125results about How to "Resolve inconsistencies" patented technology

The invention discloses a platform vehicle abnormal behavior intelligent analysis method based on deep learning. Accurate early warning is realized through multi-dimensional data acquisition and deep learning model construction. Firstly, platform passenger flow, road conditions, video monitoring and vehicle operation information are integrated, and a comprehensive data basis is provided for anomaly analysis. After the data is preprocessed, a graph neural network and an attention mechanism are utilized to perform preliminary fusion, and an AGCN adaptive graph convolutional network and a DTW dynamic time warping algorithm are introduced to optimize a fusion result, so that time sequence data are effectively aligned, and the feature extraction accuracy is improved. And then, a deep learning model is constructed to automatically identify abnormal behavior modes of the platform vehicles, such as illegal parking and passenger congestion. Early warning information is issued in real time, potential risks can be timely handled, and safe and efficient operation of a public transportation system is guaranteed. According to the scheme, the identification and early warning capability of the abnormal behavior of the station vehicle is enhanced, and powerful support is provided for bus safety management.

The invention discloses an end-cloud cooperative vehicle-mounted interaction method for multiple task scenes. The method comprises the following steps: (1) training a fine tuning model in an edge executor of a vehicle-mounted end; (2) the vehicle-mounted terminal receives a user instruction and converts the user instruction into a task text instruction; (3) calculating the execution confidence coefficient of the task text instruction, if the execution confidence coefficient is greater than a threshold, entering step (5), and if the execution confidence coefficient is less than the threshold, entering step (4); (4) the cloud planner receives the original task, decomposes the original task into sub-tasks through a cloud large model, converts the sub-tasks into task text instructions and then sends the task text instructions to the edge executor; (5) the edge executor receives a task text instruction, sends the task text instruction into the fine tuning and enlarging model and outputs user instructions meeting different scenes; a scene-related vehicle end API interface is called, and a processing result is returned; and (6) summarizing the returned processing result by the edge executor, and making a final response to a user instruction. The problem that calling interfaces of the vehicle-mounted external tool are not uniform can be solved, and the efficiency of processing a multi-task scene is improved.

The invention provides a multi-source medical data fusion method and system, a medium and equipment. The multi-source medical data fusion method comprises the steps of collecting multi-modal data from different data sources; performing feature extraction on the acquired multi-modal data by adopting a deep learning model to obtain feature data of different modal data; fusing the feature data of the different modal data to obtain fused data; and adjusting fusion weights of different modal data so as to carry out data quality control on the fusion data. According to the system, through a multi-source data fusion technology, the problem that medical data are scattered and inconsistent is solved, and the comprehensive utilization value of the data and the accuracy of analysis are improved.

The invention relates to the technical field of brain-computer interfaces, and discloses a multi-interface fusion dynamic brain-computer interaction method based on frequency phase information multiplexing and a terminal. Based on an offline electroencephalogram experiment of a user, the most brain electrical stimulation classification number U of the user for achieving the optimal electroencephalogram stimulation classification effect, frequency-phase information of the brain electrical stimulation of the U classification and a first corresponding relation between the brain electrical stimulation of the U classification and electroencephalogram signal characteristics are obtained, and an interactive interface after multi-interface fusion is dynamically partitioned; the method comprises the following steps: acquiring Z sub-regions and brain control instructions included in each sub-region, distributing frequency-phase information of U-class brain electrical stimulation to each brain control instruction, acquiring brain electrical stimulation classification based on a first corresponding relation on the basis of brain electrical signal characteristics induced by a flicker rule of brain electrical stimulation corresponding to a certain brain control instruction when a user watches the brain control instruction, and determining a sub-region based on the user sight drop point coordinate, and outputting a brain control instruction in combination with the brain electrical stimulation classification and the determined sub-region. According to the invention, the brain-computer interaction work efficiency can be improved.

