2726results about How to "Efficient management" patented technology

The invention discloses an exhibition hall robot visual language navigation method based on a large model, belongs to the field of exhibition hall robots, and aims at solving the problems existing in an existing exhibition hall robot. According to the method, an exhibition hall robot multi-mode scene sensing system senses a current scene, and candidate target objects are obtained through fusion features of RGB images and laser point clouds; extracting image features of all candidate target objects in the scene; according to the exhibition hall robot multi-mode environment understanding system, an exhibition hall robot receives a voice instruction, a visual language multi-mode large model is used for understanding a target object and a user intention, then high-dimensional semantic features of a target object text are extracted from the system, and the target object is found out from candidate target objects; according to the exhibition hall robot intelligent navigation system based on the multi-modal large model, an exhibition hall robot carries out path planning and autonomous movement according to a pose sequence generated by an intention, and self behaviors are dynamically adjusted according to a real-time changing scene provided by the exhibition hall robot multi-modal scene sensing system.

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention provides a buried water delivery pressure steel pipe disease inspection method and system, and the method comprises the following steps: carrying out the time sequence alignment and quality evaluation of multi-source sensor data, such as ultrasonic waves, videos and positions, carrying out the feature extraction and optimization through adaptive filtering and frequency smoothing technologies, and carrying out the partitioning compression storage based on entropy and variability; carrying out decompression and feature reconstruction on the compressed data, carrying out state evolution prediction based on multi-scale state trajectory analysis, and carrying out anomaly detection and classification in combination with a dynamic reference model; health feature vectors are constructed, health state distribution is obtained through nonlinear feature fusion and probability density analysis, deterioration trend prediction is carried out based on environment coupling analysis, risk rating and early warning are carried out by adopting a multi-level dynamic threshold value, and finally a maintenance priority sequence and a maintenance scheme are generated. According to the invention, pipeline diseases can be accurately identified, and detection efficiency and reliability are improved.

The invention provides a security encryption communication method and system based on quantum key management. The method comprises the following steps: acquiring system state data, and generating node state data, network connection diagram data and a system state matrix; receiving a communication request data packet, and generating a request feature vector, a service weight coefficient and a service demand matrix; the method comprises the following steps: collecting real-time network state data, constructing an enhanced Bayesian network model, and generating a network state scoring matrix; acquiring key pool state data, establishing a key consumption prediction model, and generating a key resource distribution matrix; constructing a multi-objective optimization model, and generating a scheduling strategy matrix; and executing the scheduling strategy and performing performance evaluation. According to the method, the quantum state de-coherence effect and the channel noise influence are considered, efficient management and safe distribution of quantum keys are achieved by establishing a complete evaluation and prediction system, and the performance and reliability of a communication system are improved.

The invention discloses an asset risk tracing method and device. According to the method, the network topological graph and the asset vulnerability list are obtained, the system asset topological graph is drawn to identify the key asset risk, and the system risk list is formed. And further drawing a data flow diagram, identifying data asset risks, and forming a data risk list. Big data analysis is used for identifying risk events and influences thereof, a risk event causal chain is constructed, and risk roots are traced. And based on the risk source, constructing a risk propagation map, and realizing visualization and dynamic tracking of the risk event.

The invention relates to a financial question and answer method, system and device based on multi-agent interaction and a medium. The method comprises the steps that query understanding is conducted on user input through relevant entity word recall and similar question and answer pair recall, then knowledge recall and tool recall are conducted, and a prompt template of a planning agent is filled with recall results; the user input is answered according to the prompt template through the planning agent, the answer content is extracted to obtain an API execution link, and a directed acyclic graph is generated; calling a calling tool packaged by a related API through an execution agent, and executing a task corresponding to the directed acyclic graph in parallel; and generating an answer according to an output result of the execution agent through the summary agent, performing re-planning judgment through the reflection agent according to input and output information of the planning agent and the answer of the summary agent, and outputting a current answer. By adopting the method, more professional, accurate and efficient financial question and answer service can be provided for the user.

