917results about How to "Accurate identification" patented technology

The invention discloses a multi-user sharing-oriented multimedia network video recommendation method, which comprises the following steps: firstly, constructing multi-user characteristics by utilizing collected program information in a multi-user sharing environment, and constructing a leading user label according to the similarity of the program characteristics and the continuity of user watching behaviors, so that separation of multi-user mixed logs is realized; performing periodic multi-user identification prediction of future sessions; secondly, building a user interest mining model based on a time-varying LinUCB algorithm to learn interest changes of a user for each program theme, and enhancing the personalized ability and efficiency of a recommendation system from three aspects of parallel calculation, adaptive control of an exploration coefficient and incremental updating based on LSTM; and finally, establishing an article quality model based on a non-time-varying LinUCB algorithm to further ensure the program quality, and integrating the two algorithms into a final recommendation system model by adopting a cross weighting strategy to form a final program recommendation list. The novelty and accuracy of the recommendation result are ensured.

The invention discloses an automatic auditing system and method for road engineering completion settlement. The system comprises a data acquisition and integration module; a data preprocessing module; a settlement index construction module; an intelligent auditing engine; an abnormity early warning and tracing module; a settlement result management module; and a data analysis and optimization module. The auditing method comprises the following steps: S1, collecting and integrating multi-source settlement data; s2, big data preprocessing; s3, constructing a settlement auditing index system; s4, performing intelligent automatic auditing; s5, performing abnormal early warning and tracing; s6, managing settlement results; and S7, performing big data driving optimization. According to the invention, multi-source data intelligent integration, full-process automatic auditing, abnormal accurate tracing and dynamically optimized big data driven settlement auditing can be realized; the method is applied to whole-process management of road engineering completion settlement, and data-driven intelligent auditing and continuous optimization control are realized in the process.

This invention discloses a method for indicating the degree of pyrite development in carbonate reservoirs, belonging to the field of geological exploration technology. Its key features include the following steps: a) selecting sensitive parameters to distinguish pyrite development based on the well logging response characteristics of pyrite-developed strata; b) establishing trend lines for the variation of sensitive parameters in formations with no or low pyrite content using these sensitive parameters; c) constructing trend lines for the variation of sensitive parameters with pyrite content based on strata data from key wells with different pyrite contents; d) identifying pyrite-enriched strata in the wells to be identified based on the intersection indications of the sensitive parameters; e) evaluating the degree of pyrite development in the pyrite-enriched strata by assessing the degree of difference in the intersection of sensitive parameters. This invention achieves the identification of pyrite-enriched strata through resistivity and sonic transit time intersection, enabling rapid and accurate identification of the degree of pyrite development in carbonate reservoirs.

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

The invention discloses a multivariate time sequence prediction method based on dynamic complex spectrum filtering, which comprises the following steps: acquiring multivariate time sequence data of a target scene, determining a predicted target variable and a window length, and constructing a training set, a verification set and a test set based on the data; constructing a DCSFNet prediction model, wherein the model sequentially comprises a reversible instance normalization module, a frequency domain conversion module, a dynamic complex spectrum filtering module, a time domain projection module and an inverse reversible instance normalization module; and realizing multivariate time sequence prediction by using the trained DCSFNet model. According to the method, the frequency domain features are adaptively processed through the dynamic complex spectrum filtering module, and reversible normalization and time domain mapping are combined, so that the problems that an existing method is poor in adaptability to diversified time sequence data, frequency domain information is insufficient in utilization, and prediction precision and calculation efficiency are difficult to consider at the same time are effectively solved; and multivariate time sequence prediction with high precision, high efficiency and cross-scene adaptability is realized.

The invention relates to the technical field of vehicle suspension system control, and discloses a multi-source data fused shock absorber self-adaptive regulation and control method and system.The method comprises the steps that multi-source signal data are synchronously collected through a sensor, and a preliminary signal set is generated in combination with environment change data; analyzing vibration signal peak amplitude and displacement data integral variation trend, distinguishing high-frequency small-acceleration and low-frequency large-amplitude pavement excitation characteristics, and determining signal contribution degree distribution; dynamically adjusting the signal weight and introducing temperature compensation to correct stiffness drift to generate a weighted vector; carrying out weighting processing, smoothing, noise suppression and nonlinear amplitude estimation to generate fusion data, and optimizing the weight to obtain a refined weighted vector; after secondary fusion, a cross-frequency-band transition smoothing technology and a least square method are adopted to determine damping characteristic parameters; according to the vehicle suspension system self-adaptive regulation and control method and system, the self-adaptive regulation and control accuracy and the working condition adaptability of the vehicle suspension system are improved.

