115results about How to "Strong data support" patented technology

The invention discloses a textile product defect detection method and system, and the method comprises the steps: collecting a textile surface image, and carrying out the preprocessing; constructing a feature extraction model, and extracting multi-modal features of the textile fabric surface defects; analyzing the extracted features, and classifying defect types; positioning defect positions, and marking defect areas and types; and outputting a defect detection report, and optimizing model parameters according to a detection result. Through multi-modal feature extraction and a feedback optimization mechanism, the classification accuracy and the positioning precision of textile fabric defect detection are remarkably improved, and particularly, the method has outstanding performance in detection of complex and low-contrast defects. According to the invention, the detection efficiency is greatly improved, false detection and missing detection are reduced, and an efficient and intelligent solution is provided for quality control of the textile industry.

The invention discloses an intelligent travel text system based on python deep learning, and relates to the technical field of python deep learning, and the system comprises the following components: a data collection module which is used for automatically collecting comment data about a specific travel destination from various channels of various travel websites, social media platforms and online travel forums, according to the method, massive comments and evaluation data about a specific tourism destination can be automatically collected from multiple channels, the accuracy and consistency of analysis data are ensured through fine data preprocessing, fine adjustment is performed by applying an advanced deep learning sentiment analysis algorithm in combination with a specific corpus in the tourism field, and the accuracy and consistency of data analysis are ensured. The system can accurately judge the emotional tendency of tourists and deeply mine key opinions and suggestions in comments, and the deep emotional mining ability not only helps tourism management departments and scenic spots to quickly understand real feedback of the tourists, but also can accurately identify satisfaction distribution and main concerns of the tourists on services.

The invention relates to the technical field of CIM (common information model) platforms, in particular to a multi-element and multi-scale data management method and system based on a CIM basic platform, and relates to data acquisition, access and classification in a service interface, incremental acquisition and offline copying mode. Data needs to be translated to conform to spatial, elevation and temporal references of a CIM platform, unify geocoding and spatial expression, and semantic-based place name address association. And the BIM and the CIM are fused through a knowledge graph. And performing three-dimensional data grading processing, and constructing CIM1 to CIM5 grade data. Government affair data are subjected to graph falling through a unified address library. Overall data governance covers data aggregation, classified governance, two-dimensional and three-dimensional fusion, BIM and CIM fusion technology and government affair data association, classification and level-to-level management of data is realized, different data combination application is supported, and CIM business application and government affair process auxiliary approval and decision making are assisted.

The invention provides a work plan and task management system, which belongs to the technical field of work plan tasks and comprises a portal layer, an application program interface gateway layer, a business service layer, a public service layer and a data layer. And the application program interface gateway layer realizes intelligent management of the interface through a self-adaptive division module, wherein the intelligent management comprises dependency analysis, granularity calculation, aggregation optimization and recombination execution. The business service layer focuses on work plans and task management, and realizes collaboration through flexible interface interaction. The public service layer provides basic support for user authentication, log recording, message queue, service registration, configuration management and the like. And the data layer adopts a diversified storage strategy, integrates a relational database, a cache database, a time sequence database and distributed file storage, and meets data processing requirements of different scenes. The problem that in the prior art, interface definition and calling relations between micro-services are difficult to manage in a self-adaptive mode is solved.

The invention provides a digital twin city construction and operation optimization method based on cloud computing and a big data technology. The method comprises the following steps: optimizing layout of various sensor devices through a particle swarm optimization algorithm and an adaptive sampling algorithm, and adjusting acquisition frequency; establishing a distributed database, and realizing uniform distribution and efficient access of data in the distributed database through a load balancing algorithm; mining a time rule of urban operation by using a time sequence model, and discovering association between different data; a city model is constructed by using a city three-dimensional live-action fusion modeling method which takes unmanned aerial vehicle oblique photography as a main part and takes ground laser radar as an auxiliary part, different areas in a city are identified by applying a machine learning method, and a refined model of each field of the city is constructed; real-time simulation of a city operation state is realized through virtual reality and augmented reality technologies; the system is continuously optimized by using an online learning algorithm, so that the system continuously learns and adapts to a new data mode in the operation process, and the construction and operation optimization capability of the system is improved.

