180results about How to "Strong correlation" patented technology

The invention discloses a transmission network fault positioning method, system and device and a storage medium, and relates to the field of communication, and the method comprises the steps: firstly, collecting network data to construct a multi-granularity network topology model, then obtaining original alarm data, analyzing the topology association relationship between alarms, and obtaining a multi-granularity network topology model; and mining horizontal peer association and vertical upstream and downstream association among the alarm objects, and identifying multi-alarm data of each alarm object. Thirdly, constructing an associated alarm directed graph based on the multi-alarm data, and performing associated alarm compression on the associated alarm directed graph to obtain a compressed alarm set; and finally, constructing an alarm propagation graph based on the compressed alarm set and the multi-granularity network topology model, querying the alarm propagation graph through a reverse hierarchical sorting algorithm, and determining a root alarm. According to the method, through innovative methods such as topological modeling, association mining, graph compression and reverse sequencing, alarm data and a topological structure are closely combined, an alarm propagation rule is analyzed, and fault root causes are diagnosed.

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 belongs to the technical field of intelligent transportation, and discloses a driving state recognition method and system based on multi-modal fusion. According to the method, the wrist movement information of the driver is collected through the non-invasive physiological bracelet, the wrist movement information is combined with traditional physiological signals, driving performance data and the like, and the accuracy and robustness of driving state recognition are improved. The invention provides a feature dynamic weighting model, the model captures a long-term dependency relationship in a time sequence by using an LSTM module, and weights the features among different modes by using a cross attention mechanism module, so that the dynamic importance of each feature can be adjusted according to different driving states, and the accuracy of driving is improved. And deep interaction and feature enhancement of different modal data are realized by using a Transform module. According to the method, the XGBoost model is utilized, driving state classification is carried out based on the weighted multi-modal features, and different driving states can be accurately distinguished.

The invention relates to the technical field of side slope monitoring and early warning, in particular to a mine site side slope remote online real-time monitoring and early warning system which comprises a multi-source data monitoring unit, a data processing and analyzing unit, a side slope instability judging unit, an instability grading early warning unit and an early warning verification pushing unit. The method comprises the following steps: collecting monitoring data of a plurality of data sources to obtain time sequence monitoring data of each data source, carrying out noise reduction on the time sequence monitoring data, then decomposing the time sequence monitoring data, calculating a time-frequency feature vector through an energy density function, and optimizing the time-frequency feature vector through feature screening; calculating a slope stability evaluation index by optimizing the time-frequency feature vector, constructing an instability risk index to judge a slope instability risk level, constructing an early warning index, and combining the early warning index with the slope instability risk level to establish a graded early warning mechanism to obtain a slope instability early warning level; and verifying the slope instability early warning grade, and performing early warning according to the slope instability early warning grade passing the verification. According to the invention, the false alarm rate of slope monitoring and early warning can be reduced.

The invention provides a new energy transmission project input-output data mapping relation determination method, and belongs to the technical field of new energy transmission projects, and the method comprises the steps: carrying out the standardization processing of historical input data (construction, equipment and operation and maintenance) and output data (output electric energy and electric energy quality); then constructing an equipment parameter matrix, and obtaining an equipment feature vector through fast Fourier transform; a principal component analysis method is adopted to extract main characteristic components, and core equipment characteristic vectors after dimension reduction are constructed. Establishing an interaction influence matrix, determining a weight coefficient of each equipment parameter, constructing an equipment parameter comprehensive evaluation index based on the weight coefficient, and establishing an integrated mapping relation with input-output data; and finally, constructing a multi-input multi-output deep neural network, taking the core equipment feature vector as input, taking input-output data as output, and performing network training based on the integrated mapping relation to obtain a final mapping relation capable of fully considering the mutual relation between different data.

