1053results about How to "Reduce human intervention" patented technology

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention relates to the technical field of sand making process optimization, in particular to an SCM sand making process optimization system based on the Internet of Things, and provides the following scheme that the system comprises a self-adaptive crushing module, an intelligent screening module, a grinding control module, an intelligent conveying module and a feedback optimization module. And the self-adaptive crushing module adjusts working parameters of crushing equipment in real time according to material characteristics through material identification and equipment adjustment logic. The intelligent screening module achieves material granularity monitoring and blockage prediction and adjustment in the screening process through granularity monitoring and material blockage prediction logic. And the grinding control module dynamically adjusts the pressure and the rotating speed of the grinding equipment by monitoring the dust change in real time. The intelligent conveying module achieves optimization of the speed and the inclination angle of the conveying belt through material flow monitoring and conveying parameter adjustment. And the feedback optimization module optimizes and adjusts parameters of each module of the system according to the finished product quality data. Through intelligent regulation and control, the efficiency of the sand making process and the product quality are improved.

The invention relates to the technical field of hydraulic engineering management, and discloses a hydraulic engineering seepage intelligent monitoring system. Comprising a multi-sensor sensing module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision module, an adaptive monitoring scheduling module, an automatic inspection and response module, a digital twinning and simulation analysis module and a communication module. The multi-sensor sensing module is responsible for collecting multi-dimensional data related to seepage in real time. A machine learning algorithm is adopted to intelligently predict the seepage trend, and risk assessment is performed in combination with real-time data, so that compared with a traditional monitoring method based on a preset threshold value, the method can automatically identify a complex seepage mode penguin through learning historical data and real-time data to predict the future risk in advance, and the risk assessment efficiency is improved. The prospective management of the seepage risk of the water conservancy project is realized, and the prediction precision and the response speed of the system are remarkably improved.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention discloses an electronic bill data management system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting bill data in an electronic bill, extracting features from the bill data by using a feature extraction algorithm, classifying article names in transaction article details through a predefined article classification model, and matching the article names with standard codes in a commodity database; checking whether the bill data has a modification trace or not, comparing historical data, detecting consistency, and returning the electronic bill and marking a corresponding collection user when an exception is found; when the bill data is not abnormal or inconsistent, analyzing whether the amount of money in the electronic bill exceeds a conventional transaction range or not, and generating a risk rating of the bill data; performing risk rating on the receiving users, and analyzing the price interval of the same type of the receiving users in a period of time; and carrying out weighted integration to generate an electronic bill comprehensive risk rating, and transmitting the electronic bill data to a reimbursement platform.

The invention relates to the technical field of artificial intelligence research, in particular to an intelligent desulfurization prediction system based on an FT-LSTM-XGBoost algorithm, which comprises a data acquisition module, a data preprocessing module, a feature engineering module, a model training module, a model fusion module and a control execution module, the data acquisition module monitors and acquires multi-dimensional parameters in a desulfurization process in real time; the data preprocessing module is used for preprocessing the acquired multi-dimensional parameters; the feature engineering module forms a final feature set related to desulfurization; the model training module adopts Fourier transform, a long-short-term memory network and a limit gradient boosting tree to train a desulfurization prediction model; the model fusion module obtains a fused prediction result; the control execution module is used for adjusting the overall operation parameters of the desulfurization system in real time; the method has high-precision prediction capability, so that the accurate control of the desulfurization effect is ensured.

The invention discloses a marketing data link abnormity diagnosis method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of multi-source data format difference, lack of real-time anomaly detection mechanism and low data management efficiency exist in anomaly diagnosis. The method comprises the steps that multi-source heterogeneous data of a marketing data link is collected in real time, and data cleaning, missing value filling and abnormal value processing are completed; extracting statistical characteristics of the link data, performing clustering analysis on the data, and generating a self-supervision label; constructing a support vector machine classification model, and detecting normal and abnormal data in the link; optimizing the anomaly detection model through a deep neural network; and realizing interpretability analysis and decision support of anomaly diagnosis by using a multi-classification result generated by the LSTM model and a self-supervised learning strategy. According to the invention, data link abnormity diagnosis is efficiently and intelligently realized, and data quality management, abnormity detection precision and service decision support capability are improved.

