235results about How to "Resolve accuracy" patented technology

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

The invention relates to the technical field of carrier communication, in particular to a multichannel power line carrier communication system which comprises a data acquisition module, a first determination module, a second determination module, a judgment module, a selection module, a sending module and a correction module. According to the invention, through an accurate data acquisition and real-time determination mechanism, complex environment changes in a power network can be effectively dealt with, stability and high efficiency of communication are ensured, channels with optimal parameters such as signal strength, noise, delay and the like are optimized through multi-stage screening and determination, channels with relatively large interference and noise are effectively avoided, and the communication efficiency is improved. Meanwhile, the correction module can adjust the intensity threshold value in real time, it is guaranteed that the system still maintains excellent communication performance when the number of channels fluctuates and the network load changes, and the problems that due to the fact that the attention degree of the influence of real-time environment changes on signal quality is low, channel selection is inaccurate, and verification results are deviated are effectively solved.

The invention discloses a power cable intelligent segmentation management method and system, and belongs to the field of power supply management, and the method comprises the steps: setting a first time sequence segmentation influence coefficient and a second time sequence segmentation influence coefficient, and building a dynamic decision node; acquiring cable layout information of a to-be-monitored power cable; configuring a backtracking time window, and establishing backtracking basic parameters of the power cable to be monitored; according to the backtracking basic parameters, adaptive analysis of balancing task requirements and balancing climate influences is carried out, and a first clustering constraint is generated based on an adaptive analysis result; and establishing a first clustering constraint according to the cable layout information, establishing a second clustering constraint according to the cable layout information, establishing a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint and the dynamic decision node, allocating an edge joint early warning network, and executing segmentation management based on the edge joint early warning network. A segmentation strategy is dynamically generated based on multi-dimensional data, precise and intelligent early warning and management of the power cable are realized, and the early warning response speed and accuracy are improved.

The invention relates to a method, a device and equipment for estimating the state of a power battery of a sweeper. The method comprises the following steps: performing impedance characteristic acquisition on a power battery of the sweeper under various working conditions, and performing random low-frequency modulation multi-sine excitation signal measurement to obtain battery working condition characteristic data; performing fitting operation through the equivalent circuit model to obtain a battery health factor model; performing charging process feature extraction analysis to obtain a battery charging curve reconstruction model; carrying out extended Kalman filtering operation to obtain a battery state-of-charge value; performing segment processing and Gaussian process regression calculation on voltage and current data in the charging process to obtain a battery health state value; and updating the polynomial mapping coefficient of the battery health factor model and the weight parameter of the battery charging curve reconstruction model to obtain a target health factor model and a target charging curve reconstruction model. According to the invention, the adaptability and accuracy of the state estimation method in the whole life cycle of the battery are ensured.

The invention discloses a high-speed railway roadbed structure design method based on dynamic stability. The method comprises the following steps: starting a design process: clearly designing load and control conditions; constructing a lumped parameter dynamic coupling model of the high-speed railway vehicle-track-roadbed layered structure; setting parameter input and verification are carried out on the model based on the design parameter system; solving a roadbed layered dynamic response result through the dynamic coupling model; judging critical conditions of the first design threshold and the second design threshold; various design parameters meeting the first design control threshold value and the second design control threshold value are substituted into the dynamic coupling model for recalculation, it is verified that various dynamic response rules meet design requirements, and finally the design target is completed. According to a typical high-speed railway subgrade structure subgrade bed surface layer graded broken stone and subgrade bed bottom layer coarse-grained soil filler type, a high-speed railway subgrade filler dynamic stability critical state judgment standard is established in combination with a long-term deformation development rule and hysteresis energy characteristics of different fillers under the action of train power; and the support is provided for the stability design of the high-speed railway roadbed structure.