The invention discloses a query method and device based on a natural language and data knowledge and a storage medium, and the method comprises the steps: constructing a first database based on collected metadata and corresponding data consanguinity, and constructing a second database based on collected historical business data; determining a plurality of data consanguinity corresponding to the metadata in the first database, and determining a plurality of joint knowledge vectors based on the metadata and the corresponding data consanguinity; the first query vector is matched with the multiple joint knowledge vectors, a target joint knowledge vector corresponding to the first query vector is determined, the first query vector corresponds to a first query text of the terminal equipment, and the first query text is natural language text information; generating a first SQL language instruction based on the target joint knowledge vector and a pre-trained generative language model; and querying in a second database by utilizing the first SQL language instruction, and obtaining and returning corresponding first business data to the terminal equipment.

The invention belongs to the technical field of intelligent questioning and answering, and particularly discloses a business questioning and answering method and system based on a large language model and a scene label, corresponding knowledge base question sets are associated through business question expansion matching, and then answers are called from a knowledge base to determine corresponding data blocks and scene labels. Then, through scene label matching and scene consistency evaluation, a database with high scene consistency is selected to be combined with user questions to be input into a large language model to achieve intelligent question and answer. The business question and answer requirements of the user can be effectively met, and the user does not need to directly query professional knowledge; according to the method, questions proposed by users can be analyzed more comprehensively, questioning intentions and question scene information are fully covered, and question and answer feedback results are more targeted and have higher relevance with business questions of the users; and the question and answer result is simpler and more effective.

The invention discloses a voice-driven facial animation simulation method based on facial muscle linkage, and belongs to the field of artificial intelligence, and the method comprises the steps: S1, constructing a PPMF encoder; s2, constructing a decoder based on FDCP to decode FP features provided by the PPMF so as to obtain a facial animation; s3, training a 3D face animation framework DCPTalk driven by voice; s4, optimizing the model; and S5, quantitatively evaluating the model. According to the invention, a DCPTalk framework is provided, and the Mouth2Face is provided based on the linkage characteristic of the facial muscle group. Motion of the mouth has strong correlation with voice signals, and can be synthesized with sound track dynamics easily. In order to further enhance the details of the facial movement, the Refine Decoder is adopted to simulate the skin deformation of the surface layer to refine the facial animation. Inherent body characteristics and body characteristics related to facial muscle group movement are embedded into Mouth2Face, a personalized facial muscle control system is constructed, and meanwhile, an external driving signal is modulated by using a speaking style. Qualitative and quantitative experiments and user research show that the DCPTalk is superior to the most advanced method in the prior art.

The embodiment of the invention provides an insurance product recommendation method and device, electronic equipment and a storage medium, belongs to the technical field of artificial intelligence, and is suitable for the field of financial science and technology. The method comprises the following steps: acquiring original user behavior data, and performing activeness screening on the original user behavior data to obtain active user behavior data; obtaining target behavior data of the target object; performing user similarity calculation based on the active user behavior data and the target behavior data to obtain target user similarity data; performing user screening on the active user behavior data based on the target user similarity data to obtain target similar users; determining a target insurance product from the active user behavior data based on the target similar user; and performing product recommendation to the target object based on the target insurance product. According to the embodiment of the invention, recommendation errors caused by rare user behavior data can be effectively reduced, and the accuracy of insurance product recommendation is improved.

The invention relates to an interaction method and device, computer equipment and a storage medium. The method comprises the following steps: determining a target intention related to a user according to multi-modal information related to a target vehicle; and starting a dialogue service flow, the dialogue service flow comprising: sending an instruction corresponding to the target intention to an application to which the atomization service function for realizing the target intention belongs, so that the application calls the atomization service function for realizing the target intention according to the instruction.

