89results about How to "Eliminate differences" patented technology

The invention relates to the technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention discloses an operator migration method and device between hardware platforms and a migration model construction method. The migration method comprises the following steps: obtaining an operator code and a function label of an operator to be migrated; when the existing operator information of the target hardware platform does not contain the function label, obtaining first information of the target hardware platform and second information of the source hardware platform; training a large language model based on the first information and the second information, and taking the trained large language model as an operator automatic migration development model; generating an initial operator matched with the precision requirement of the target hardware platform based on the operator automatic migration development model and the operator code; analyzing the initial operator to obtain an operator precision difference; taking an initial operator corresponding to the operator precision difference passing verification as a target operator; and storing the target operator to a corresponding level of the target hardware platform. According to the method, automatic non-inductive migration of the model operator on different hardware platforms can be realized, and efficient and seamless adaptation with different platforms is realized.

The invention discloses a face anti-fraud model training method and device based on image-text comparison assisted learning, and belongs to the technical field of artificial intelligence. The method comprises the following steps: identifying the content of each sample image in an image set to obtain a description text corresponding to each sample image; generating text data corresponding to each sample image in combination with the description text and the label; and inputting each sample image in the image set into an image encoder in the multi-modal pre-training model, and inputting text data corresponding to each image into the text encoder in the multi-modal pre-training model, so as to train the multi-modal pre-training model and obtain a face anti-fraud model. According to the method, the image content description text and the face type label are integrated, the features are mixed, the central difference convolution adapter module is introduced to capture the fine features of the face, the features are subjected to semantic alignment through image-text comparison learning and matching, and the generalization ability of the model in different application scenes is improved.

The invention discloses a time sequence heterogeneous network link prediction method and system based on hierarchical comparative learning, and relates to the technical field of time sequence heterogeneous network prediction. The invention aims to mine and model the space complexity and the time sequence complexity in the network from three different microscopic perspectives of node level, edge level and time level so as to predict the node connection behavior in the time sequence heterogeneous network in the real world. According to the technical key points, heterogeneous structure information of node representation vectors is stored through a proposed link prediction method, the time sequence evolution process of the heterogeneous network is captured, meanwhile, the topological dependency relationship between heterogeneous snapshots is captured, and the distribution mode in the complex time sequence heterogeneous network is described, so that the dynamic and complex connection relationship between entities is predicted. The fine-grained difference relation between different nodes and edges and the difference of evolution paradigms directly affect representation learning, and the performance of link prediction is affected to a great extent. The method is used for predicting dynamic and complex connection relations between entities in social recommendation and traffic management.

The invention relates to an intelligent glasses control method and system based on multi-modal privacy protection, and the method comprises the steps: collecting multi-modal sensory data of a user, carrying out the preprocessing of the multi-modal sensory data, and generating sensory sharing data; analyzing the sensory shared data by using a multi-modal privacy feature extraction algorithm to generate a privacy feature vector; classifying and identifying the privacy feature vectors based on a multi-layer privacy judgment rule to obtain a judgment result; performing data processing based on processing logic in the multi-layer privacy decision rule to generate shared data after privacy protection processing; storing the shared data subjected to privacy protection processing to a cloud end, retrieving the corresponding shared data subjected to privacy protection processing from the cloud end according to the sharing demand information of the target equipment, and encrypting the shared data to generate encrypted shared data; and decrypting and reconstructing the encrypted shared data in the target equipment to generate shared display data. The method has the effect of improving the accuracy of identifying the privacy data of the intelligent glasses.