The invention discloses a road obstacle recognition method based on machine vision, and the method comprises the steps: carrying out the precise positioning of the spatial position of an obstacle through a multi-sensor information fusion algorithm according to the azimuth and distance information of the obstacle estimated through a sound source positioning algorithm and the position information outputted by an obstacle detection model, and improving the positioning precision; the method comprises the following steps: acquiring guide mark information, such as road signs and lane lines, extracting guide mark features through a semantic segmentation algorithm, carrying out dynamic fusion on the guide mark features and an obstacle recognition result, and improving the accuracy and robustness of obstacle recognition by using prior information; in order to solve the problem of insufficient parallax analysis precision in long-distance, high-speed motion and other scenes, a monocular depth estimation algorithm based on deep learning is introduced, pixel-level depth information is predicted from a single image through a convolutional neural network, and the precision of obstacle distance estimation is improved.

The invention provides an evaluation method of a long document retrieval enhancement generation large model, and relates to the technical field of data processing, the method comprises the following steps: constructing an automatic question answering strategy based on a focus segment; constructing an evaluation data set, and processing the evaluation data set based on an automatic problem strategy to obtain an optimal evaluation data set for generating a large model by aiming at long document retrieval enhancement; and performing evaluation on the basis of an optimal evaluation data set for generating the large model by aiming at long document retrieval enhancement. According to the method, the problems that a long document question and answer data set is difficult to construct and an evaluation method aiming at a long document retrieval enhancement generation large model system is lacked are solved.

The invention relates to a LiDAR point cloud precision enhancement method based on POS trajectory correction, and the method comprises the steps: laying a target control point, and obtaining data; fusing the base station GNSS data, the mobile station GNSS data and the inertial navigation measurement data to obtain an initial POS trajectory; importing actual measurement coordinates of a target control point on the basis of the laser scanning data and the initial POS trajectory, and fusing nearby point clouds to obtain pre-fused point clouds; matching and fitting to obtain a fitting center point; searching the nearest point cloud, and interpolating the time of the fitting center point according to the GPS time information; and correcting the initial POS trajectory according to the actually measured coordinates of the two target control points obtained at the same time and the coordinates of the point cloud target, and performing secondary fusion with the laser scanning data to obtain result data. According to the method, the automatic target matching and center point fitting algorithm is adopted, the error introduction of manual target point selection is reduced, the strict requirement for field data acquisition is reduced, and the working efficiency is improved.

The invention relates to a multi-source data fusion statistical analysis method based on a Bayesian network. The method comprises the following steps: collecting electric power multi-source data samples of a long time sequence within a period of time; identifying main features of the multi-source data through an auto-encoder to obtain reconstructed multi-source data; constructing a Bayesian network model by using the reconstructed multi-source data, and determining causal probability association features among the multi-source data; identifying time correlation features of the multi-source data by constructing a time sequence model; coupling the Bayesian network model and the time sequence model to obtain a multi-source data association model; and obtaining multi-source data of a to-be-configured time period, inputting the multi-source data association model, identifying a multi-source data variable with abnormal data, and adjusting the abnormal data variable to optimize power configuration. According to the invention, the problem that the accuracy and rationality of power configuration are low due to the fact that relevance among multi-source data is difficult to identify in the prior art is solved.

The invention discloses a reimbursement process optimization method and device based on a multi-modal model. The method comprises the following steps: receiving a to-be-reimbursed material submitted by a target object; performing image recognition and text extraction on the to-be-reimbursed material to obtain document classification information and reimbursement text information of the to-be-reimbursed material; determining a reimbursement type based on the information by using a reimbursement classification model; comparing the feature information of the target object and the above information with fields in the reimbursement application form, and if missing information does not exist, directly generating a target reimbursement application form according to the above information; otherwise, filling the missing information submitted by the target object into the reimbursement application form to generate a target reimbursement application form; and submitting the target reimbursement application form to enter a reimbursement process. The technical problem that the efficiency and accuracy of the reimbursement process are low due to the fact that the traditional financial reimbursement process generally depends on manual operation, the operation process is tedious and time-consuming, and errors are prone to occurring in the prior art is solved.