The invention belongs to the technical field of geophysical exploration. The invention provides an inversion method and system based on transient electromagnetic and ground penetrating radar data collaborative correction, and the method comprises the steps: converting the preprocessed ground penetrating radar data into a format of transient electromagnetic detection data, or converting the preprocessed transient electromagnetic detection data into the format of the ground penetrating radar data; performing weighted summation on the ground penetrating radar data and the transient electromagnetic detection data after format conversion to obtain fusion data; performing three-dimensional joint inversion according to the fused data to obtain a final three-dimensional joint inversion result; according to the method, collaborative detection of different depths of the detection area is realized, a brand new underground structure detection interpretation scheme is provided through intelligent data preprocessing, intelligent correction and three-dimensional joint inversion technologies, and comprehensive, efficient and high-precision interpretation of the underground structure is realized.

The invention relates to the technical field of network security, in particular to a GIS (Geographic Information System)-based network security protection system, which comprises a dynamic topology adjustment module, an intelligent threat identification module, a network security evaluation module, a global topology optimization module and an automatic recovery and redundancy design module, the global topology optimization module and the dynamic topology adjustment module are combined, long-term optimization of the whole network is realized on the global level, and the system can comprehensively evaluate each node and dynamically calculate the connection weight in combination with the multi-dimensional characteristics such as the traffic load, the security score and the geographic position of the node, so that the reliability of the network is improved. When a node fault or abnormity is detected, a redundant path can be automatically selected according to historical data and a geographic position, and the method is subjected to topology recovery, so that high-priority nodes can obtain better connection resources, and low-priority nodes can automatically allocate the redundant path, and network performance optimization and load balance are ensured.

The embodiment of the invention discloses a chip register description document generation method and device, a storage medium and terminal equipment, and relates to the field of document processing. The method comprises the following steps: analyzing a register attribute table file of a chip, and determining each functional module included in the chip and a register included in each functional module according to an analysis result; querying an associated register description template document according to the model of the chip; extracting field values corresponding to fields in the register list display area and the register description display area from the register attribute table file, and filling the read field values into a register description template document to generate a register description document of the chip, the problems that in the prior art, compiling of the chip register description document is low in efficiency, prone to errors and poor in timeliness are effectively solved, and a more efficient, accurate and reliable method is provided for compiling and managing the chip document.

The invention relates to the field of progressive stamping die manufacturing, in particular to a stamping die manufacturing control method and system for a new energy motor iron core, and the method comprises the following steps: analyzing the material performance and the stamping effect through artificial intelligence, and intelligently recommending an optimal material combination; the die is quickly verified by using digital design and 3D printing, and the design is optimized by combining virtual simulation; developing an intelligent control system, dynamically adjusting the position of the mold, and ensuring accurate alignment of procedures; a big data platform is established, and punching parameters are automatically optimized through machine learning; online detection is integrated, and mold parameters are adjusted in real time to correct deviation; and mold health monitoring and predictive maintenance are implemented, so that shutdown is reduced. The steps jointly achieve intelligentization and self-adaptive optimization of stamping die manufacturing.

The invention relates to the technical field of powder concentrator control, in particular to an unpowered powder concentrator air volume control method and system, which reduce the time and frequency of shutdown adjustment, improve the continuity and stability of a production line and finally improve the overall production efficiency through real-time and dynamic control. The method comprises the following steps: collecting a plurality of material grading samples obtained by grading and powder selecting of an unpowered powder selecting machine; based on preset material grading target features, target deviation feature extraction is carried out on each material grading sample, and a material grading deviation vector corresponding to the collection time is obtained; obtaining material grading deviation vectors at a plurality of collection times, and arranging the material grading deviation vectors according to a time sequence to obtain a material grading deviation matrix; and inputting the material grading deviation matrix into a pre-constructed powder concentrator grading precision evaluation model to obtain a material grading precision index corresponding to the unpowered powder concentrator.