The invention discloses a lithium ore exploration system and method based on data analysis, and relates to the technical field of lithium ore exploration. In the operation process of the system, data from different sources are collected, geological abnormal parameters, lithologic density fluctuation parameters and gravity gradient parameters are obtained, the preprocessed data are analyzed, and a lithium ore exploration result is obtained; the method comprises the following steps: extracting geological features related to mineral resources, carrying out pattern recognition by using a deep learning technology, extracting key features from geological data, obtaining lithologic density Sgeo, rock magnetic parameters Wrock and rock resistivity Rgrad, and generating a three-dimensional geological model of a mining area through an AI algorithm based on the extracted features. The AI algorithm comprises regression analysis and a convolutional neural network (CNN) to process spatial data and generate three-dimensional visualization of a geologic structure, historical mining area distribution data is combined, the system uses a prediction algorithm random forest or a support vector machine (SVM) to predict lithium ore distribution, survey feedback is generated according to a prediction result, and a survey scheme is dynamically adjusted.

The invention discloses an enterprise data intelligent analysis system based on deep learning, which relates to the technical field of data analysis, and realizes real-time acquisition and transmission of data through a data acquisition and processing module, division of department areas and integration of a staff performance system, a training system and an attendance system through multiple APIs (Application Program Interface); the effective data analysis module analyzes performance data and calculates a performance index Jxzs, the loss prediction analysis module predicts employee loss in combination with historical demission data and a performance evaluation result, the recruitment demand analysis module predicts recruitment demands in advance and calculates a recruitment demand index Zpzs, and the learning model optimization module optimizes a model and updates a data set. The real-time performance and the accuracy of the model are ensured; the system evaluates each index by presetting a plurality of thresholds, generates a corresponding evaluation result and proposes improvement measures, thereby effectively reducing the employee loss risk, and improving the recruitment efficiency and employee satisfaction.

The invention discloses a Non-IID federated learning method based on dynamic knowledge distillation and dynamic regularization. The method comprises the steps that after a local model of a client is trained, distribution Ps of local data tags is output; uploading local model parameters to a server; the server gathers a group of client model parameters obtained by random sampling, and outputs global model parameters for the next round of use; the client downloads global model parameters, loads the global model parameters to a local model and outputs global data label distribution Pt; performing dynamic knowledge distillation at the client, integrating Ps, Pt and a dynamic function d (r), and calculating a local loss function of the client; and updating the local model of the client through back propagation. According to the method, the convergence speed and accuracy of the model can be remarkably improved under the condition of non-independent and identically-distributed Non-IID client data.

The invention provides a multifunctional self-assembly peptide recognition method and system, and relates to the technical field of biological peptide recognition. The method comprises the following steps: firstly, obtaining peptide sequences of a self-assembled peptide and a plurality of biological functional peptides, and inputting the peptide sequences into an embedded layer of a polypeptide function prediction model for 21-dimensional vectorization; and learning combination modes and sequence features of amino acids through an amino acid association feature extraction layer of the LSTM network. Then, acquiring direct contact information among different amino acids on the basis of a fine-grained feature learning layer of a multi-head self-attention mechanism; and finally, designing a Transform multi-task classifier which comprises five sub-task classifiers which are used for respectively predicting the self-assembly function, the anti-cancer function, the anti-inflammatory function, the antibacterial function and the antioxidant function. According to the method, a multi-layer structure is adopted to carry out layer-by-layer deep learning and optimization on polypeptide sequence features, efficient recognition and classification of multifunctional biological peptides are achieved, and finally the model performance is comprehensively evaluated through the accuracy rate, the precision rate, the recall rate and the F1 value.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a cluster unmanned aerial vehicle task dual planning method, which comprises the following steps of: constructing a layered task planning framework comprising a global planning subsystem and a local planning subsystem, and enabling the global planning subsystem to execute task subarea division and task distribution; based on the local planning subsystem, task refinement and path planning are carried out on the unmanned aerial vehicle in each task subarea divided by the global planning subsystem, and information is fed back to the global planning subsystem; based on feedback information, through a global planning subsystem, a task planning strategy of the cluster unmanned aerial vehicle is adjusted again, a layered task planning architecture is adopted to get rid of excessive dependence on a single central control node, a complex task can be decomposed into a plurality of relatively independent sub-tasks, the task planning complexity is reduced, the task calculation amount and planning time are reduced, and the task planning efficiency is improved. The resource of each unmanned aerial vehicle is fully utilized, and the resource utilization rate, the information processing capability and the decision scientificity of the cluster unmanned aerial vehicles are improved.