The invention relates to the technical field of charging management, and provides a new energy automobile charging management system and a charging device, a cloud server is in communication data connection with a user mobile terminal and the charging device, and the cloud server comprises a battery analysis module and a charging planning module; the battery analysis module obtains battery performance data from the charging device, establishes a battery internal resistance prediction model, and calculates the charging time based on the battery internal resistance prediction model when the charging device is connected to a new battery; the charging planning module is in data connection with the battery analysis module, acquires charging device related information from a charging device, and performs charging planning in combination with user vehicle related information acquired at a user mobile terminal; and finally transmitting the data to a user terminal and pushing the data to a reservation user. By constructing the battery internal resistance prediction model, the problem that an existing charging management system cannot estimate the charging time accurately is solved, a user can accurately judge the charging time and arrange a travel plan, and meanwhile the utilization rate of a charging device is increased.

The invention discloses a risk assessment method for enterprise cross-domain data. The method comprises the following steps: collecting original assessment data of an enterprise for data exchange and sharing among different departments; preprocessing the collected original evaluation data, wherein the preprocessing comprises the steps of carrying out standardization processing on numerical data and carrying out fuzzification processing on text description data; constructing a risk assessment model based on a Bayesian network, and determining a dependency relationship and a conditional probability between risk factors by introducing expert knowledge and historical data; calculating the posterior probability of each risk factor through a Bayesian network; an expert evaluation method is adopted to allocate a weight for each risk evaluation index to carry out risk-based scoring on each risk factor; and calculating a comprehensive risk score of the enterprise cross-domain data through a comprehensive risk scoring formula. According to the method, an enterprise can be helped to accurately evaluate and manage risks in a complex data environment, and the data security and compliance are improved.

The invention relates to a multi-dimensional evaluation planning method and device for a thermal power plant integrated energy system, and the method comprises the steps: taking a coal-fired power generation unit as a core energy conversion device, and building a plurality of renewable fuel input alternative items except fire coal and a plurality of energy product supply alternative items except electric power; establishing a multi-dimensional evaluation index system; generating a mathematical model of the thermal power plant integrated energy system based on a pre-established thermal balance all-condition mathematical model library of the thermal power plant integrated energy system and the target configuration combination; and generating a final planning scheme and an index evaluation condition of the thermal power plant integrated energy system according to the configuration meeting the preset optimal condition and the equipment capacity. According to the embodiment of the invention, various indexes of the comprehensive energy system based on the thermal power plant are evaluated from multiple angles, and a multi-objective optimization method and the like are adopted, so that the optimization method for comprehensive energy transformation of the coal-fired thermal power generating unit is established, and effective reference is provided for engineering decision-making of comprehensive energy transformation of the coal-fired thermal power generating unit.

The invention relates to the field of image recognition or understanding, in particular to a face and tongue coat recognition and tea drink recommendation method and system based on artificial intelligence. The method comprises the following steps: identifying a plurality of image annotation labels, training a feature extraction model and a classification model by using the annotated images, obtaining a new image, performing feature extraction by using the trained feature extraction model, and inputting the feature into the trained classification model to obtain a label of the new image. The device is used for applying the method. The existing method and system for identifying the face and the tongue coating of the tea making machine and recommending the tea making have the problem that the identification of a face image and a tongue coating image is not accurate enough. The method for identifying the face and the tongue coating of the tea making machine and recommending the tea making based on the artificial intelligence provided by the invention is accurate in identification of the face image and the tongue coating image.

The invention discloses a personnel off-post detection method and device, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a current frame monitoring image in a target monitoring scene, and transmitting the current frame monitoring image to a YOLO target detection model to obtain a first image personnel number; if the number of the first image personnel is smaller than the preset number of post personnel, detecting whether the post lacking duration reaches the preset duration or not; if the off-duty duration reaches the preset duration, detecting the current frame of monitoring image through a multi-modal target detection model to obtain a second image personnel number; and if the number of personnel in the second image is smaller than the number of post personnel, determining that personnel leaving exists in the target monitoring scene. According to the method and the device, the high efficiency of the YOLO target detection model and the high accuracy of the multi-modal target detection model are combined, so that the relatively high detection efficiency is maintained, and the accuracy of personnel off-post detection is improved.