The invention discloses a robust federated learning method for processing heterogeneous noise and non-independent identically distributed data, and belongs to the technical field of federated learning. The method provides a robust learning framework of two-stage client quality perception. The method comprises the following steps of: 1, constructing a category-level loss vector and clustering by using a Gaussian mixture model, and accurately dividing a clean and noise client set; stage 2, performing differential training: performing standard training on the clean client; dual-network cooperative training, dynamic sample screening and exchange, and a self-distillation and entropy regularization mechanism are introduced to a noise client, so that robust learning is realized; in the global aggregation stage, a distance sensing weighting strategy is further adopted to dynamically suppress the influence of a noise client; according to the method, original data does not need to be shared, the robustness and generalization performance of the federated learning model in the coexistence environment of heterogeneous noise and non-independent identically distributed data can be effectively improved, and the method has wide application value in the fields of medical images, financial risk control and the like.

The invention relates to a network prediction model for identifying a new cancer gene, a model construction method and application, and belongs to the field of biostatistics. According to the method, genomics, transcriptomics and proteomics data are integrated, a dynamic tensor twinning graph neural network is constructed, a hybrid multilayer random block model is utilized to perform tensor decomposition to extract global and local community features, causal and non-causal information is separated, causal feature mutual information is maximized, and non-causal interference is inhibited, so that the dynamic tensor twinning graph neural network is obtained. The model interpretation force is enhanced; through a dynamic community perception algorithm and a twin graph neural network, inter-layer graph dissimilarity is learned by using a graph similarity function, accurate detection of network structure mutation nodes in a high-frequency dynamic scene is realized, and an independent state transition and co-evolution mode is synchronously positioned. And the biological functions of the candidate genes are verified by combining KEGG pathway enrichment analysis and an independent database. Compared with an existing optimal algorithm, the mutation detection accuracy and the calculation efficiency are remarkably improved, and the AUROC and the AUPRC are both higher than those of an existing advanced recognition method.

The invention provides a rainfall monitoring method and system based on unmanned aerial vehicle multi-mode noise reduction in the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: S1, collecting a visible light image and a long-wave infrared image through a multispectral vision module after an unmanned aerial vehicle takes off, and collecting a rainfall audio from an acoustic cabin through a microphone; s2, carrying out noise reduction processing on the visible light image, the long-wave infrared image and the rainfall audio; s3, extracting a raindrop size distribution histogram and a spatial density thermodynamic diagram based on the visible light image and the long-wave infrared image, and extracting acoustic MFCC features based on rainfall audio; s4, performing feature fusion operation based on the raindrop size distribution histogram, the visual density thermodynamic diagram and the acoustic MFCC features to obtain rainfall intensity; and S5, carrying out cumulant dynamic compensation based on the rainfall intensity to obtain the cumulative rainfall. The rainfall monitoring method has the advantages that the precision, robustness and practical level of rainfall monitoring are greatly improved.

The embodiment of the invention discloses a surface temperature inversion method and device based on remote sensing satellite data, and a medium, and relates to the technical field of remote sensing, and the method comprises the steps: receiving a heat island effect analysis demand triggered by a user, obtaining satellite remote sensing data of a target region and a corresponding metadata file based on the heat island effect analysis demand, and storing the satellite remote sensing data in the target region; processing the satellite remote sensing data to determine earth surface radiation parameters; the atmospheric features and the earth surface features of the target area are evaluated through the metadata file and a pre-obtained normalized vegetation index, a dynamic adjustment factor corresponding to the target area is determined, a preset single-window algorithm model is optimized through the dynamic adjustment factor, and an optimized single-window algorithm model is determined; and inputting the surface emissivity, the atmospheric transmittance, the brightness temperature value and a pre-acquired atmospheric average action temperature into an optimized single window algorithm model for calculation, and determining a surface temperature distribution diagram of the target area for heat island effect analysis.