The invention discloses a method for quantifying the fusion degree of new asphalt and old asphalt, and relates to the technical field of road thermal regeneration. An asphalt film sample on the surface of an RAP material is subjected to layered extraction, and a uniform film is formed in combination with a potassium bromide sheet technology; secondly, acquiring spectral characteristic data and characteristic peak values of each layer of sample by adopting a Fourier transform infrared spectroscopy FTIR technology, normalizing the data by adopting methods such as baseline correction and standardization, extracting characteristic peak positions and intensity by utilizing a first-order derivative, and constructing a standardized curve; a dynamic time warping (DTW) algorithm is adopted to quantify the spectrum curve matching degree of the new asphalt and the old asphalt, a fusion similarity index FSI is defined, and the similarity of the new asphalt and the old asphalt in chemical component distribution is effectively quantified; meanwhile, a microscopic infrared spectrum technology is adopted to further perform local scanning on RAP particle surface spectrum distribution, a fusion heat map is generated in combination with an FSI result, and the local fusion difference of the new asphalt and the old asphalt is visually displayed.

The invention belongs to the technical field of Internet, and particularly relates to an energy optimization scheduling method and system, a terminal and a storage medium, and the method comprises the steps: collecting real-time energy data, equipment state information and environment data of energy consumption equipment in a park; processing the real-time energy data, the equipment state information and the environment data by adopting a multi-source data fusion technology to obtain a park data set so as to generate a uniform data input format; obtaining actual demand data of the park, inputting the park data set and the actual demand data of the park into a pre-trained energy model for prediction, and obtaining prediction results of the energy demand and the equipment health state of the park at the next moment; and adjusting the operation mode and parameter of the corresponding equipment based on the prediction result of the energy demand and the equipment health state of the park at the next moment. The method has the effect of improving the energy utilization rate of the park.

The embodiment of the invention relates to the technical field of communication, in particular to a channel establishment method and device and a first terminal. The method comprises the following steps: under the condition that a channel establishment request initiated by a first application is obtained, executing a channel establishment method in an SDK (Software Development Kit) to obtain establishment parameter information required for establishing a data channel; sending the creation parameter information to a background service to indicate the background service to interact with an IMS protocol stack in the first terminal according to the creation parameter information, and constructing a data channel between the first terminal and a second application of the second terminal; the first application and the second application are the same application.

The invention discloses a method and a system for predicting a starting transient temperature field of a photo-thermal steam turbine rotor. The method comprises the following steps of: obtaining material setting, contact construction and boundary conditions of the photo-thermal steam turbine rotor to be predicted, and obtaining steady-state temperature field data of the rotor through finite elements; preprocessing the acquired steady-state temperature field data; exporting a steady-state temperature field of the result, converting the steady-state temperature field into steady-state temperature values of all nodes sorted according to node numbers and adding and generating the steady-state temperature values to obtain a steady-state temperature field sequence; and inputting the preprocessed steady-state temperature field sequence data into a preset transient temperature field prediction model to obtain a to-be-predicted starting transient temperature field of the photo-thermal steam turbine rotor. The system comprises a data acquisition module, a data preprocessing module and a prediction module. According to the invention, rapid and accurate prediction of the temperature field is realized, and a rapid decision basis is provided for operation optimization, life calculation and reliability maintenance of the unit.

The invention relates to the technical field of constructional engineering environment monitoring, in particular to a constructional engineering environment monitoring method and system. The method comprises the following steps: performing feature extraction on a particulate matter real-time spectrum to obtain a particulate matter real-time feature spectrum; performing spectrum color conversion effect simulation on the particulate matter real-time characteristic spectrum, and performing scattering glare view angle analysis to obtain scattering glare view angle data; carrying out medium influence weight judgment according to the scattering glare view angle data to obtain particulate matter glare influence weight data; performing convolution calculation on the particulate matter glare influence weight data, and performing logic learning to obtain particulate matter glare view angle logic data; and carrying out automatic identification firmware design based on the particulate matter glare view angle logic data to obtain particulate matter glare view angle identification firmware. According to the invention, the construction engineering environment monitoring technology is optimized, so that the construction engineering environment monitoring technology is more perfect.

The present invention provides a digital twin system for attitude and orbit control based on a spacecraft cluster and a construction method thereof. The digital twin system for attitude and orbit control based on a spacecraft cluster includes several digital simulation subsystems, and the digital simulation subsystems include a source code dynamic link library of the spacecraft cluster, an attitude and orbit dynamics module, a telemetry and remote control interface, and a fault setting interface. The source code dynamic link library of the spacecraft cluster is connected to the attitude and orbit dynamics module, the telemetry and remote control interface is used to implement the telemetry and remote control function, and the fault setting interface is used to implement fault setting. The technical solution of this application simplifies the testing process of attitude and orbit control software products, shortens the development cycle; saves development costs; provides a reliable environment; and can adapt to the continuous increase in the scale of spacecraft.

