium for automatically adjusting network resource configuration based on a probabilistic grammar model, the method comprising: first, using network connection data to train a probabilistic grammar model corresponding to each service state, these models are constructed based on a probabilistic knowledge graph. Then, the working status data of the target node in the target time period is obtained, and these data are input into the probabilistic grammar model to obtain the first graph data. The target graph data with the highest similarity to the first graph data is selected from the pre-trained second graph data, and the working status is determined based on the target graph data. Finally, the node state of the target node is predicted based on the working status, and the target node is adjusted according to the prediction result. The present application can improve configuration efficiency.

The invention discloses a text processing method and device, a storage medium and electronic equipment. The text processing method comprises the steps of obtaining an original text input by a user; when it is determined that the original text meets a trigger condition for executing text processing, determining a to-be-processed text according to the original text, and determining a processing process for executing a processing operation on the to-be-processed text; executing the processing process to input the to-be-processed text into a preset text processing model; and processing the to-be-processed text through the text processing model. According to the scheme, the text content is automatically optimized in the process of inputting the text by the user, and the readability and the quality of the text are effectively improved.

The application belongs to the technical field of embroidery and discloses an AR-based embroidery method, system, AR glasses and storage medium, which comprises the following steps: inputting an embroidery sketch into a cycle generative adversarial network to obtain an embroidery pattern, wherein the cycle generative adversarial network is used for feature fusion of line structures of the embroidery sketch and embroidery patterns and generation of the embroidery pattern; embroidering according to the embroidery pattern by using AR glasses; in the embroidery process, in response to collected eye movement data and brain wave data, inputting the eye movement data and the brain wave data into an emotion model to obtain an emotion quantitative index, searching for the emotion quantitative index in a preset color-emotion association library to obtain a recommended color combination and displaying the recommended color combination. The application can realize convenient and efficient personalized embroidery work creation assistance.

The embodiment of the invention discloses a data optimization method and device, equipment, a storage medium and a product. The method comprises the steps that associated data and reference information of a to-be-optimized statement are obtained, the associated data comprise an analysis process of the to-be-optimized statement, the reference information comprises a rewriting example of a reference statement, supplemental content retrieval is conducted on the to-be-optimized statement, and supplemental content of the to-be-optimized statement is obtained; and rewriting the analysis process based on the to-be-optimized statement, the rewriting example of the reference statement and the supplementary content to obtain a rewritten analysis process, and generating an optimization result of the to-be-optimized statement through the rewritten analysis process. It can be seen that the analysis process of the to-be-optimized statement is rewritten by adding the rewriting example and the supplementary content of the reference statement, automatic optimization of the to-be-optimized statement can be achieved, and the optimization efficiency of the to-be-optimized statement is improved.

The application discloses a design, verification analysis and optimization method of a bracket-free invisible orthodontic appliance model, and belongs to the technical field of oral orthodontics. The design method of the application adopts a cooling method to simulate and adjust the thermal expansion deformation of a local appliance model, restricts the deformation direction and size, and realizes the design deformation of the appliance model. The analysis verification method of the application comprises iterative simulation of the osteogenesis and osteolysis biological effect under orthodontic treatment force based on computer three-dimensional finite element analysis, simulation of wearing of the appliance, reading of simulation results, and analysis and verification. The optimization method of the application is based on the simulation results. The application creatively adopts a "cooling method" with prestress loading to realize the model deformation simulation of the appliance, and can combine the finite element simulation of the periodontal membrane biological effect of the actual clinical treatment time. The application implements the step-by-step design, analysis verification and optimization of the invisible orthodontic appliance model according to the analysis verification results.

The invention relates to the technical field of three-dimensional geographic modeling, in particular to a land utilization planning auxiliary method and system based on a 3D terrain model, and the method comprises the following steps: obtaining point cloud and elevation information, converting the information into a terrain curved surface, discretizing the information into a voxel grid, analyzing a planning index, converting the planning index into a geometric constraint boundary, and traversing to construct a planning voxel field. Calculating earthwork digging and filling requirements and sunshine hours to generate an evaluation matrix, correcting the building voxel distribution and finely adjusting the terrain elevation. According to the method, terrain refined expression is realized by using an octree segmentation algorithm, indexes such as a plot ratio are converted into three-dimensional space geometric constraints, site suitability is evaluated through connectivity analysis, and building layout and earthwork balance are automatically optimized by adopting morphological operation, so that the problems of lack of space intuition and error accumulation in traditional two-dimensional planning are solved; the planning calculation precision and decision scientificity under the complex terrain are remarkably improved, and dynamic adjustment and visual expression of the scheme are achieved.