The invention discloses a document generation method and device, a storage medium and electronic equipment, and relates to the field of artificial intelligence. The method comprises the steps of obtaining a target template, table data and a target theme input by a target user; determining a target directory title associated with the target theme according to the target template; the semantic similarity of the table data, the target content and the target directory title is calculated, the target content is text content obtained from a target library and associated with the target theme and the target directory title, and the target library is a database comprising data and documents associated with multiple services; and matching the table data and the target content to a corresponding target directory title according to the semantic similarity to obtain a target document corresponding to the target topic. According to the method and the device, the technical problems of low efficiency and low accuracy when a traditional method is used for processing document automatic generation are solved.

The invention provides a river flow online measurement and calculation method based on multi-source data fusion, and belongs to the technical field of river flow measurement. A Bayesian neural network and active learning collaborative hydrological memory reconstruction model is adopted to carry out high-confidence data interpolation on a sensor failure period and quantify uncertainty, fractional calculus is introduced to model a river memory effect, and flow evolution is analyzed through a fractional order water balance equation. And selecting a steady-state or non-constant flow calculation mode according to the flow change rate, inverting an optimal flow field for the non-constant flow by adopting a four-dimensional variational data assimilation method in combination with regularization constraint and time smoothing constraint, and outputting a flow measurement result and an uncertainty quantitative index. The technical problems that flow measurement and calculation data are missing and accurate interpolation is difficult due to sensor failure under the extreme hydrological condition are solved.

The invention discloses a PDF (Portable Document Format) image screening method driven by intelligent analysis, which belongs to the technical field of document processing and comprises the following steps of: intelligently analyzing a plurality of PDF files to obtain a plurality of pieces of image information for format conversion; traversing the plurality of PDF files based on the plurality of bitmap formats for image capture; performing image content identification on the multiple pieces of image information according to the multiple pieces of image proportion data; identifying the plurality of pieces of image information based on a plurality of text data sets, and traversing a plurality of PDF files according to a plurality of image indexes; simulation preselection is carried out based on the target PDF file, full-text search is carried out based on multiple preselected images, preselected feedback information is sent to the remote control terminal, and an image screening instruction is generated to carry out intelligent screening response on the PDF images. According to the method and the device, the problems of low PDF file image processing efficiency and inaccurate information extraction in the prior art are solved, so that a user faces tedious manual screening and processing flows in document management and data analysis.

The invention discloses an intelligent question and answer method and system based on a mineral knowledge base, relates to the technical field of artificial intelligence and data mining, and is used for solving the technical problems that an existing mineral knowledge base is poor in reasoning ability and low in mineral knowledge acquisition efficiency and accuracy. The intelligent question and answer method based on the mineral knowledge base comprises the steps of obtaining mineral knowledge; distinguishing mineral names, mineral attribute names and mineral attribute values in the mineral knowledge; establishing a mineral knowledge base data table structure and a mineral knowledge graph triple structure; the mineral knowledge base data table structure and the mineral knowledge graph triple structure are stored in a knowledge database; building a webpage interface, wherein the webpage interface is provided with a search engine and an intelligent question and answer mechanism relying on a large model; the intelligent question-answering mechanism realizes intelligent question-answering of the mineral knowledge base based on the knowledge database. The invention is used for providing the intelligent question answering method and system based on the mineral knowledge base.

The invention provides an AI-based tourism intelligent interaction platform, and relates to the field of tourism intelligent interaction platforms, and the platform comprises the following units: a user portrait construction unit which is used for synthesizing basic information, historical behaviors and social media activity data of a user, and constructing an omnibearing user portrait; the dynamic itinerary planning unit dynamically plans an optimal itinerary according to the current position of the user, the weather, the traffic condition and the preference of the user; and the personalized recommendation unit is used for providing customized tourism recommendation for the user based on the user portrait and the real-time situation. And the intelligent travel photographing unit integrates an AI photographing guidance function and provides photographing position, angle and light suggestions for the user. According to the invention, omnibearing and intelligent tourism service is provided for the user; the user experience and the flexibility and accuracy of travel planning are improved, and the functions of travel partners with emotional resonance, intelligent travel photography guidance and the like are provided; meanwhile, data management and utilization are optimized.