The invention discloses a method for predicting the residual capacity of a vehicle-mounted lithium battery, and the method comprises the steps: achieving the collection of battery data through a battery management system, analyzing the correlation of the multi-dimensional features of the data through a cloud server according to the data of a plurality of electric vehicle battery samples through a Pearson correlation coefficient algorithm, and screening out the strong features which greatly affect the capacity of the battery; performing standardization processing on the data; constructing an LSTM neural network prediction model according to the dimension of the input data; setting parameters of a chaos particle swarm optimization algorithm according to the neural network structure, and applying the optimal parameters obtained by the chaos particle swarm optimization algorithm to the LSTM neural network; and finally completing training and testing of the neural network model to obtain the LSTM neural network model capable of being used for prediction. According to the invention, the Pearson's correlation coefficient algorithm is utilized to carry out battery capacity correlation characteristic analysis, strong characteristics having great influence on the battery capacity are found out, and the accuracy of the model is improved; and the LSTM neural network model optimized by the chaos particle swarm optimization is used for prediction, so that the prediction precision is improved.

The present invention discloses a method and system for automatically generating a multimodal standardized knowledge graph, which specifically relates to the technical field of automatic generation of educational knowledge graphs, and is used to solve the problem of inaccurate knowledge graph generation. The present invention preprocesses and selects features of multimodal data, performs text segmentation, video key frame extraction, and image grayscale conversion, so that different modal data are standardized to eliminate differences between each other, and then screens the key features of each modality through information gain, and performs normalization processing to ensure that the feature vector has a uniform numerical range, laying a foundation for subsequent feature fusion and knowledge graph generation. The weight is calculated through a modal credibility function, and the importance of each modality is considered in the feature fusion process to generate a unified feature vector, avoid hierarchical conflicts, and automatically update according to dynamic changes in data, dynamically adjust knowledge points and their hierarchical labels, thereby improving the intelligence and adaptability of the graph and the efficiency of knowledge management.

The invention discloses a seal verification method, an electronic device, a storage medium and a product, and relates to the technical field of image recognition, and the method comprises the steps: determining a to-be-verified seal and a target reserved seal corresponding to the to-be-verified seal; aligning the to-be-verified seal with the target reserved seal, and inputting the aligned target reserved seal and the to-be-verified seal into a preset depth measurement network to determine the difference between the target reserved seal and the to-be-verified seal; and when the difference is smaller than a preset similarity threshold value, determining that the to-be-verified seal is a true seal. The technical problem that the seal verification effect is poor is solved.

The invention discloses a drainage monitoring data intelligent platform, and belongs to the technical field of data processing. Collecting drainage information in real time through intelligent monitoring equipment, recording collection time, integrating to generate a monitoring log, and transmitting the monitoring log to a cloud database for storage; water quality parameters are obtained through laboratory analysis of a water sample and are converted into standardized numerical values so as to eliminate differences among different dimensions; based on the weight corresponding to the water quality parameter, calculating to obtain a comprehensive water quality index, and performing early warning prompt according to the comprehensive water quality index; the average flow velocity is obtained by obtaining the flow velocity collected in the monitoring period, and the pressure loss value is calculated and compared with the preset threshold value so as to adjust the operation parameters of the pump station. A comprehensive water quality index, a pressure loss value, a water level and a flow rate are selected as target features, a decision function output result of a support vector machine (SVM) is utilized to predict a drainage operation state, and an early warning prompt is given out according to a prediction result.