The invention discloses a data synchronization method, device and equipment and a medium, the method is applied to any target service node in a service cluster, and the method comprises the following steps: when the target service node is used as a candidate node, sending a main node election request to each survival service node in the service cluster, when election success responses fed back by all the survival service nodes are received, the candidate node is converted into the main node; when the target service node is used as a main node, sending a data acquisition request to each survival service node in the service cluster to acquire a consensus information version number fed back by each survival service node; and determining a first service node to which the latest consensus information belongs according to each consensus information version number, acquiring snapshot data of the latest consensus information from the first service node, and synchronizing the snapshot data to all survival service nodes in the service cluster. According to the scheme, the consistency of the consensus information in the service cluster can be accurately ensured on the premise of effectively ensuring the time delay performance of the system.

The invention discloses a learning effect evaluation system and method for artificial intelligence image generation training courses, and relates to the technical field of computers. In order to solve the technical problem that in the prior art, a learning management system cannot feed back learning conditions of students in time, the technical scheme provided by the invention is that the system comprises a plurality of modules used for collecting and analyzing data in the learning process of the students, and comprises a student single course homework quality evaluation module, the quality evaluation module is used for performing quality evaluation on a single homework of a student in an artificial intelligence image generation course to obtain a quality score; the overall learning ability evaluation module is used for carrying out comprehensive learning state analysis on the quality scores and the homework process logs of the multiple homeworks of the students to obtain overall learning ability scores of the students; and the visual feedback module is used for generating learning diaries of the students according to the change of the homework quality and the learning ability of the students. The method is suitable for being applied to learning effect evaluation work of artificial intelligence image generation courses.

The invention relates to the field of engineering investigation, and provides a graphical geological logging method for a probe groove in order to realize the integrity and continuity of a two-dimensional logging result after three-dimensional restoration: determining the spatial position of the axis of the probe groove in a three-dimensional engineering overall coordinate system, and generating a three-dimensional spatial form of the probe groove based on basic parameters of the probe groove; the probe groove is divided into a left side wall, a bottom plate and a right side wall to form a probe groove two-dimensional expansion view; establishing a local coordinate system on the two-dimensional expansion graph by taking a starting point or a stopping point of the axis of the probe groove as a coordinate origin, and drawing a two-dimensional coordinate grid on the two-dimensional expansion graph of the probe groove based on the local coordinate system; drawing a geologic body outcrop line and recording geological attribute information on the two-dimensional exploring groove expansion graph on which the two-dimensional coordinate grid is drawn according to data obtained after field excavation exploring groove; and mapping the outcrop line to the three-dimensional space model according to the mapping relation between the local coordinate system and the three-dimensional engineering overall coordinate system. By adopting the mode, the integrity and continuity of the two-dimensional catalog result after three-dimensional reduction are realized.

The invention provides a system and a method for switching main and standby user plane network elements of a core network of a high-throughput satellite system. The system and the method are used for solving the problem of split brain between main and standby equipment and the problem that the context of a user plane and the control plane network elements are inconsistent after the standby equipment is converted into the main equipment. Comprising an MME (Mobility Management Entity), a Redis database, an XGW (Extensible Gateway) and a watchdog thereof, the MME carries main and standby state attributes through a heartbeat message to specify the main and standby states of XGW equipment, and the XGW receives the indication of the MME to set and switch the main and standby states. And the MME stores a user control plane and a user plane context in a redis library, after the XGW standby is switched to the master, the MME performs access information user plane context recovery on the XGW standby, the master XGW reconstructs the user plane context, and the user service is recovered to be normal. When the MME is started for the first time, all the XGWs are in a standby state, multiple threads initiate heartbeat to all the XGWs at the same time, the XGWs which receive heartbeat responses first by the MME conduct standby-to-main conversion, and the other states are standby.