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 relates to the field of sedimentary stratigraphy, in particular to a method for analyzing a deep-time El Nino-Southern oscillation cycle based on a sea-lake facies sediment annual stripe layer. The method comprises the following steps: sampling and grinding a sheet, carrying out high-definition scanning, estimating a deposition rate, carrying out element plane distribution, carrying out principal component analysis on the content of each element, carrying out laser in-situ carbon and oxygen isotope test and time domain spectrum analysis on an annual stripe layer one by one, and finally analyzing the climatic change of the interannual scale in the geological historical period. According to the method, element data, the thickness of the annual lamina and the carbon and oxygen isotope of the annual lamina are analyzed and explained in a combined mode, relevance exists among the indexes, a new method is provided for identification and analysis of the deep-time El Nino-Southern oscillation cycle, a time scale with the year as the unit is established on the basis of the marine facies or lacustrine facies annual lamina, and the identification accuracy of the deep-time El Nino-Southern oscillation cycle is improved. And the climate fluctuation is subjected to high-precision analysis under the scale, so that the related technical blank is filled.

The invention relates to a lithium battery health state assessment method, which comprises the following steps: S1, selecting a sample data set in a database, and extracting factor data of health factors from the sample data set; s2, calculating a correlation coefficient between the factor data of the health factor and the health state; s3, comparing the correlation coefficient with a preset coefficient threshold value, and judging whether the health factor is a target health factor or not; s4, constructing a linear regression model taking the target health factor as input and the health state as output; and S5, extracting factor data of each target health factor of the lithium battery to be evaluated, inputting the factor data of each target health factor into the linear regression model, and outputting the health state of the lithium battery. The invention also relates to a device and a medium. The target health factors have strong correlation with the SOH, and the accuracy is evaluated by a linear regression model taking the target health factors as input and the health state as output.

The invention provides a high-precision building construction carbon emission real-time metering method and system, and belongs to the field of carbon emission calculation. The invention aims to solve the problem that during carbon emission calculation in building construction, a calculation boundary needs to be determined, repeated calculation or missing lacks are easy to occur, and large access exists between a metering result and actual carbon emission. The construction activity consumption data is collected through an automatic data collection means and uploaded to the system platform through the Internet of Things, and finally the carbon emission data of each subentry construction activity is calculated in real time and analyzed and displayed. According to the method, carbon emission calculation is considered as a whole, the carbon emission of the whole project is explored from overall resource input and output, detailed analysis of internal process links is avoided, the problem of determining the internal boundary of the project for multiple times and the influence of construction uncertainty are avoided, and the construction efficiency is improved. And moreover, the calculation boundary is clear, the calculation process is simple, the calculation result precision is very high, and the method has relatively high operability and popularization value.

The invention relates to a lithium ion battery monomer / module capacity estimation method based on a deep migration model, which comprises the following steps: collecting charge-discharge cycle data in a lithium ion battery aging test process, and taking the maximum capacity in the whole discharge process in the lithium ion battery cycle process as a capacity label; according to the relation between the battery polarization reaction intensity and the battery capacity, by means of depolarization characteristic data of constant-voltage charging current and relaxation voltage, multi-dimensional practical characteristics which are not affected by a charging starting point are extracted, the Spearman correlation coefficient between the selected multi-dimensional practical characteristics and the capacity is calculated, the characteristics with the correlation coefficient larger than 0.8 are reserved, and the battery capacity is calculated. Carrying out fusion processing on the features by using PCA, and taking the previous four principal components as dimension reduction features; and constructing a hybrid neural network DBN-SE-LSTM, and carrying out transfer learning by taking the dimension reduction features as input for online estimation of the capacity. The method can indirectly evaluate the capacity of the lithium ion battery monomer or module according to part of battery data, and has high estimation precision and good applicability.

The invention relates to an interaction method and device, computer equipment and a storage medium. The method comprises the following steps: determining a target intention related to a user according to multi-modal information related to a target vehicle; and starting a dialogue service flow, wherein the dialogue service flow comprises calling an atomization service function for realizing the target intention.