The invention relates to the technical field of optical detection and image processing, in particular to a mobile phone lens window glass defect detection method. Comprising the following steps: acquiring a high-resolution image of a window glass sample by imaging equipment by adopting a multispectral imaging technology; inputting the acquired image into a pre-trained deep learning model for preprocessing; determining whether defects exist or not according to an output result of the deep learning model, marking the positions and types of the defects, and generating a detection report containing defect geometric parameters; if the defect exists, the severity of the defect is judged according to preset standards and geometric parameters of the defect, and the standards comprise the size, the shape, the depth and the position of the defect; and the detection result and the image data are stored in a cloud database to support subsequent quality analysis and process improvement. By combining the multispectral imaging technology, the deep learning model and the fuzzy logic system, the quality detection level of the window glass is comprehensively improved.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention belongs to the technical field of building energy conservation and carbon reduction, discloses a building energy conservation and carbon reduction method, device and equipment and a storage medium, and constructs a high-precision building BIM model. A digital twinborn model of a Xian mansion is constructed by applying a BIM technology, and the model not only depicts the physical structure of the building in detail, but also simulates key operation details such as personnel flow and equipment operation. By strategically simplifying the model, it is ensured that the model is efficient and accurate. The comparative analysis of the corrected model and actually measured data shows that the prediction error of the model is only 0.10%, the extremely high consistency of the prediction result and the actual situation is highlighted, the high accuracy of model correction is verified, and the high standard requirement of in-depth research is met; according to the invention, balance optimization of carbon emission minimization and comfort maximization is realized. By intelligently adjusting the operation strategy of the air conditioning equipment, the method obviously reduces the carbon emission of the building while ensuring the living comfort.

The invention relates to a three-dimensional instance segmentation method in a paddy field high-density shielding scene in combination with a multi-modal fusion network. The method comprises the steps of collecting a multi-view RGB image and three-dimensional point cloud data of a paddy field for preprocessing; inputting the multi-view RGB image in the multi-source data set into the image segmentation model to generate an image instance segmentation result; matching the image instance segmentation result with the three-dimensional point cloud data in the multi-source data set; and inputting the point cloud data containing the multi-view RGB image information into the improved three-dimensional instance segmentation network model, and outputting a three-dimensional instance segmentation result. According to the method, accurate segmentation in a high-density shielding scene is realized, and the segmentation accuracy is remarkably improved; effective fusion of multi-modal data is realized, the robustness and precision of instance segmentation are effectively improved, and the method is especially suitable for a fine-grained segmentation task in a dense vegetation scene. The calculation complexity is reduced, the processing efficiency is improved, and the long-distance dependency relationship in the point cloud can be captured while the efficient calculation performance is kept.

The invention relates to a database table generation method based on a large model, belongs to the technical field of database table generation, and solves the problems that a database table generation method in the prior art is tedious in creation, time-consuming and high in misoperation risk. Comprising the following steps: creating a dialogue based on a database table, and generating the database table by a large model to create an SQL statement; creating SQL statements for the database table, analyzing the SQL statements, updating a database logic table, and verifying and modifying the updated database logic table to obtain a modified database logic table; and based on the modified database logic table, updating the current database table to create an SQL statement, and further creating a database table entity. The automatic creation of the database table is realized.

The invention discloses a new energy power prediction correction method and system based on a mixed channel cloud model, and the method comprises the steps: obtaining historical key data, including a predicted power value and an actual power value, of new energy power prediction from different data channels, and obtaining the predicted power value and the actual power value after normalization processing through normalization processing; constructing a cloud function by using a triangular membership function based on the predicted power value and the actual power value after normalization processing; and constructing a mixed channel cloud model, generating a correction weight for the power prediction value of each time point in the target time period by a cloud function, performing automatic dynamic component optimization on the power prediction value of each time point, correcting the power prediction value of the corresponding time point, and obtaining a final new energy prediction power value. According to the method, the power prediction value can be dynamically adjusted and optimized to correct the error in prediction, and the precision, reliability and stability of the prediction result of new energy such as wind energy and solar energy can be remarkably improved.