A spatially adaptive non-structured element resistivity tomography method and system, relating to the field of geological exploration technology. The method comprises: obtaining the resistivity and acquisition depth of multiple acquisition layers of complex structural geological conditions, generating first non-structured elements corresponding to each acquisition layer according to each resistivity and each acquisition depth; performing an inversion operation on each of the first non-structured elements to obtain the first resistivity of each of the first non-structured elements, and adjusting the first non-structured elements according to the first resistivity to obtain the second non-structured elements; performing an inversion operation on each of the second non-structured elements to obtain the second resistivity of each of the second non-structured elements; and so on, until the iteration is completed, and finally generating an apparent resistivity layer image of complex structural geological conditions. The effect of improving the accuracy of resistivity tomography is achieved.

The invention discloses a green pellet crack detection system and method based on an interpretable space-time atlas, and relates to the technical field of mineral product detection, and the system comprises a knowledge graph construction module which is used for constructing a knowledge graph of space-time perception green pellets and cracks thereof based on the image data of the preprocessed historical green pellets and cracks thereof; the green ball feature extraction module is used for obtaining a feature data set of screened green balls and cracks thereof by utilizing a knowledge graph of the green balls and the cracks thereof through space-time perception; the detection model construction module is used for training the green ball crack detection initial model to obtain a green ball crack detection model; and the green ball crack detection module is used for carrying out green ball crack detection on the collected real-time green balls and crack data thereof to obtain a green ball crack detection result. The method can solve the problems of low detection efficiency and insufficient reliability of current human subjective factors and a traditional detection method, and ensures the quality of green pellets and the stability of the production process.

The present invention discloses a high-speed train speed curve optimization method and system based on reinforcement learning, including S1: obtaining road data, train running time data, train speed, train acceleration data, and preprocessing them to obtain the preprocessed road data, train running time data, train speed, and train acceleration data; S2: defining the state space and action space of the train speed curve optimization network; S3: constructing the value function of the train speed curve optimization network, and updating the value function according to the iteration step; S4: setting the reward function of the train speed curve optimization network, the reward function includes the passenger comfort function, the total energy consumption function, and the punctuality function; S5: reinforcement learning training, updating the Q table and outputting the high-speed train speed curve. This solution solves the problem that the traditional train speed curve does not take passenger comfort into consideration.

The present invention discloses an unsupervised microseismic denoising method and system based on a multi-channel Kalman filter network, which belongs to the intersection of earth science technology and artificial intelligence, including: obtaining multi-channel microseismic data and preprocessing; constructing a multi-channel Kalman seismic denoising network based on a recurrent neural network; constructing a loss function, and unsupervised training the multi-channel Kalman seismic denoising network based on the preprocessed multi-channel microseismic data and the loss function; denoising the real-time microseismic data through the trained multi-channel Kalman seismic denoising network. The present invention establishes a multi-channel Kalman filter state prediction and update equation, designs an RNN operation operator, realizes automatic parameter optimization, and improves the accuracy and efficiency of denoising. The method makes full use of the correlation of microseismic data, improves the filtering effect, is suitable for time-varying signals, and provides support for subsequent data processing.

This application provides a model update method, apparatus, device, medium, and product. The method includes: acquiring target item features of a first item uploaded by a first user, the target item features including at least one of item usage status, item attribute parameters, and item identifier; performing a quality assessment on each target item feature using a quality assessment model to determine a first assessment result corresponding to each target item feature; acquiring target quality inspection information uploaded by a second user, the target quality inspection information including at least a second assessment result obtained by the second user on the quality assessment of the target item features in the second item; acquiring a first deviation between the first assessment result and the second assessment result, and updating the quality assessment model in reverse based on the first deviation. This method can improve the accuracy and timeliness of users determining the quality assessment results of target items online.

The present invention relates to the field of medical devices, and in particular to an intelligent control system for electric stimulation of nystagmus, comprising: a data acquisition unit configured to collect nystagmus data and related data; a data analysis unit configured to determine a first electric stimulation parameter according to the nystagmus data; a parameter adjustment unit configured to determine an influencing factor of the first electric stimulation parameter according to a preset electric stimulation parameter adjustment strategy and related data, and determine a second electric stimulation parameter according to the first electric stimulation parameter and the influencing factor. The electric stimulation parameters given by the system of the present invention combine the influence of nystagmus patterns and individual differences, and determining the second electric stimulation parameters according to the first electric stimulation parameters and the influencing factors can achieve a more accurate and effective electric stimulation effect for the target user.