The invention provides an interactive guest playing interaction method based on intelligent glasses, and belongs to the technical field of interaction between intelligent equipment and guest playing, and the method comprises the steps: responding to the received first interaction information, generating first text information based on the first interaction information, and sending the first text information to second equipment, the first text information is used for indicating the second equipment to generate first broadcast text information matched with the first text information based on the target AI large model; and in response to the received first broadcaster text information sent by the second equipment, converting the first broadcaster text information into first broadcaster voice information, and playing the first broadcaster voice information. According to the interactive guest broadcasting interaction method based on the intelligent glasses, the interactivity and individuation can be improved, and the requirement of a user for obtaining specific information in real time is met.

The invention discloses an intelligent reply method and device fusing a large language model and a knowledge graph. The method comprises the following steps: judging whether a target user has a second question or not; if yes, judging whether a time interval between the first question and the second question is within a first preset duration or not; if yes, extracting a first keyword set of the first question and a second keyword set of the second question; whether the keyword repetition rate between the first keyword set and the second keyword set is larger than a repetition rate threshold value or not is judged; if yes, screening out a target knowledge graph from a preset knowledge graph database; filling the preset cue word structure framework with the plurality of first retrieval results to obtain a first text; obtaining a second text; and inputting the second text into a preset large language model to obtain a target reply corresponding to the first question of the target user. The invention belongs to the field of intelligent customer service. According to the invention, the hit rate of large language model answering can be improved.

The invention discloses an ultrasonic full-focusing full-matrix data compression method which is based on the following steps: in a full-matrix acquisition process, because the distance between two adjacent array elements is very close, after the two adjacent array elements are excited, echo data sampled by all the array elements have extremely strong correlation; therefore, by calculating the residual error of the echo data of the two adjacent continuous array elements, as the correlation of the echo data acquired when the two adjacent continuous array elements are excited is very strong, a lot of small data exist in the difference value, and the FMC data can be greatly compressed in a lossless manner by adopting exponential Golomb coding on the difference value; therefore, the requirement of data transmission on bandwidth can be reduced, so that the real-time requirement of detection is met; and under the same data bandwidth, more full-matrix acquisition data can be transmitted, so that the frame rate of FMC full-focus imaging is improved.

The invention provides a code generation method and device based on a program call graph, electronic equipment and a storage medium, and relates to the technical field of intelligent software development. The method comprises the steps that a received input current code is responded, a code file related to the current code is selected and called from a plurality of code files by calling a relation graph, and the calling relation graph exists in a computer program; and generating subsequent code contents of the current code according to at least one code file in the code files and the current code file of the current code. By calling the relation graph, it can be ensured that the called code file has high correlation with the current code, and the calling accuracy is improved.

The embodiment of the invention discloses a lithium battery capacity prediction method based on short-term charging data and an enhanced whale optimization algorithm, which can accurately predict the battery capacities of different types of batteries, improve the capacity prediction accuracy and reduce the training data volume at the same time. The method comprises the steps that in the cyclic charging and discharging test process of different types of batteries, charging and discharging data of the batteries are collected and recorded in real time, short-term charging data are extracted, and the charging and discharging data comprise charging time, voltage, current and capacity data; the short-term charging data is obtained by performing spline interpolation processing on the charging voltage and current data within a preset short-term charging duration; based on an enhanced whale optimization algorithm, battery aging characteristics are extracted from short-term charging data through iterative optimization, and the enhanced whale optimization algorithm is obtained by introducing a pooling mechanism, a migration search strategy, a preferential selection search strategy and a surrounding prey search strategy into a classic whale optimization algorithm. The battery aging characteristic is the voltage increment of the voltage interval corresponding to the charging time interval with the strongest correlation with the battery capacity; and for different types of batteries or the same type of batteries, charging and discharging data and aging characteristics are input into a Gaussian process regression model to predict a battery capacity prediction value, and the Gaussian process regression model is constructed based on a Gaussian process regression algorithm.