The invention discloses a patient condition tracking method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting the focus image data of a patient, and carrying out the rigid registration of the focus image data through affine transformation; focus probability distribution is predicted based on a deep convolutional neural network, and focus segmentation and marking are carried out based on a conditional random field CRF. According to the method, through the combination of the deep convolutional neural network and the conditional random field, the accuracy of focus region segmentation is improved, human intervention is reduced, through the combination of rigid registration and model recognition, global alignment of images can be quickly realized, the change of the focus region can be tracked in a detailed manner, and the accuracy of focus region segmentation is improved. The displacement of pixels can be smoothly interpolated between control points through non-rigid registration of the B-spline primary function, the generated image can be accurately aligned with the form and position of a focus area, and reliable basic data is provided for subsequent work such as focus change analysis and form change quantification.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent operation and maintenance system of a cluster system based on reinforcement learning. The system comprises a cluster modeling module, a multi-dimensional situation awareness module, an intelligent decision planning module and an efficiency evaluation module, and realizes autonomous operation and maintenance and dynamic management of the cluster system in a complex environment in combination with various reinforcement learning algorithms. According to the system, reinforcement learning algorithms such as a deep Q network (DQN), Actor-Critic Monte Carlo Tree Search (AC-MCTS) and distributed near-end strategy optimization (DPPO) are introduced to optimize a maintenance time sequence and a strategy, so that the fault rate and the operation and maintenance cost of the system are reduced; the system function is quickly recovered, and the performance loss caused by sudden failure of the system is reduced; and task collaboration and system stability are ensured.

The invention discloses a power grid facility three-dimensional model fitting method and system based on panoramic point cloud, and relates to the technical field of three-dimensional model fitting. The method comprises the following steps: collecting panoramic point cloud data and high-definition image data of a power grid facility, and carrying out data preprocessing; importing a BIM model of a power grid facility, and establishing a homologous coordinate system with the panoramic point cloud data; performing data alignment on the panoramic point cloud data and the BIM model, and extracting key features of the power grid facility from the aligned point cloud data; adjusting vertexes and edges of the BIM model based on the extracted key features to enable the BIM model to be matched with the point cloud data, and ensuring the precision and reliability of the BIM model through consistency verification; and generating a three-dimensional model consistent with an actual power grid facility based on the corrected BIM model for real-time monitoring and defect detection. The operation and maintenance risk caused by data deviation can be reduced, human intervention is reduced, and the automation level of the system is improved.

The invention is suitable for the technical field of public rental housing management, and provides a public rental housing inspection management system, which comprises a hardware equipment layer used for collecting environment data, equipment state data and inspection data of a public rental housing, and the hardware equipment layer comprises a plurality of sensor equipment, a wireless communication module and mobile inspection equipment; the data processing layer is used for receiving, storing and processing data collected by the hardware equipment layer, and the data processing layer comprises a data receiving module, a data processing module, a data storage module, a data analysis module and an early warning module; and the application service layer is used for providing user interaction and system management services according to the data acquired by the hardware equipment layer and the data processed by the data processing layer, and the application service layer comprises a housing resource management module, an inspection task distribution module, an inspection management module, an early warning management module, a contract management module and a housing maintenance management module. The problem of low inspection efficiency in the prior art is solved.

The invention discloses a PCB surface defect detection method and system, an electronic device and a medium, and relates to the technical field of defect detection, the connection condition caused by excessive tin soldering of a tin bridge or adjacent welding spots can be automatically identified by judging the connection state between the welding spots, an overall detection instruction is triggered, early detection is facilitated, connectivity defects are avoided, and the detection efficiency is improved. And the welding spot connection reliability is improved. The image is divided into a plurality of image blocks, the texture difference data of each image block is obtained, the texture abnormal state of the surface of the PCB can be finely analyzed by calculating the texture deviation coefficient, the subtle texture change in different areas can be identified by the block analysis method, and surface defects such as scratches and bubbles on the surface of the PCB can be accurately detected. And in combination with the texture deviation coefficient and the geometric features of the welding spots, unified normalization fitting is performed on the defect features to generate the defect recognition index, so that the defect features of different types can be captured, and the robustness and accuracy of the model can be improved.