The invention discloses a vehicle environment data uploading method and device, equipment and a storage medium, and relates to the technical field of data processing, and the vehicle environment data uploading method comprises the steps: obtaining sensor data and an environment change flag bit; when the change frame number of the environment change flag bit reaches a preset frame number threshold value, a yaw angle change value and a pose change value are obtained according to the sensor data; when the system performance, the yaw angle change value and the pose change value all meet preset conditions, caching the sensor data; and when the sensor data reaches a preset data threshold value, uploading the sensor data. According to the method, efficient acquisition and uploading of the environment data are realized through judgment of the environment change mark monitored by the continuous frame vehicle in real time and modification of the key frame screening threshold value by the system resource information. The data acquisition efficiency and the system resource use efficiency are improved, the acquired data transmission and storage pressure is reduced, and the resource processing consumption of the cloud server is reduced.

The invention discloses a truth value mining method for a multi-source unstructured text service in a meta-universe crowdsourcing environment, and provides a solution of three stages, namely a semantic preprocessing stage, a high-dimensional content vector representation of a crowdsourcing answer is constructed, and clustering is carried out; in the truth value optimization and feature generation stage, the optimized truth value mining model is used for evaluating the work quality of crowdsourcing workers, and features are dynamically generated. And a task clustering and class cluster mapping stage: clustering the tasks with generated features, accurately estimating the real class of each task, and finally determining a true value. According to the method, the accuracy and efficiency of true value mining are improved, and the applicability of the method in a complex unstructured data environment is expanded. By integrating the multi-source sensing data in the meta-universe crowdsourcing environment, the intelligent service meeting the user requirements can be accurately pushed, and the service quality and the user experience of the meta-universe are improved.

The invention provides an electric power construction risk intelligent early warning method and device based on multi-dimensional information fusion, and relates to the technical field of electric power construction risk intelligent early warning. Monitoring environment change information, equipment operation state information and safety distance information of the power construction site; determining a first early warning result based on the environment change information and a first judgment rule; determining a second early warning result based on the equipment operation state information and a second judgment rule; determining a third early warning result based on the safety distance information and a third judgment rule; and based on the first early warning result, the second early warning result and the third early warning result, determining a power construction risk level. Therefore, potential safety hazards are found by monitoring the environment change, the equipment operation state and the safety distance in real time, so that the occurrence of construction accidents is reduced. Therefore, the life safety of constructors can be protected, and the property loss can be reduced.

The invention relates to the technical field of invisible orthodontic appliance wearing monitoring and adjusting systems, in particular to an invisible orthodontic appliance wearing monitoring and adjusting system and method based on computer vision, and the system comprises a cloud storage module which is used for storing initial oral data and re-consultation oral data of a patient; the cloud processing module is used for analyzing the oral cavity picture uploaded by the patient and executing analysis software; the client module is used for managing an electronic medical record of a patient, providing treatment information, obtaining an analysis result and generating personalized feedback; according to the invention, a computer vision algorithm is combined with oral medical knowledge, so that not only can the position change of teeth be accurately identified and tracked, but also the judgment ability of the device can be continuously optimized through a deep learning algorithm. Meanwhile, the cloud computing architecture adopted by the invention enables the system to process mass data, and powerful data support is provided for formulating a personalized treatment scheme.