The invention relates to the field of genes, in particular to molecular markers for judging flowering time of brassica napus L. The molecular markers are Indel-282, Indel-326 and Indel-353 respectively and are located on an A02 chromosome of the brassica napus L.. The molecular marker has high specificity and stability, and is significantly related to the flowering time. The markers show high specificity and stability in brassica napus plants under different genetic backgrounds. By detecting the molecular markers through PCR amplification and electrophoresis, the flowering time of different plants can be quickly and accurately identified in the early stage of breeding. Therefore, the flowering time of the plant can be determined in the early stage, so that the breeding efficiency is improved. The molecular markers can be used for molecular marker-assisted selective breeding, and plants with early flowering or late flowering characteristics can be quickly screened by detecting the markers, so that the breeding process of excellent varieties is accelerated, and the functions and regulation mechanisms of related genes are further disclosed.

The invention belongs to the technical field of oil and gas pipeline safety monitoring and unmanned aerial vehicle image analysis, and discloses a two-stage oil and gas pipeline occupation pressure monitoring method and system based on unmanned aerial vehicle space perception and deep learning fusion, and the method comprises the steps: obtaining image data and space data of a to-be-monitored region, combining the image data and the space data with a DEM, and obtaining a DEM image; realizing projection of geographic coordinates of the pipeline to image pixel coordinates to obtain a plane image of the pipeline; constructing and deploying a lightweight target detection network DRF-YOLOv8n, performing reasoning on a plane image of the pipeline based on the lightweight target detection network DRF-YOLOv8n, outputting a candidate bounding box, and performing screening and key frame judgment; a Promp-guide SAM pixel level segmentation module is used to realize pixel level segmentation of the key frame, and a candidate pixel mask is generated; and judging whether occupation occurs or not through the intersection of the pipeline projection line and the candidate pixel mask, and outputting alarm information with geographic coordinates.

The application provides a multi-agent cooperative task state management and abnormal backtracking method and system, relates to the technical field of artificial intelligence, and comprises the following steps: obtaining interaction data, state data and execution logs, constructing an initial task knowledge graph based on an ontology model, establishing a bidirectional causal chain by using a Bayesian network to form a dynamic knowledge graph, analyzing state migration rules when an abnormality is detected, identifying a propagation node, constructing an abnormal propagation subgraph, generating an influence chain, determining a recovery priority and triggering state rollback. The method can quickly trace the abnormal source, effectively control abnormal diffusion, and improve system robustness and task recovery efficiency.

The invention discloses a low-speed new energy vehicle driving assistance system based on vision and radar fusion, and belongs to the technical field of automotive electronics, which can adapt to price sensitive attributes, reduce the popularization threshold, adopt hardware combination of a low-cost monocular camera and an angle radar, and are matched with an integrated ADAS domain controller to replace a plurality of independent ECUs (electronic control units). The method does not need to depend on expensive devices such as a high-end millimeter wave radar and a laser radar, hardware and system cost is greatly reduced while multifunctional assistance is achieved, market positioning of low-speed new energy vehicles is perfectly matched, and through the fusion design of vision and a radar sensor and the combination of a front fusion and rear fusion cooperative verification algorithm, the positioning precision of the low-speed new energy vehicles is greatly improved. Visual data accurately identifies visual information such as lane lines, vehicles and pedestrians, radar data accurately detects parameters such as distance and speed and adapts to severe environments such as rain, fog and dark light, the advantages of the visual data and the radar data complement each other, the functional defects of a single sensor scheme are overcome, and the accuracy and stability of environment perception in different scenes are remarkably improved.

The invention relates to the field of equipment diagnosis, and discloses a submersible fault time sequence causal chain diagnosis method and system, and the method comprises the steps: obtaining multi-dimensional operation monitoring data in the operation process of a submersible; performing exception association analysis on the multi-dimensional operation monitoring data to obtain a cross-dimensional collaborative exception coefficient of an association unit group under each monitoring dimension; performing threshold comparison on all the cross-dimension collaborative anomaly coefficients, and determining each anomaly association unit group; and determining a diagnosis dimension trigger signal of the submersible according to the number of the abnormal association unit groups and the evolution characteristics of the cross-dimension collaborative abnormal coefficients of all the abnormal association unit groups. And a diagnosis dimension trigger signal is dynamically generated through the change and evolution characteristics of the cross-dimension collaborative abnormal coefficient, so that fault diagnosis scheduling of the submersible is triggered, and the response mechanism ensures the flexibility and adaptability in the diagnosis process.