The invention discloses a rice early recognition method and device based on remote sensing data and a time sequence model. The method comprises the following steps: S1, acquiring multi-source remote sensing data and auxiliary data; s2, preprocessing the multi-source remote sensing data and the auxiliary data; s3, constructing a sample set based on the preprocessed multi-source remote sensing data and auxiliary data; s4, constructing a DualMama model, and training the DualMama model on the basis of the sample set; and S5, inputting the multi-source remote sensing data and the auxiliary data obtained in the step S1 into the trained DualMama model to obtain a rice early recognition distribution diagram. According to the method, through the proposed DualMama model, the utilization efficiency of multi-source remote sensing data is remarkably improved, the problem of inconsistent feature dimensions is solved, the early recognition precision of the rice is greatly improved under the complex hill terrain, the F1 score of the rice category can reach 90% within two months before the rice growth period, and accurate early recognition of the rice is achieved.

The invention relates to an enterprise credit evaluation system and method, the system comprises a data acquisition module, a feature engineering module and an evaluation module which are connected in sequence, and the method comprises the steps: obtaining the local data of an enterprise and the data of a regional big data center, and converting the data of the regional big data center into the data of an enterprise big data center; carrying out entropy calculation, IV value calculation and correlation coefficient analysis on the obtained data to determine in-mold multi-dimensional information, carrying out feature screening, screening out important features from the in-mold multi-dimensional information, and constructing a feature set; training an XGBoost model by using the feature set to obtain an enterprise credit evaluation model; and inputting the in-mold multi-dimensional information and the unified social credit code information of the to-be-assessed enterprise into the enterprise credit assessment model, and outputting a credit score and a suggested credit line corresponding to the to-be-assessed enterprise. Compared with the prior art, the method has the advantages that the problems of dispersion and inconsistency of multi-channel data sources in the prior art can be solved, timely updating of the data is ensured, and the accuracy of enterprise credit assessment results is improved.

The invention discloses a millimeter wave radar and monocular camera fused information acquisition device and method, and relates to the technical field of target detection. The device comprises a millimeter wave radar, a monocular camera and an adjusting bracket, wherein the adjusting bracket can adjust the positions and angles of the radar and the camera. And the data fusion module performs time synchronization and space alignment on the radar information and the image information, and obtains a target recognition result through a preset target recognition model. The acquisition method comprises the steps of timestamp matching, time compensation, joint calibration, coordinate transformation, image distortion correction and the like. Further, radar physical features are extracted by using a CFAR algorithm, image features are extracted by using a deep learning model, and effective features are obtained through PCA dimension reduction. And finally, an accurate target recognition result is obtained through feature splicing and optimization by using a support vector machine (SVM). According to the method, efficient fusion and target identification of millimeter wave radar and monocular camera information are realized, and the method has a wide application prospect.

The embodiment of the invention provides an intelligent rail transit data management and analysis method and system. The method comprises the following steps: receiving and importing professional rail transit data to a data lake; performing full-process quality monitoring on the professional rail transit data and generating a data quality index; calculating a rail transit index based on the data quality index; providing a user interaction interface to access, analyze and display the rail transit indexes; wherein in the process of executing the step of monitoring the quality of the whole process and generating the data quality index and the step of calculating the rail transit index, task coordination and resource scheduling are continuously carried out, so that automatic operation according to a preset strategy and priority is ensured. According to the embodiment of the invention, the real-time performance and quality of data processing and the expansibility of the system are remarkably improved, the fusion of cross-professional data and the dynamic allocation of computing resources are enhanced, the whole-process data management is realized, the overall operation efficiency and service quality are improved, and the travel experience of passengers is improved.

The invention discloses a method and a system for checking the effectiveness of a voltage identifier of a layout line and electronic equipment thereof, and belongs to the technical field of layout data processing. Comprising the steps of obtaining line voltage of a circuit, including a line name and a voltage value; obtaining a layout line voltage identifier which comprises a line name and a voltage value; and comparing whether the layout line voltage identifier is consistent with the line voltage of the circuit, and generating a voltage identifier validity report of the layout line. The correctness and effectiveness of the layout line voltage identification value are verified through the line voltage value under the actual working condition of the circuit, the situation that the line voltage is unreasonable due to the fact that the identification layer or the voltage value is manually and mistakenly used is avoided, and therefore the interval requirement rule checking result caused by the line voltage difference is prevented from being influenced; and the accuracy and the reliability of the layout line voltage validity check are improved.