The invention discloses a power generation equipment maintenance knowledge question-answering system and method based on a multi-modal large model. The system comprises an equipment question-answering robot module, a data lake module, a large data model module and the like. A data lake knowledge base is constructed; receiving user demand questions for semantic analysis and classification, and determining data types in the demand questions; according to the data type, selecting an appropriate trained RAG large model to carry out comprehensive processing and reasoning, and generating a question answer; and presenting the question answer to the user as a reply. According to the invention, data intercommunication between equipment knowledge questions and answers and maintenance questions and answers is realized through the data sharing module, so that the accuracy of fault diagnosis and the maintenance efficiency are improved; the application of the multi-modal large model and the data management module provides powerful data support for equipment maintenance; and through the real-time monitoring function of the fault alarm module, the equipment fault can be found in time, the downtime is reduced, and the production efficiency is improved.

The invention discloses a double-model-driven virtual power plant power resource disposal method, which comprises the following steps of: decoupling energy resource data from a specific disposal scheme, judging the current real-time scheduling capability by utilizing an energy resource model, inputting the real-time scheduling capability and real-time operation state data into an energy disposal model, and performing real-time scheduling on the real-time scheduling capability and the real-time operation state data; and outputting a real-time disposal scheme of electric power energy scheduling. According to the method, the black box problem of a single model is avoided, the scheduling capability change caused by the change of the energy resource data can be clearly understood, the energy disposal model can be judged to ensure the feasibility of the disposal scheme, meanwhile, the resource scheduling capability judgment result can be visually verified, and the energy resource model can be independently optimized even if errors exist.

The invention provides a language resource library system based on data security control, and the system comprises an acquisition module which collects language resources in a manner of manual input and crawler program directional capture, obtains a language data set, and stores the language data set in a database; the processing module develops a software program according to a preset metadata specification, and processes and manually marks the language data set to obtain a standard data set; the management module is used for performing data management, user management and log management in combination with the data access control model and the network security monitoring model; the presentation module is used for providing data retrieval and statistical analysis functions according to the data type and carrying out visualized display of the data; and the product library module is used for developing a language resource platform. By establishing a perfect security mechanism, unauthorized access and data leakage can be effectively prevented, the security of language resources in cross-border transmission and use is improved, and efficient and stable operation of the system is ensured.

The invention relates to a control method, device and equipment for a power transmission system and a storage medium, and the method comprises the steps: obtaining sensor data, external environment data and historical operation and maintenance data in the power transmission system, inputting the sensor data and the external environment data into a monitoring model, and carrying out the monitoring of a working state, thereby obtaining a monitoring result, and when the monitoring result shows that the power transmission system is abnormal, inputting the historical operation and maintenance data into the resource scheduling model for resource scheduling. According to the method, due to the fact that the working state of the power transmission system is analyzed in combination with the real-time monitoring data, the historical operation and maintenance data and other multi-dimensional data, data fusion and efficient processing are achieved, the accuracy and integrity of the data are improved to a certain degree, and powerful data support is provided for subsequent decision support.

The invention relates to a subway passenger flow prediction method and device based on a multi-scale space-time diagram attention network. The method comprises the steps of collecting passenger flow data of subway OD pairs, subway line network structure data and land use type data; constructing an OD-level urban rail transit knowledge graph based on the collected data, and calculating node features of the knowledge graph, including calculating historical passenger flow features, calculating land use information, calculating accessibility of OD pairs and calculating correlation of the OD pairs; inputting node features of the knowledge graph into a multi-scale space-time diagram attention network, performing diagram filtering on the node features, and updating the node features of the knowledge graph in combination with a result of a space-time attention mechanism; and inputting the updated node features into the multi-scale LSTM model, and outputting a multi-scale prediction result of the subway passenger flow. Compared with the prior art, the method has the advantages of being more accurate in prediction, beneficial to intelligent scheduling and the like.

The invention relates to the technical field of early warning platform construction, and discloses a port infrastructure marine environment disaster early warning platform construction method and system, and the method comprises the steps: obtaining the current time point, the current position and the current speed of a typhoon, and the air density and the gravitational acceleration of the height of the typhoon; constructing a motion equation, and calculating the speed of the typhoon at the next time point; calculating an adjacent value between the current time point and the next time point of the typhoon; updating the position and speed between the current time point and the next time point when the typhoon occurs to obtain an updated position, an updated speed and a typhoon path; calculating the typhoon wave height according to the typhoon path and the typhoon intensity; by updating the typhoon path in real time, the actual movement condition of the typhoon is reflected more accurately, subtle changes of the typhoon path can be captured better, real-time monitoring is achieved, and powerful data support is provided for construction of an early warning platform.