The invention provides a multi-source data processing method, system and device, equipment and a storage medium. The method comprises the steps of obtaining multi-source signal lamp data, performing data standardization processing on the signal lamp data according to a preset standardization protocol to obtain standardized signal lamp data, making the standardization protocol based on business application of a vehicle end, eliminating the difference of the signal lamp data among the multiple sources through data standardization processing, and obtaining the standardized signal lamp data. The flow of signal lamp data application is ensured, and the data processing flow of the vehicle end is simplified; and performing quality verification on the standardized signal lamp data to determine whether the standardized signal lamp data quality is qualified, and if the standardized signal lamp data quality is qualified, sending the standardized signal lamp data to the vehicle end for application of the vehicle end. According to the method, the development and maintenance cost of the application program is reduced while the safety of the vehicle end is improved.

The invention discloses a domain adaptation WiFi human body sensing method based on a comparative learning model, and the method comprises the steps: taking pre-collected data as source domain data when a human body sensing model based on WiFi CSI is trained, taking the data of a place where practical application is deployed as target domain data, and carrying out the training of a human body sensing model based on WiFi CSI; the method comprises the following steps: firstly, learning a migration corresponding relation between a source domain and a target domain by adopting an unsupervised generative adversarial network model, and obtaining a feature extractor of WiFi CSI data; and the CDCL model constructs positive and negative samples by using a data enhancement method to carry out comparative learning to obtain accurate data representation of a sub-domain and realize fine-grained domain self-adaption. According to the technical scheme of the invention, various environmental interference factors can be eliminated at the same time, the difference between the sub-domains of each environmental interference domain is eliminated, fine-grained domain alignment is realized, the interference of sub-domain superposition of various environmental domains is eliminated, and the accuracy of WiFi-based human body perception and the reliability of the system are improved.

The invention belongs to the technical field of power grid time synchronization management, and discloses a power grid time synchronization management method based on big data analysis. Comprising the steps of collecting comprehensive power grid information at a current moment, performing standardization processing on the comprehensive power grid information, extracting feature data according to the processed comprehensive power grid information, and obtaining a comprehensive feature set; performing data dimension reduction processing through correlation analysis of each data category in the comprehensive feature set to obtain a low-dimension data set; training a time synchronization scheme design model by using the training set, and importing the real-time low-dimensional data set into the trained time synchronization scheme design model to obtain a real-time adjustment scheme; and performing visual interaction on the adjustment scheme to realize intelligent management of power grid time synchronization.

The invention relates to the technical field of equipment synchronous calibration, and particularly discloses a synchronous calibration method based on multi-equipment control, which comprises the following steps: designing a calibration board card, and counting equipment which is connected with the calibration board card and needs to be subjected to synchronous calibration; any two devices are controlled to communicate, time delay generated in the process that a signal reaches a receiving device from a sending device in the communication process is obtained, and a matrix T is generated after all combinations are traversed; performing operation on the matrix T to obtain a matrix C; calculating the average number of each row of the matrix C, and obtaining the maximum value in the average number; and taking the difference value between the maximum value and the average corresponding to the equipment as the time delay required to be compensated by the equipment. According to the invention, synchronization of signal sending / receiving of the equipment can be realized based on the time point of signal sending / receiving of the equipment.

The application discloses a brain function state evaluation and psychological disease auxiliary analysis system based on a brain fluctuation map, comprising: a multi-modal electroencephalogram signal acquisition and preprocessing module; a brain fluctuation map feature extraction module; an individualized baseline modeling module; and an adaptive weighting evaluation module based on an attention mechanism, which constructs an individualized deviation vector and adaptively weights the current features and deviation features based on an attention network, and outputs a disease risk probability and a channel contribution weight map. Compared with traditional population norms or simple arithmetic means, the method can more truly reflect the stable ground state of the subject itself, effectively eliminate individual differences and measurement noise, and provide accurate dynamic reference system for subsequent abnormal deviation judgment. Meanwhile, the fluctuation threshold calculation unit sets an adaptive threshold based on a statistical confidence interval, and introduces an abnormal deviation index and a multi-dimensional joint determination, further reducing the false positive risk.