The invention provides a radioactive substance transportation monitoring system, and relates to the technical field of radioactive substance transportation safety monitoring. The radioactive substance transportation monitoring system comprises a transfer protection box and a monitoring unit integrated on the transfer protection box, and the monitoring unit is composed of a double-detector fusion module, a three-dimensional data linkage analysis module and an active emergency disposal module which are sequentially connected through a circuit. The double-detector fusion module comprises a GM counting tube and a scintillator detector which are arranged inside and outside the box so as to realize alpha, beta, gamma and neutron multi-nuclide coverage; the three-dimensional data linkage analysis module performs comprehensive judgment based on internal and external doses of the box, temperature and humidity and vibration data, and accurately identifies protection failure or article leakage; and the active emergency disposal module automatically starts gate closing and adsorption measures in an emergency. According to the system, the problems of monitoring blind areas, reduced protection performance, frequent false alarm, emergency passivity and the like are solved, and the transportation safety and the intelligent level are improved.

The invention belongs to the technical field of biology, and relates to a pathogen detection technology based on a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) system, in particular to a tubercle bacillus detection and activity determination method based on fluorescent T7-CRISPR. The first purpose is to provide a reagent combination for detecting viable tubercle bacillus, and the reagent combination comprises a) a T7 reverse transcription reaction reagent and b) a CRISPR-Cas13a reaction system; a) the T7 reverse transcription reaction reagent comprises a T7 reverse transcription primer and a T7 reverse transcriptase; and b) the CRISPR-Cas13a reaction system comprises specific crRNA, a fluorescence labeled ssRNA reporter, a reaction buffer solution, a Cas13a protein, RNase-Free H2O and a T7 reverse transcriptase. The invention also provides application of the reagent combination in detection of viable tubercle bacillus. The primer combination disclosed by the invention has the advantages of strong specificity and high sensitivity in detection application, can realize rapid detection and visual detection, and is low in detection cost, simple and convenient to operate and suitable for large-scale clinical application.

The application discloses a kind of asset value dynamic evaluation methods for model full life cycle, comprising the following steps: S1, acquisition life cycle process data, generate model asset identification result;S2, construct life cycle state chain;S3, generate stage asset feature set;S4, generate model asset life cycle value function diagram, and according to model asset life cycle value function diagram generation life cycle value mark sequence;S5, life cycle value mark sequence is input asset value evolution diagram Mamba network, and model asset value evolution characterization is obtained;S6, according to model asset value evolution characterization generation dynamic asset value evaluation result, and update model asset archives and asset sorting result.The application improves the continuity, traceability and accuracy of dynamic asset value evaluation by asset value evolution diagram Mamba network.

The invention provides an automatic opening and closing drawer structure which comprises a cabinet body, an inner cavity is formed in the cabinet body, a drawer is arranged in the inner cavity in a drawable mode, a rack is arranged on one side of the drawer in the drawer drawing direction, a driving unit meshed with the rack to drive the drawer to be opened or closed is arranged in the drawer, and an electromagnetic lock is arranged on the rear side of the inner cavity. The front end of the rack is hinged to the interior of the inner cavity, a back plate of the cabinet body is provided with a sliding groove, the rear end of the rack is provided with an operation arm which penetrates through the sliding groove and extends to the outer side of the cabinet body, and the locking mechanism is connected with the operation arm. The operating arm is detachably installed on the back plate of the cabinet body and at the rear end of the fixed rack, a pressing rod is arranged in the operating arm and used for pressing a shifting rod of the electromagnetic lock to release the locking state of the electromagnetic lock, and when the drawer needs to be opened emergently, the locking mechanism is detached, the operating arm is pressed, the gear is disengaged from the rack, and the locking state of the electromagnetic lock is released.