The invention provides a stacking model-based obesity genetic risk model construction method, electronic equipment and a storage medium, the method comprises the following steps: obtaining a data set, the data set comprising genetic feature data, and the genetic feature data comprising single nucleotide polymorphism; screening the single nucleotide polymorphisms by using a random forest to obtain a first genetic feature set related to obesity genetic risks; screening the single nucleotide polymorphism by using a distributed gradient enhanced library model to obtain a second genetic feature set related to the obesity genetic risk; obtaining a significant genetic feature set according to the first genetic feature set and the second genetic feature set; constructing an obesity genetic risk model; according to the method, the model evaluation result of the obesity genetic risk model is obtained, the parameters of the obesity genetic risk model are optimized according to the model evaluation result, stacking training is carried out by integrating multiple basic learners and combining meta-classifier logistic regression, the advantages of different algorithms are effectively fused, and the method is suitable for large-scale popularization and application. And the accuracy and the stability of obesity genetic risk prediction are improved.

The invention discloses a knowledge retrieval method and device applied to large-model knowledge questions and answers. The method comprises the following steps: sorting and cleaning knowledge data and converting the knowledge data into knowledge texts; the knowledge text is segmented into independent knowledge points, corresponding titles, abstracts, keywords and the like are marked, and the relation between the knowledge points is determined; constructing a knowledge full-text retrieval library and a knowledge graph; identifying knowledge points in the retrieval problem, retrieving corresponding candidate knowledge points, calculating the similarity between the candidate knowledge points and the knowledge points in the retrieval problem, and screening based on the similarity to obtain a knowledge point set; the knowledge points with correlation are grouped according to the minimum adjacency graph, the knowledge points without correlation are grouped independently, and the grouped knowledge points and the independent knowledge points are sorted and combined according to the maximum spanning tree mode; preparing a proper prompt template for the question big model, nesting the sorted and combined knowledge points into the template, and generating a prompt context; and inputting the prompt context into the questioning big model, and sorting out a final retrieval result by utilizing the reasoning, induction and summarization capabilities of the questioning big model.

The invention discloses a depression detection method based on image-text social media analysis, relates to the technical field of automatic detection of depression, and solves the technical problem in the prior art that whether a user is a depression patient or not is detected by analyzing character and picture data published by the user on a social media platform. Specifically, the method comprises four steps of data capture, post screening, image-text representation and fusion classification. Generally speaking, the method comprises the following steps: firstly, capturing a posted document or posted post of a user in a certain time period from a social media platform, including characters and picture data in the posted document; then, performing depression related symptom prediction on the posted document, and screening out the most related K posts; then, respectively carrying out text and picture representation on the screened posts; and finally, fusing the characterization vectors of the two modes for depression detection.

The embodiment of the invention provides a text generation method and device based on a large language model, and relates to the technical field of artificial intelligence, the method comprises the steps that a target abstract is determined according to current interaction content between target objects, and the target abstract represents semantics of the current interaction content; extracting target memory information which corresponds to the target object and is related to the target abstract from stored historical memory information, wherein the historical memory information comprises a first result obtained by summarizing and / or expanding historical interaction content by using a large language model; the target abstract and the target memory information serve as first cue words to be input into a large language model, a target text for the current interaction content is generated, and the target text of the current interaction content comprises a second result obtained by summarizing and / or expanding the current interaction content through the large language model. By applying the scheme provided by the embodiment of the invention, the quality of thoughts generated by the large language model can be improved.

The invention discloses an infrared image column stripe heterogeneity correction method based on an improved median histogram, and belongs to the field of infrared image heterogeneity correction. The method comprises the following steps: calculating a column histogram of a single-frame infrared image and a column mean value curve subjected to wavelet denoising according to an input infrared image; performing inversion operation according to the obtained column histogram to obtain a column inverse histogram; generating an adaptive window of the bilateral filter according to the column mean value curve; generating a bilateral filter according to the spatial domain distance of the column and the gray domain difference of the column mean value curve; filtering the column inverse histogram according to the obtained bilateral filter to obtain a median histogram; and an input infrared image is mapped according to the median histogram to obtain an infrared image after stripe removal. According to the method, a better fringe removal effect is achieved, blind pixel points are avoided, and the step of repeatedly calculating a comparison result to solve an optimal parameter value in an original algorithm is omitted.