The invention discloses a data analysis system for predicting respiratory diseases of children, and relates to the technical field of health data analysis, and a data acquisition platform comprises a data cleaning model used for acquiring heterogeneous data of the respiratory diseases of children; the data cleaning model identifies the data inconsistency of the child respiratory diseases, analyzes unstructured clinical patient feedback, and extracts semantic information; the prediction model construction module is used for constructing a hybrid prediction model based on the medical environment data and the semantic information; and the scheme making module is used for inputting the respiratory disease condition of the child into the prediction model building module, analyzing the influence of different climate changes on the air quality and customizing a prevention and treatment scheme. According to the invention, real-time early warning of air quality is realized, parents and medical institutions are helped to take preventive measures in time, the onset risk of respiratory diseases of children is reduced, the intelligent level of prediction is improved, and a solid technical support is provided for personalized health management.

The utility model discloses a battery management circuit of an automobile energy storage power supply, which comprises a main control module, a data acquisition module, a communication module, a protection module and an equalization module, the main control module is used for receiving, processing and sending instructions, and the data acquisition module is used for acquiring voltage, current and temperature parameters of a battery pack in real time; the communication module is used for information interaction between the battery management circuit and other vehicle systems, the protection module is used for over-charge, over-discharge, over-temperature and short-circuit protection of the circuit, the equalization module is used for realizing voltage equalization between single batteries in a battery pack, and the master control module is connected with the data acquisition module, the communication module and the protection module through data lines. Data transmission and control instruction issuing are achieved, and the communication module is connected with other vehicle systems through a communication bus in a vehicle to achieve information sharing and interaction.

The invention discloses a management and control system for traditional Chinese medicine decoction piece production based on intelligent Internet of Things, and relates to the technical field of Internet of Things. During operation of the system, a humidity sensitivity analysis module, a regional humidity detection module, a subarea control module, a humidity adjustment feedback module, a data processing and analysis module and an effect evaluation and iteration module are integrated; the humidity management in the traditional Chinese medicine decoction piece production process is comprehensively optimized, the humidity in a production area can be dynamically and accurately controlled, intelligent classification and personalized management are carried out on different humidity-sensitive decoction pieces, and therefore the humidity in each area is kept within the optimal range all the time, and the production efficiency of the traditional Chinese medicine decoction pieces is improved. The real-time feedback and regulation mechanism not only improves the precision of humidity control, but also significantly reduces the quality problem of decoction pieces caused by unstable humidity. The problems of hysteresis and inconsistency can be effectively solved, the negative influence of humidity fluctuation on the quality of the traditional Chinese medicine decoction pieces is prevented, the production loss is reduced, and the production efficiency is improved.

The application discloses a kind of man-machine collaborative multi-modal data intelligent labeling and quality checking method, it is related to man-machine collaborative technical field.The method first obtains at least containing two modalities of original data of image, text, audio, generates multi-modal joint data sample after pre-processing and modal alignment;Input pre-trained multi-modal AI pre-labeling model generates pre-labeling result with confidence;Through cross-modal consistency check calculation consistency score, sample is divided into high, medium, low confidence three categories;High confidence sample is directly adopted and enters the sampling pool, and medium and low confidence sample is based on labeling personnel ability portrait and AI model dynamic capability evaluation and carries out intelligent task distribution.The application effectively solves the problem that efficiency and quality are difficult to balance in multi-modal data labeling, significantly improves labeling accuracy and automation rate, reduces labeling cost, and can meet the construction demand of large-scale high-quality multi-modal data set.

The invention provides a photovoltaic power station multi-data-source electric quantity correction module and correction method. The correction module comprises a multi-source data acquisition module, a priority ranking module, a slicing module and a data source selection module. The multi-source data acquisition module, the priority ranking module, the slicing module and the data source selection module are connected in sequence. According to the method, by introducing a logic isolation mechanism of a time window and combining the characteristics of multiple data sources, time-sharing fusion calculation for different configurations and data sources is realized, the problem of data inconsistency can be effectively solved, and the accuracy of electric quantity calculation is remarkably improved.