The invention relates to the technical field of data analysis, in particular to an intelligent assessment system and method for safe operation of a silt dam, and the system comprises a data collection module, a data analysis module, an intelligent assessment module and an assessment analysis module. The data analysis module draws a speed time domain graph to determine a historical average speed and a unit speed change frequency in a unit period, and the intelligent evaluation module determines an operation characterization trend to determine an operation characterization state of the silt dam in combination with an upstream and downstream water level difference. Whether safety early warning information is sent out or not is determined according to the operation characterization trend or the operation characterization state, and the assessment analysis module determines a dangerous operation reason according to the judgment result of the dangerous operation state and the upstream and downstream water level difference. Through the functions of real-time monitoring, accurate evaluation, quick response, intelligent analysis and the like, the safety management level of the silt dam is remarkably improved, the safety risk and the maintenance cost are reduced, and powerful technical support and decision basis are provided for inspectors and managers.

The invention discloses a high-performance real-time market record information auditing and inputting system. The system comprises a data acquisition module; a data preprocessing module; an auditing rule engine module; the data entering module is used for sending the audited data into the data entering module, and the data entering module is responsible for storing the audited data into a database; the system comprises a user interaction interface and a database, the user interaction interface provides an operation platform for a system user and comprises a data display unit, an auditing result feedback unit and a manual adjustment input data unit, and the database is used for storing all audited market record information. Through the highly automatic and intelligent design, the data processing speed and quality are improved, the data security is enhanced, the user experience is improved, and powerful data support is provided for enterprises so as to adapt to constantly changing market environments and business requirements.

The invention provides an electric spindle thermal error advanced prediction method based on a deep learning algorithm, and the method comprises the steps: collecting the surface temperature of an electric spindle and the displacement data of the tail end of the electric spindle through arranging a plurality of temperature sensors and displacement sensors on the surface of the electric spindle; and training a thermal error advanced prediction network composed of the time domain convolutional network, the gating circulation network and the full-connection network to realize thermal error advanced prediction of the electric spindle. The advanced quantity of the thermal error of the motorized spindle is predicted by adopting the thermal error advanced prediction network, compensation can be carried out before the error occurs, the machining precision and the stability of the motorized spindle are improved, meanwhile, the thermal error advanced prediction network adopts the design of a multi-path parallel neural network and a full-connection network, the adaptability and robustness of the model are enhanced, and the accuracy of the model is improved. And a flexible solution is provided for motorized spindles of different types and specifications.

The invention discloses a virtual simulation data intelligent generation and enhancement method based on deep learning. The method comprises the following steps: step 1, collecting and converging multi-source heterogeneous simulation data; 2, simulation data preprocessing and feature extraction; step 3, constructing and configuring a depth generation model; step 4, iterative training and optimization of the depth generation model; step 5, intelligently generating and synthesizing new simulation data; step 6, generating data quality verification and enhancement feedback; step 7, carrying out controllability adjustment and directional enhancement on the generated data; and 8, model online learning and data generation closed-loop optimization are carried out. The method can automatically learn the internal law and distribution of the simulation data, efficiently generate highly-vivid and diversified new simulation data meeting specific requirements, and deeply enhance the existing limited data, thereby significantly reducing the simulation data acquisition cost, improving the data quality and richness, and improving the simulation data acquisition efficiency. And powerful data support is provided for development and test of a virtual simulation system and simulation-based AI model training.