The present invention discloses a method and device for flexibly encapsulating a webservice interface of a network element platform as a standard soap interface, the method comprising: S01, operation and maintenance personnel configure the device, configure the webservice interface address and port of the encapsulated network element platform; S02, perform a connectivity test, perform a connectivity test on the webservice protocol URL address of the corresponding acquisition and control source, and the test method is a simulated file transfer tool; S03, perform parameter processing; S04, perform message management; S05, perform an FTL test, if the FTL test result fails, proceed to S06, if the FTL test result succeeds, proceed to S07; S06, if the FTL test result fails, re-perform parameter processing configuration; S07, if the FTL test result succeeds, complete the capability packaging work. The present invention realizes a flexible and configurable packaging function, and compared with the previous customized development packaging capability, it can quickly deliver network capabilities and greatly improve network operation and maintenance capabilities.

The present application relates to a multi-source data consistency processing method, device, computer equipment, computer-readable storage medium and computer program product based on a data comparison table, which can be used in the field of big data technology. The method includes: establishing a corresponding data comparison table for the source system; performing data conversion processing on the original data to obtain the target data, and updating the target data to the source system field set in the data comparison table; updating the data change information to the master data platform field set in the data comparison table; performing data comparison processing on the source system field set and the master data platform field set according to the corresponding relationship to obtain the data comparison result; determining the data to be checked in the data comparison table according to the data comparison result; checking the data to be checked to obtain the checking result; and performing data consistency update processing on the master data platform or the source system according to the checking result. The use of this method can improve the efficiency of data consistency processing.

The invention discloses a carbon dioxide inter-section displacement pre-fracturing process and a design method and related equipment thereof, and belongs to the technical field of oil and gas production, the design method comprises the following steps: establishing an integrated fractured horizontal well numerical simulation model according to seismic data of a target well, logging interpretation data and an engineering parameter range given by an oil production scheme; the integrated fractured horizontal well numerical simulation model is used for simulating single well yields under different section intervals and different single-section carbon dioxide injection amounts by adopting a section-separated gas injection method; drawing a parameter optimization chart based on the single well yield and the carbon dioxide injection section number obtained under different section distances and different single-section carbon dioxide injection amounts; and according to the parameter optimization plate, determining the final segment spacing and the single-segment carbon dioxide injection amount. While the recovery efficiency is improved, the construction efficiency is improved, and the fracturing cost is saved.

The invention discloses a partial-label-oriented multi-modal learning method and system and a storage medium, and the method comprises the following steps: S1, processing a training data set comprising an image and corresponding text description, and obtaining a training data set with partial labels; s2, according to the processed training data set, constructing a partial-label-oriented multi-modal learning model for cross-modal retrieval; s3, inputting a data mode needing to be retrieved into the multi-mode learning model, and calculating the similarity between the data mode and the data in the retrieval library; s4, similarity sorting is carried out according to the calculated data similarity, and a cross-modal retrieval result is output; according to the method, the problems of label ambiguity and modal difference faced by cross-modal retrieval with part of labels are solved, and the accuracy of cross-modal retrieval is improved.

The application relates to the field of artificial intelligence and power equipment operation and maintenance data processing technology, and particularly discloses a power energy storage operation and maintenance health evaluation method based on deep learning, which comprises the following steps: a pre-processing unit first performs caliber unification according to a historical caliber change set and extracts single data in real time, eliminates equipment differences, makes the algorithm identify only one set of caliber, and reduces special cases and error sources; an edge gateway performs clock consistency test, aligns the time axis, and avoids wrong time misjudgment; if the test fails, the data is improved and data reliability is output, and the caliber change set is rewritten and corrected; then, the data and the reliability are sent to a model to perform health evaluation and physical consistency verification, and the correlation is improved to be explainable physical reliability; finally, the evaluation result is mapped to a standardized operation and maintenance disposal suggestion, and the operation and maintenance plan is synchronized to a platform; the standardized action and plan are synchronized to realize a closed loop of diagnosis, disposal and recovery, and the whole process is auditable and reproducible.