The application discloses a water network state, event, constraint integrated modeling and runtime updating method. The method accesses the runtime multi-source data on the pre-constructed object semantic base, and performs semantic binding based on the object identification and the state template to generate a runtime state instance set; a runtime event instance set is generated based on threshold rules, time sequence mode rules, fault identification rules, scheduling instruction rules and / or external business trigger rules, and a runtime event causal chain is established when a trigger dependency exists; a runtime constraint instance set is generated by fusing rule templates, the current runtime state instance set, the runtime event instance set and the scope context, and a runtime active constraint set is obtained by evaluating the activation conditions; the runtime consistency check, constraint conflict processing and propagation analysis are performed on the runtime state instance set, the runtime event instance set and the runtime active constraint set to determine the affected runtime subgraph; local incremental updating or regional recalculation is performed according to the subgraph to generate a runtime model version snapshot, a difference snapshot, a version log, a runtime context and a calling interface; and the application can improve the unified expression, event traceability, constraint decision and high-frequency updating and maintenance capabilities of the water network operation state.

This application relates to a method for identifying hourly-scale extreme high temperatures using temperature series data. The method includes: preprocessing hourly temperature data to obtain an hourly-scale temperature series, and calculating a daily-scale average temperature series based on a meteorological daily window. Based on the hourly-scale temperature series and historical reference periods, determining hourly-scale extreme high temperature thresholds for different hours and identifying hourly-scale extreme high temperatures. Based on the daily-scale average temperature series and historical reference periods, determining daily-scale extreme high temperature thresholds for different dates and identifying daily-scale extreme high temperature days and non-daily-scale extreme high temperature days. Extracting the corresponding times that meet the hourly-scale extreme high temperature criteria from both daily-scale and non-daily-scale extreme high temperature days to obtain the corresponding hourly high temperature identifier sequences. This method enables accurate identification of the specific time periods and distribution characteristics of extreme high temperature events, avoiding the omission of high-temperature events.

This invention discloses a quality control method for an anti-snoring oral liquid and its application, belonging to the field of traditional Chinese medicine quality testing technology. The method includes thin-layer chromatography identification of Gleditsia sinensis thorns, Scrophularia ningpoensis, and Glycyrrhiza uralensis, as well as high-performance liquid chromatography (HPLC) determination of hesperidin content. By optimizing pretreatment, developing solvent, and colorimetric conditions, this invention achieves efficient and specific identification and quantification of multiple components in the compound preparation. The method is highly specific, produces clear spots, has good separation, and is tolerant to a wide temperature and humidity range (2-40℃, 20%-75%RH), requiring no strict environmental control, significantly improving detection efficiency and providing a reliable basis for establishing quality standards for this preparation.

This invention relates to the field of image processing technology, specifically providing a method, apparatus, and medium for fruit and vegetable identification based on neural networks and feature matching, aiming to solve the problem of difficulty in identifying fruit and vegetable categories during automatic settlement. To this end, the fruit and vegetable identification method based on neural networks and feature matching of this invention includes: acquiring fruit and vegetable images of the category to be identified; performing image preprocessing on the fruit and vegetable images; performing image segmentation on the preprocessed fruit and vegetable images of the category to be identified; inputting the segmented fruit and vegetable images of the category to be identified into a trained first feature extraction model and a second feature extraction model respectively to obtain features of the fruit and vegetable images of the category to be identified; acquiring the features of fruit and vegetable categories stored in a feature database; and calculating the cosine similarity between the features of the fruit and vegetable images of the category to be identified and the features in the feature database to obtain the category of the fruit and vegetable images of the category to be identified.

The present application relates to the technical field of intelligent identification system of crop pests, in particular to a plant field pest fine-grained identification method, system, device and storage medium based on deep learning. The identification method provided by the present application is specialized in high-precision identification of real field scenes, and can provide technical support for important work such as future development of field inspection robot, automatic identification and monitoring system of field pests and the like. In addition to pest monitoring, the field biological safety test of genetically modified plants is gradually carried out at present, and by using the identification method provided by the present application, the dynamic change of farmland insect community can be quickly and accurately identified and predicted, so that the efficiency and accuracy of ecological investigation are greatly improved.

The invention belongs to the field of wafer processing, and discloses a wafer grinding method based on an acoustic emission signal, which can avoid the quality defect of a ground wafer, and comprises the following steps: propping a predetermined wafer against a basic grinding surface, and grinding the predetermined wafer through the relative rotation of the basic grinding surface and the predetermined wafer. An acoustic emission signal of a predetermined wafer is obtained in real time, the abutting force on the predetermined wafer is adjusted based on the acoustic emission signal, the predetermined wafer is abutted through a grinding entity, an acoustic emission sensor is embedded in the grinding entity, and the grinding entity is composed of a plurality of grinding sub-entities corresponding to different radial directions of the predetermined wafer in a matched mode. The adjacent grinding sporocarp bodies can relatively move in the vertical direction, and therefore the multiple grinding sporocarp bodies correspondingly apply different abutting force in the radial direction of the preset wafer. The invention further discloses a wafer grinding device which is used for grinding the wafer grinding method in real time.