The invention discloses a rumor detection method based on multivariate feature fusion and emotion difference analysis, and particularly relates to the technical field of rumor detection, and the technical key points are as follows: respectively inputting network text data and a plurality of pieces of network comment data into a BERT model for feature extraction; text semantic features and text emotion features corresponding to the network text data are obtained, and comment semantic features and comment emotion features corresponding to each piece of network comment data are obtained; performing feature fusion on the text semantic feature and the plurality of comment semantic features to obtain a fused semantic feature, and calculating the difference between the text emotion feature and each comment emotion feature to obtain a difference vector feature; and performing feature fusion on the fusion semantic features and the difference vector features to generate fusion features, and identifying the fusion features by using a full-connection network to obtain a rumor identification result corresponding to the news event.

The embodiment of the present invention discloses a data processing method, device, equipment and storage medium based on sentiment analysis, the method comprising: obtaining financial field review text data of a target date; inputting the financial field review text data into a hybrid neural network to obtain sentiment analysis results of the financial field review text data of the target date; generating financial market trend data of a target associated date according to the sentiment analysis results of the financial field review text data of the target date. The technical solution of the embodiment of the present invention can realize the automatic analysis and processing of financial field sentiment data, improve the accuracy and efficiency of the analysis and processing of financial field sentiment data, and thus improve the guidance and applicability of financial field sentiment data.

The invention belongs to the technical field of material lumpiness statistics, and particularly discloses a material lumpiness statistics method based on multi-view three-dimensional laser point clouds, which comprises the following steps of: obtaining point cloud data of materials through a three-dimensional laser scanner, preprocessing the obtained point cloud data of the materials, and storing the preprocessed point cloud data in a tree structure; determining point cloud groups of all surfaces of the material blocks by using a point cloud region growth algorithm, dividing the relationship between adjacent point cloud groups by using spatial feature normal vectors of the point cloud groups, and obtaining all point cloud groups of each material block in combination with a nearest neighbor algorithm so as to judge the spatial relationship of adjacent materials; according to the method, the point cloud data are preprocessed to reduce illumination variation, shielding objects, noise interference and the like, and the material identification precision and accuracy are improved, so that the problems of low material identification precision and poor accuracy caused by factors such as illumination variation, shielding objects, noise interference and the like in a complex environment during statistics of different forms of materials are solved.

The invention discloses a boiler NOx emission estimation method based on a width learning system and integrated learning. The boiler NOx emission estimation method comprises the steps of 1, data processing; step 2, constructing an integrated learning prediction model; step 3, performing real-time estimation; according to the invention, the hybrid quantum dune cat swarm algorithm QSCSO is provided, a Levy flight strategy and a quantum strategy are added on the basis of the SCSO for improvement, the problems of low convergence precision, low optimization efficiency and the like caused by limitation of global search and easy falling into local optimum of the SCSO are solved, and a modeling method based on QSCSO-BLS is designed for individual learning device construction. According to the method, the training precision and generalization ability of the BLS model during modeling of the single-working-condition system are improved, meanwhile, the boiler NOx emission estimation method based on QSCSO-BLS and integrated learning is provided, and the problem that the model generalization ability is poor during modeling of the BLS full-working-condition system is solved.

The invention relates to the technical field of warehouse logistics, and discloses a carrying method, a carrying device and a warehousing system.The method comprises the steps that the number of first to-be-put-in objects and / or the number of first to-be-put-out objects corresponding to at least one to-be-processed task is determined; according to at least one of the number of the first to-be-put-in objects and the number of the first to-be-put-out objects and the first number, a proportion threshold value corresponding to a cache region of the warehousing system is adjusted; wherein the first number is related to the number of cache bits in the cache region; determining the occupation proportion of cache bits in the cache region; and according to the occupancy proportion and a proportion threshold value, the first carrying equipment is controlled to carry the target warehouse-out object stored in the storage area in the warehousing system to the cache area, and / or carry the target warehouse-in object stored in the cache area to the storage area. According to the invention, adaptive adjustment of the proportion threshold value of the cache region can be realized, and the working efficiency of the warehousing system is improved.