The invention relates to the technical field of wet cleaning in a semiconductor manufacturing process, and discloses a semiconductor wet cleaning process optimization method based on PLC programming, and the method specifically comprises the following operation steps: constructing a PLC programming dissolution rate matching system, designing and integrating a set of PLC-based dissolution rate matching system, and optimizing the PLC-based dissolution rate matching system. The system needs to have the functions of data acquisition, processing and feedback, a dissolution rate model is established according to cleaning liquid components and wafer material characteristics, an algorithm is preset to be matched with the actual dissolution process, and sensors are deployed in cleaning equipment and used for monitoring key parameters such as the temperature and concentration of cleaning liquid and the dissolution rate of the surface of a wafer in real time. According to the semiconductor wet cleaning process optimization method based on PLC programming, the rotating speed and cleaning liquid distribution in the cleaning process are accurately controlled, the problem of flowing particle defects caused by the poor dissolving speed in traditional wet cleaning is effectively solved, and the cleanliness and the yield of semiconductor wafers are improved.

The invention discloses a system and a method based on laboratory process intelligent management, and relates to the technical field of intelligent management. The system comprises an experiment task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping time period analysis and intelligent scheduling module and a real-time monitoring and task progress adjustment module. The experiment task classification and sorting module is responsible for acquiring an initial experiment task list and carrying out task classification and sorting; the historical data analysis and task duration prediction module predicts the total duration of the experiment task by using historical data and provides accurate task duration prediction; time overlapping between the overlapping time period analysis and intelligent scheduling module processing tasks is carried out, and the task execution sequence and the equipment use time are intelligently adjusted; and the real-time monitoring and task progress adjusting module tracks the task execution condition in real time, analyzes the actual and predicted deviation and carries out necessary progress adjustment.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a decision generation method based on big data, and the method comprises the steps: collecting multi-dimensional data related to a target business, extracting key features, such as a behavior mode, a potential problem and resource stability, of a customer, constructing a customer analysis model, and carrying out the decision generation. Generating a time sequence data set in combination with the behavior trend data; a behavior data set is generated through scene analysis and quantitative analysis is carried out to obtain a behavior mode and a resource utilization trend of a client, and finally a strategy adapted to each business scene is generated. According to the method, the service strategy is finally generated by collecting the multi-dimensional data, extracting the customer characteristics, constructing the analysis model, analyzing the customer behavior trend, generating the time series data set and combining different service scenes to perform scene analysis and quantitative processing, so that the real-time analysis, scene decision and dynamic adjustment of the multi-dimensional data are realized; the flexibility and accuracy of the system in a complex service scene are improved, and the service decision-making efficiency and accuracy are improved.

The invention relates to a gob-side entry retaining grouting reinforcement technology in the field of mining engineering, provides a method for grouting reinforcement of a reuse roof of a gob-side entry retaining of a coal mine and accurate control of the grouting amount, and aims to realize real-time monitoring and control of the grouting amount in the grouting process. According to the method, pressure monitoring, flow velocity monitoring, low-frequency electromagnetic wave detection, ultrasonic detection and on-site drilling slurry discharge monitoring are combined, the grouting process is detected in real time through multiple monitoring means, and it is ensured that cement slurry fills cracks and does not flow into a goaf. And meanwhile, cement paste with different particle sizes is adopted for grouting step by step according to the size of the crack, and paste leakage is avoided while the filling effect is guaranteed. The method is suitable for grouting operation under complex geological conditions, has the characteristics of high efficiency and accuracy, and remarkably improves the stability and safety in the use process of a roadway.

The invention belongs to the technical field of ship control motion prediction, and discloses a ship control motion real-time prediction method and system based on a fusion technology. According to the method, standardization processing is carried out on ship motion data and marine environment data, the marine environment data and the ship motion data are enabled to be input into a constructed ship motion prediction model based on a Transform architecture in the same numerical range, a sliding time window technology is used, online gradient updating is carried out, and the method adapts to new data features; a physical loss item is introduced, and an improved ship motion prediction model based on a Transform architecture is obtained; and carrying out optimization verification to finish nonlinear ship control motion prediction. According to the method, the defects of a traditional model in the aspect of nonlinear processing can be overcome, the reliability of a data driving model can be improved through physical constraints, and powerful technical support is provided for operation control and safe navigation of intelligent ships in the future.