The present application belongs to the technical field of data analysis, and particularly relates to a high-throughput biosensing system for early warning of algal blooms and pathogens, which comprises multiple sampling units, a microfluidic chip with a multi-channel structure, and a high-throughput surface-enhanced Raman detection unit with an integrated metal nanostructure surface, and obtains the original surface-enhanced Raman spectrum of the water sample through a Raman spectrum acquisition mechanism. The system uses compressed spectrum sampling and sparse reconstruction methods to reduce the data volume and maintain the integrity of the spectrum information, constructs a pathogen Raman fingerprint dictionary through a dictionary learning method, and realizes the identification of different algal metabolites, algal toxins and pathogen-related molecular components and the concentration interval judgment thereof in combination with a sparse representation classification mechanism. Based on the type and concentration interval of the pathogens, the system further generates algal bloom risk warning and pathogen risk warning, and realizes early discovery of abnormal changes in the water body. The present application has significant advantages in stability, sensitivity, throughput and real-time performance.

The application discloses a power grid power transmission section identification method based on an improved PageRank algorithm and network conversion, first, an improved PageRank algorithm is used to calculate a node criticality index, and a network constraint coefficient is calculated according to a network topological structure and is used to correct a restart vector; second, a key power transmission section search topological graph is constructed through network conversion and node criticality index calculation, and identification of the key power transmission section is converted into search of a shortest path; finally, according to energy transaction between a generator node and a load node, a search range of the key power transmission section is set, a related cut set with the highest criticality is found, and through checking and correction, an identification result of the key power transmission section is obtained. The application can well improve the deficiency of the original PageRank algorithm when applied to the power grid, can accurately identify the power transmission section of the power grid, provides a key monitoring guide for power grid monitoring and dispatch personnel, and better guarantees the safe and stable operation of the power grid.

The utility model relates to a kind of point counting and checking machine, it includes two side wallboards and controller, two sides the wallboard between sequentially be equipped with counting mechanism, checking mechanism and receiving mechanism along longitudinal direction, the checking mechanism includes dispensing wheel and pass currency platform, the dispensing wheel is along the axial interval arrangement of second mounting shaft, the second mounting shaft rotation is arranged, the dispensing wheel and the pass currency platform between form pass currency passageway, wherein, the checking mechanism further includes optical detection component, the dispensing wheel between form checking gap, the optical detection component is configured at the checking gap, the optical detection component includes light source emission lamp and first receiver, light signal emitted by light source emission lamp reaches first receiver at checking gap at banknote surface, banknote is exposed at checking gap at banknote surface longitudinal direction when banknote passes through dispensing wheel when banknote passes through dispensing wheel, identification is accurate.

The application is suitable for the technical field of man-machine conversation, and provides a multi-round conversation generation method and device based on emotion judgment and terminal equipment, the method comprising: obtaining conversation information; identifying first emotion information in the conversation information through an emotion classifier, wherein the emotion classifier identifies and classifies emotions contained in the conversation information based on a classification model, and the first emotion information represents the emotion of the conversation information; determining second emotion information based on the first emotion information by using an emotion transfer mathematical model, wherein the second emotion information is one kind of emotion information in multiple first emotion information, and the emotion transfer mathematical model is used to determine the second emotion information based on conversation time and conversation round; and outputting the current round of conversation containing the second emotion information according to the second emotion information through a conversation generation model. The application can improve the recognition accuracy of the emotion classifier and improve the experience of users using the man-machine conversation system.

The invention belongs to the technical field of oil-gas field development, and discloses a low-salinity formation water identification method, which comprises the following steps of: obtaining data corresponding to a test oil well through drilling data, and comparing step by step according to the obtained data to judge whether water is discharged from a formation; the invention further discloses a low-salinity formation water recognition device and equipment and a storage medium. The method is suitable for identifying the low-salinity formation water, whether water flows out of the formation or not is judged in real time by draining the test oil well, an important basis is provided for identifying the low-salinity formation water, and the defects of a formation water judgment method are overcome.