The invention discloses a data processing method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining first graph structure data corresponding to a target transaction, determining at least one transaction sub-graph structure data corresponding to the first graph structure data, determining a first transaction graph processing model based on the transaction sub-graph structure data, performing edge structure knowledge distillation processing on the first transaction graph processing model to obtain a second transaction graph processing model, obtaining second graph structure data of the target transaction, and performing graph sparsification processing on the second graph structure data based on the second transaction graph processing model to obtain target transaction graph structure data, and performing target transaction processing based on the target transaction graph structure data.

The invention provides a work plan management method and device based on an intelligent management and control platform. The method comprises the following steps: firstly, obtaining an annual work plan, generating a preliminary target file through an automatic importing function, then setting rules according to the work field and category, carrying out verification and error correction on the target file, generating a corrected monthly plan file, then correcting the monthly plan through a temporary work plan, and forming a final weekly and daily work plan. And the completion progress is visually displayed in real time. According to the application, automatic proofreading, automatic splitting and automatic issuing of the annual plan are realized through the intelligent management and control platform, and the corresponding port is set for workers to modify the monthly plan and add the temporary plan, so that automation of planning and overall planning is realized, and the problem that in the prior art, all work planning, distribution and overall planning work are completed manually, and the working efficiency is high is solved. The efficiency is low and the accuracy is difficult to guarantee.

The invention discloses a belt sand ship safety dynamic management system, and the system comprises a ship information collection module which is used for collecting ship information, including position, speed and course information; the tide change trend prediction module is used for collecting tide information of the navigation area and obtaining the tide height and the tide change trend of the area where the ship is located in combination with the ship information; and the shallow water area depth change prediction module is used for collecting the sea chart data of the navigation area, and judging the depth change of the shallow water area during low tide by combining overlay analysis of the tide height data and the current sea chart water depth data. By integrating the tide change trend prediction module, the shallow water depth analysis module and the channel safety planning module, the system can monitor tide fluctuation and shallow water depth dynamic change in real time, and provide real-time data-based safety channel planning in combination with the ship draft.

The invention relates to the technical field of current sensors, in particular to a double-air-gap high-precision high-range current sensor which comprises two Hall chips and an MCU controller, the two Hall chips are used for outputting magnetic field signals into two paths of electric signals, the MCU controller is used for collecting and processing output signals of the Hall chips, and the MCU controller is used for controlling the Hall chips to output the output signals. Averaging the output signals of the two paths of Hall chips; and by adopting the design of averaging output of the two Hall chips, the accuracy and the stability of measurement can be improved. By averaging the output signals of the two Hall chips, measurement errors caused by factors such as errors and temperature drift of the chips can be reduced; the double-air-gap high-precision high-range current sensor provided by the invention can maintain good performance when being influenced by external temperature and humidity, and solves the technical problem that the accuracy and stability of a double-air-gap current sensor in the prior art become poor due to the fact that the double-air-gap current sensor is easily influenced by external temperature and humidity.

The invention discloses a state detection method and device of power equipment, electronic equipment and a storage medium. Comprising the steps that multi-dimensional data, including equipment data, detection data and external data, of power equipment is acquired, and the external data comprises one or more of video data and image data; performing feature extraction on the external data to obtain feature data, and associating the feature data, the equipment data and the detection data to obtain multi-dimensional feature data; determining associated feature data based on a pre-constructed knowledge graph; and performing state detection based on the multi-dimensional feature data and the associated feature data through at least one pre-trained state detection model to obtain at least one state detection result of the power equipment. According to the scheme, feature extraction of the multi-dimensional data of the power system is realized in combination with the knowledge graph, and the extracted multi-dimensional feature data and the associated feature data are processed through the state detection model to obtain the state detection result, so that the accuracy and the efficiency of the state detection result are improved.