The invention discloses a multi-beam backscattering intensity jump correction method, which takes a multi-beam measurement strip as a unit, automatically detects backscattering intensity jump generated by factors such as equipment parameter setting through a Gaussian weighting function, and selects a moving window to perform difference compensation by using a weighted average value. And carrying out iterative detection on the repaired intensity data and repairing the jump point until the jump point converges to 0. An example calculation result shows that the method can effectively detect and repair the intensity jump in the multi-beam strip, improve the processing efficiency and quality of the backscattering intensity data of the multi-beam strip, and improve the analysis accuracy of the submarine landform of the multi-beam intensity data.

The invention relates to the technical field of data mining, in particular to a land utilization data set processing method and system. The method comprises the following steps: acquiring ground sensing data of a monitoring area, and constructing coordinate system data of the monitoring area; fusing the ground sensing data of the monitoring area to obtain a monitoring area sensing network; acquiring remote sensing data of the monitoring area and performing calculation to obtain land vegetation coverage rate data of the monitoring area; calculating based on the monitoring area sensing network to obtain monitoring area land vegetation coverage rate error data; according to the monitoring area land vegetation coverage rate data and the monitoring area land vegetation coverage rate error data, carrying out influence factor estimation to obtain a resolution influence factor; and carrying out regional land vegetation coverage rate correction on the monitored regional land vegetation coverage rate data to obtain standardized regional land vegetation coverage rate data, and analyzing the standardized regional land vegetation coverage rate data to obtain regional land vegetation coverage rate trend data. According to the invention, the processing precision of land utilization data is improved.

The invention relates to the technical field of big data analysis and data processing, and provides an intelligent power utilization load management method based on big data analysis, which constructs an accurate power utilization load prediction model by integrating historical power utilization data, climate data, a power grid equipment state and user behavior data. According to the method, the security and traceability of prediction data are improved by adopting a block chain technology, and automation of an electrical load management process is realized by means of an intelligent contract. The method comprises the specific steps of data acquisition and preprocessing, load prediction model construction and verification, prediction data storage and hash value generation, intelligent contract writing and execution, and process node data processing and evaluation. The method effectively solves the problems of load data abnormity and model errors, and improves the stability and management efficiency of a power grid system.

The invention discloses a transparent logistics supply chain tracing management system fused with a block chain technology. The transparent logistics supply chain tracing management system comprises a data acquisition module, a block chain storage module, an intelligent contract module, a user interface module and a data analysis module, the data acquisition module is responsible for collecting real-time data of each link of a supply chain and transmitting the data to the block chain storage module; the block chain storage module uses SHA-256 and ECDSA algorithms to ensure the security and authenticity of the data; according to the method, the blockchain technology is utilized, data of all links of the supply chain is subjected to chaining storage, the data are open, transparent and non-tampering, and the credibility of the data is enhanced; through an SHA-256 Hash algorithm and an ECDSA signature algorithm, the security and integrity of the data in the transmission and storage process are ensured, and the data are prevented from being tampered or forged; and transactions and operations in the supply chain are automatically executed through the intelligent contract module according to a preset rule, so that human intervention is reduced, and the automation level and the operation efficiency of the system are improved.

The invention discloses a spring operating mechanism fault detection and optimization method and a related device, and the method comprises the steps: carrying out the real-time collection of key data of a spring operating mechanism through a stress sensor, a speed sensor and a vibration sensor; based on the collected key data, constructing a multi-dimensional data framework to obtain a multi-dimensional data set; fault feature extraction and data analysis are carried out on the obtained multi-dimensional data set, an adaptive fault model is constructed based on the extracted fault features, and then a real-time fault detection result is obtained through multi-modal data fusion; and optimizing a stress sensor, a speed sensor and a vibration sensor by utilizing automatic calibration and optimization of reinforcement learning according to the real-time fault detection result. Compared with a traditional fault detection method, high-precision real-time monitoring and fault prediction of the spring operating mechanism are achieved through the multi-modal data fusion and deep learning technology, and comprehensive and reliable technical support is provided for safe operation and maintenance optimization of equipment.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.