The invention discloses a power transformer turn-to-turn short circuit fault positioning method and system based on magnetic flux leakage distribution. The method comprises the following steps: acquiring the radial magnetic field intensity inside a power transformer; based on the obtained radial magnetic field intensity, the short circuit fault occurrence condition is judged by adopting a difference value method; when a short-circuit fault occurs, the position of the short-circuit fault point is positioned by using the position of the zero crossing point of the radial magnetic field intensity. According to the invention, the improved optical fiber sensor is used for collecting the magnetic flux leakage distribution in the power transformer, and then the amplitude and phase characteristics of the magnetic flux leakage are obtained through Fourier transform and an interpolation method. And rapid monitoring and interval positioning of the turn-to-turn short circuit fault of the power transformer are realized by monitoring the internal magnetic leakage amplitude and phase characteristics of the power transformer.

The invention relates to the technical field of oil field oil production engineering, in particular to an underground data acquisition unit with flow, pressure and temperature monitoring functions and a method. The method is suitable for underground working condition monitoring of submersible motor type lifting equipment such as submersible screw pumps, submersible plunger pumps and submersible centrifugal pumps. The unit comprises a pressure-resistant shell, wherein a central pipe, an electromagnetic flowmeter, two pressure and temperature sensors and an underground parameter collector are integrated in the pressure-resistant shell; the two sensors are used for measuring the temperature and pressure of liquid flow of the central pipe and the temperature and pressure of an oil casing annulus respectively; the electromagnetic flowmeter measures the flow, and real-time temperature and pressure data are used for compensation to improve the precision. The unit is further provided with a collecting pipe, an upper end, a lower end, a connecting sliding ring, an annulus / oil pipe inner testing channel, a sealing rubber ring and the like. Through the integrated design, synchronous high-precision in-situ monitoring of underground key parameters is achieved, the problem that underground data of multiple instruments are not synchronous is solved, and a reliable data terminal is provided for an intelligent oil field.

The invention belongs to the technical field of waste heat recovery control, and provides a natural gas cold and heat cogeneration control method, which comprises the following steps: setting a monitoring period, monitoring two ends of each natural gas conveying pipeline to obtain natural gas temperature data, analyzing and processing the natural gas temperature data to obtain an ideal heat recovery value, and controlling the heat recovery value. Analyzing and processing the natural gas temperature data again to obtain an actual heat recovery value, acquiring a gas flow in each natural gas conveying pipeline in a monitoring time period, obtaining a gas flow mean value, processing the actual heat recovery value and a theoretical heat recovery value, outputting to obtain a heat recovery efficiency value, and comparing the heat recovery efficiency value with a threshold value; if the heat recovery efficiency value is smaller than the heat recovery efficiency threshold value, it is indicated that the heat recovery efficiency of the heat recovery system in actual operation is low, and a low recovery efficiency signal is generated, so that comprehensive evaluation on the heat recovery efficiency of the heat recovery system is completed by setting a monitoring period.

The invention provides a liquid drop surface action testing method based on an acoustic suspension technology and application, the liquid drop surface action testing method adopts the acoustic suspension technology to realize high-frequency deformation of liquid drops without external force interference, and the high-frequency surface expansion modulus of a surfactant is measured and calculated; according to the method, the liquid drop deformation hindering error caused by needle contact in a traditional oscillation dropping method is avoided, the defects that the high-surfactant concentration low-frequency expansion modulus value is small, and the foam stability correlation is poor are overcome, the foam performance of the chemical oil displacement formula can be rapidly and accurately evaluated, and research and development of the chemical oil displacement formula are guided.