The present application proposes a high-dimensional data processing method based on a deep manifold transformation network, the method comprising: obtaining an input space; reducing the dimension of the input space to a latent space through a first nonlinear transformation, and reducing the dimension of the latent space to an embedded space through a second nonlinear transformation; applying a bidirectional divergence loss between the input space and the latent space, and / or applying a bidirectional divergence loss between the latent space and the embedded space, and / or applying a bidirectional divergence loss between the input space and the embedded space, so that the first nonlinear transformation and the second nonlinear transformation maintain the same structure; clustering the data of the input space in the latent space, and visualizing the reduced-dimensional data in the embedded space. Through the present invention, applying a bidirectional divergence loss between any two layers ensures the local smoothness of the network, prevents the destruction of the geometric or topological structure in the original data caused by dimensionality reduction, causes information loss, and deteriorates the effect of the final clustering.

One or more embodiments of the present specification provide an image generation method, a storage medium and a program product, the method comprising: inputting a local image and a text into an image generation model, the image generation model being obtained by training a constructed neural network model by using a training sample set, each training sample in the training sample set comprises a global reference image, a local reference image and a reference text, the local reference image is an intercepted part in the global reference image, and the content of the reference text is associated with the global reference image; and generating an extended image corresponding to the local image and the text based on the image generation model, wherein the size of the extended image is greater than that of the local image. Through the specific training mechanism for the image generation model, the image content is effectively enriched while the integral structural integrity and visual coherence of the image are ensured.

The present invention proposes a multi-core processor and a method for centralized cache consistency management thereof, which relates to the field of data processing technology. Data exchange and information access between various components are performed through the connection of a central exchange block, a system main memory and a core module; cache information of each core is recorded through the core module; data read application information is obtained, core data holding information is obtained according to the data read application information, and a core that sends a data read request is converted according to the core data holding information; core update cache demand information is obtained, a cache management unit updates a memory data block, and resets a cache management table. Through a unique central exchange block and high-speed memory design, etc., a more flexible and efficient cache consistency solution is provided, which is suitable for more multi-core processor application scenarios.

The invention provides a signature determination verification method, device and system of an operating system, equipment and a medium. The method comprises: receiving a first signature and first operating system data sent by an operation system, the first signature being obtained by the operation system according to first verification information, the first verification information being obtained by calculating the first operating system data according to a preset algorithm, and the first operating system data being operating system data of a test SIM card which is detected to be qualified; verifying the first operating system data according to the first signature; if the verification result is successful, obtaining a second signature according to the first verification information and the first signature; and determining the signature of the operating system of the SIM card to be made according to the second signature. According to the method provided by the invention, the problem that the operating system in the tested SIM card is inconsistent with the operating system in the actually produced SIM card is solved, and the security of the operating system of the SIM card is improved.

This utility model relates to the field of wall component technology, specifically a connection structure between a suspended colored steel plate rainwater hopper and a wall. The structure includes a wall body, with multiple sets of wall purlins on the outer surface of the wall body. Rainwater hopper connectors for support are mounted on the outer periphery of the wall purlins via self-tapping screws. A specially designed colored steel plate rainwater hopper for drainage is mounted on the end of the rainwater hopper connector away from the wall body via a self-tapping screw. In the use of this connection structure, rainwater from the roof is guided by a rainwater bend and discharged through the specially designed colored steel plate rainwater hopper. The outlet of the rainwater bend is located inside the specially designed colored steel plate rainwater hopper, allowing rainwater to naturally flow into the hopper for discharge, effectively avoiding the "teapot effect" and solving the problem of incompatibility between the rainwater hopper / pipe and the exterior wall.

The invention discloses an AGV scheduling method, scheduling equipment, AGV equipment and a storage medium, and the method comprises the steps: a scheduling equipment side creates a map information model based on the topological information of a to-be-scheduled region; under the condition that vehicle body registration messages sent by the multiple AGV devices are received and registration succeeds, a map information model is sent to the multiple AGV devices, and the map information model is used for indicating that the multiple AGV devices have unified attitude information in the to-be-dispatched area. The AGV equipment side receives the map information model sent by the scheduling equipment under the condition that the AGV equipment successfully registers to the scheduling equipment; and carrying out AGV scheduling in the to-be-scheduled area based on the map information model. Therefore, for the multiple brands of AGVs operating in the same area, the unified map information model is created, so that the hybrid scheduling of the multiple brands of AGVs can be compatible, and the operation efficiency of the AGVs is improved.