The invention discloses an axle load leveling method for an operation machine and the operation machine, the axle load leveling method can be applied to axle load leveling of the operation machine such as a hybrid power all-terrain crane, and the axle load leveling method comprises the following steps: determining a leveling compensation load according to a lower load of a driving shaft and a lower load of a driven shaft; the leveling upper load of the driven shaft is determined according to the leveling compensation load and the upper load of the driving shaft; the leveling compensation angle of the driven shaft suspension oil cylinder is determined according to the leveling upper load; and the installation angle of the driven shaft suspension oil cylinder relative to the vertical direction is adjusted according to the leveling compensation angle. According to the axle load leveling method, the upper load of the driven shaft is increased by adjusting the mounting angle of the driven shaft suspension oil cylinder relative to the vertical direction, then the single-axle load of the driven shaft is equal to the single-axle load of the driving shaft, the difference between the lower load of the driving shaft and the lower load of the driven shaft is made up, the axle load of the driven shaft can be increased to the maximum extent, and the service life of the driven shaft is prolonged. And the working performance of the working machine is improved.

This application discloses a gamma curve calibration method, a gamma curve calibration device, and a computer storage medium. The gamma curve calibration method includes: acquiring first image data from a reference device and second image data from a test device under identical conditions; acquiring the data difference between the first and second image data; using the data difference and the first image data from the reference device to obtain a difference curve between the reference device and the test device; and determining the gamma curve of the test device based on the difference curve and the gamma curve of the reference device. Through this method, the gamma curve calibration method of this application can automatically calibrate the gamma curve and effectively eliminate device variability issues, improving the dynamic range brightness consistency of the device.

The present application discloses a method and system for pushing advertisement matching for video screens, which collects the minimum duration and maximum duration of advertisement materials to be matched, collects at least one historical matching information from the advertisement material library according to the minimum duration and the maximum duration, and outputs a directory of historical matching information to be matched; collects video screens provided by a video platform, obtains advertisement materials that meet the video screens from the directory of historical matching information, and outputs candidate advertisement materials; collects the minimum number of playbacks of the advertisement materials to be matched, and collects recommended advertisement materials from the candidate advertisement materials according to the minimum number of playbacks; collects advertisement information in the recommended advertisement materials, and outputs recommended advertisements matched by the video platform. In the present invention, candidate advertisements between multiple video screens can be collected, which helps to eliminate the differences between multiple video screens according to the candidate advertisements in the subsequent process, so as to improve the pertinence and stability of the project.

The invention discloses a thoracolumbar vertebra fracture detection method and system based on sequence modeling and a prior graph, and the method comprises the steps: obtaining thoracolumbar vertebra CT volume data and fracture type truth value label data, carrying out the preprocessing, and constructing a training set and a test set; and constructing a fracture detection multi-branch classification network model, training and testing the constructed fracture detection multi-branch classification network model by using the training set and the test set, preprocessing thoracolumbar vertebra CT body data of a to-be-detected patient, inputting the preprocessed thoracolumbar vertebra CT body data into the trained fracture detection multi-branch classification network model, and outputting a fracture type classification prediction result. By designing an end-to-end framework of an image feature branch, a label relation branch and a branch fusion module, the problems that in the prior art, continuity of a vertebral body structure is deficient, long-range dependence is insufficient, feature fusion is insufficient and the like are solved, and accurate detection and refined classification of thoracolumbar vertebral fractures are achieved.

The invention relates to the technical field of bioengineering, in particular to a universal method for constructing Ribo-seq sequencing libraries of different tissues and cells of mice and people. The method specifically comprises the following steps: splitting a sample, collecting a splitting product supernatant, adding the splitting product supernatant to the surface of a 30% sucrose solution, centrifuging, re-suspending and precipitating, adding RNase I with the concentration of 128 U / OD, and carrying out enzyme digestion for 0.5 h at the temperature of 25 DEG C; and carrying out RPF extraction, library construction sequencing and data analysis on the enzyme digestion product. The invention aims to construct a method of an improved Ribo-seq sequencing library widely applicable to different tissues and cells of mice and people, condition exploration for different tissues is not needed, the application range is wide, and the experiment difficulty is reduced.