The invention discloses a carbon balance state calculation method and system based on regional sustainable development, and the method comprises the following steps: obtaining data, and collecting the generation data of carbon emission, related data of carbon absorption, social and economic data, and a carbon emission coefficient; carbon emission and carbon absorption are calculated; calculating a correction amount; carrying out data correction on the calculated carbon emission amount and carbon absorption amount of each region; establishing an economic development level index combined with the carbon emission data and an ecological protection level index combined with the carbon absorption data according to the corrected carbon emission and carbon absorption of each region; the method has the advantages that carbon emission and carbon absorption correction calculation of multiple standards is carried out, the actual situation of regional development is considered, the carbon emission and absorption balance situation between regions can be reflected more accurately, and the accuracy and practicability of carbon balance calculation are remarkably improved.

The invention discloses a post-diagnosis disease management follow-up visit system and a post-diagnosis disease management follow-up visit method, and belongs to the technical field of medical information management. The medical staff terminal is used for configuring a follow-up task list generated by the follow-up path according to follow-up management and receiving task push at an intervention node; the patient terminal is used for receiving a follow-up form generated according to the follow-up plan configuration at the follow-up node, and a patient checks and fills the follow-up form according to the health condition of the patient; the follow-up form content filled by the patient is automatically synchronized to the follow-up work station; according to the post-hospital disease management follow-up visit system and the follow-up visit method thereof, the basic information and the doctor seeing information of the patient are acquired by connecting the HIS, the EMR and other systems of the hospital, and the medical record information generated by the whole-process doctor seeing of the patient is used as the basis for accurately developing the follow-up visit service, so that the post-hospital management coverage rate and the post-hospital management quality are improved.

This utility model relates to the field of agricultural seedling technology, and discloses a rice seedling raising device with heat preservation and moisture retention functions. The device includes a bottom shell, in which a rotary motor is installed. A rotary support is fixedly connected to the output end of the rotary motor. A removal mechanism is provided on the rotary support, and seedling pots are installed on the removal mechanism. A sealing cover is provided on the top of the bottom shell, and an adjustment mechanism is provided within the sealing cover. In this utility model, the rotary motor and rotary support constitute the rotation center of the device. Support plates and seedling pots are evenly distributed on the circumference of the rotary support. The removal mechanism allows the corresponding seedling pots to be removed from the enclosed space of the device, improving the convenience of daily operations such as soil turning and fertilization. The adjustment mechanism expands the effective monitoring range of the sensing unit, enabling a more accurate understanding of the internal environmental conditions of the device and providing strong data support for precise temperature and humidity control.

The invention discloses a railway signal infrastructure control circuit simulation test case construction method and system, and the method comprises the steps: taking a preset condition and a driving condition as input, taking collection information and an equipment state as an expected test result, designing each state of a railway signal circuit into a simulation test case, and carrying out the simulation test of the railway signal infrastructure control circuit. Defining a basic composition structure of the simulation test case; a simulation test sequence composed of a plurality of test cases is designed for a route or a complex scene; and constructing a simulation test case library based on the obtained simulation test case and the simulation test sequence. According to the method, driving conditions serve as input, collected information and equipment states serve as expected output, each test item point is designed into a test case, the number of cases is continuously expanded and enriched, a universal test case library containing most test item points is formed, the automation degree of simulation software testing of the railway signal system can be improved, and the test efficiency is improved. The efficiency and quality of engineering design and construction are improved, and powerful data support is provided for engineering management and maintenance.

This invention relates to the field of primary heating network technology, specifically disclosing a centralized heating primary heating network energy supply balance operation regulation monitoring and control system. The system includes a user area estimated heat demand analysis module, a user area comprehensive heat demand analysis module, an energy station heat distribution module, an energy station heat transmission detection module, an energy station transmission quality assessment module, a display terminal, a protection terminal, and a cloud database. This invention ensures the accuracy and precision of the actual heat supply analysis for user areas, thereby improving the heating efficiency and effect of energy stations. It safeguards the revenue of energy stations on one hand and ensures normal use on the user side on the other. This invention alleviates the pressure on energy stations while meeting the heating needs of users, thereby improving the relative balance of energy stations, reducing the deviation between heat supply and heat demand to a certain extent, and improving the efficiency of centralized energy supply.