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

The invention provides a distributed power data asset management privacy protection encryption architecture design method, which comprises the steps of generating a watermark with robustness and imperceptibility by adopting a content-based digital watermarking algorithm according to statistical characteristics of power data assets, and storing a watermark embedding process and an extraction result through a blockchain; a graph-based data model is adopted, the electric power data assets carrying the watermarks serve as nodes, the data flow relation serves as edges, and an electric power data asset consanguinity graph is constructed; for distributed storage of the blood relationship map, a P2P network topology is constructed by adopting a block chain and a distributed hash table, and blood relationship map data is stored in each node of an alliance chain in a distributed manner; and verifying the digital watermark information embedded in all circulation path nodes through which the power data assets pass in the blood relationship map, calculating the similarity between the extracted watermark and the original watermark, calculating the distortion rate, and judging whether the power data assets are tampered or not.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

The invention relates to the technical field of task allocation, in particular to a task allocation method for flexible employment. According to the technical scheme, the method comprises the following steps that a database of related information of tasks and employees is constructed, wherein the database comprises detailed information of the tasks and comprehensive information of the employees; respectively constructing a task model and an employee portrait based on the information in the database; and matching the task with the employee through an intelligent matching algorithm, wherein the intelligent matching algorithm comprises matching algorithm selection and optimization, real-time matching and dynamic adjustment, and multi-objective optimization and tradeoff. According to the method, multiple matching algorithms are combined for optimization, weights are selected and adjusted according to tasks and employee characteristics, accurate matching is achieved, a task model and employee portrait multi-dimension consideration adaptability is achieved, adaptive changes are matched in real time, tasks and multiple aspects of employees are optimized and balanced in a multi-objective mode, the development of rights and interests of the employees is considered, and the matching quality, the working enthusiasm and the stability are improved; flexible employment resources are managed more effectively, and the task allocation effect is improved.

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention discloses a metering-based carbon emission accounting system, which relates to the technical field of carbon emission accounting, and comprises the steps of acquiring energy consumption data, modeling the acquired energy consumption data, predicting an energy consumption trend, analyzing the energy consumption data, generating a preliminary carbon emission factor according to different energy types and consumption, and calculating the carbon emission of the carbon emission accounting system according to the preliminary carbon emission factor. And performing iterative optimization on the initial carbon emission factor through a carbon emission factor generation and optimization module, calculating the carbon emission by using the optimized carbon emission factor, setting a threshold value for comparison, performing early warning on the abnormal condition of the carbon emission in real time, and matching an emission reduction suggestion through an emission reduction optimization suggestion module. According to the invention, through energy consumption data acquisition, energy consumption trend prediction, carbon emission factor generation and optimization, and early warning and emission reduction optimization suggestion, the automation and intelligence level of carbon emission management is improved, and powerful technical support is provided for green low-carbon transformation.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

The invention provides a clamping force correction method and system based on an oil-tea tree branch clamping manipulator and a storage medium, and relates to the technical field of mechanization and automation, and the method comprises the steps of monitoring and analyzing oil-tea tree branches, an operation environment and a manipulator motion state, collecting key parameters, generating a clamping adaptability index and a clamping stability index, and correcting the clamping force of the oil-tea tree branches; the clamping adaptability index and the clamping stability index are combined, and a clamping control index is calculated; constructing a deep learning model, taking the historical key parameters as a training set, taking the corresponding historical manipulator clamping force as a label, training the model to obtain a clamping force prediction model, inputting the key parameters collected in real time into the model, outputting the predicted clamping force, combining the predicted clamping force with a clamping control index, and calculating the final clamping force. On the basis of obtaining key parameters in real time, the clamping adaptability index and the clamping stability index are generated, then the clamping control index is calculated, and the purpose of dynamically adjusting the clamping force is achieved.

The invention relates to the technical field of raw material management systems, in particular to a raw material management system for building engineering construction. The management system comprises a raw material information management module, a purchase management module, an inventory management module, a use management module, an optimization allocation module, a raw material quality evaluation module, an environmental impact evaluation and material selection module, a supply chain transparency module, a life cycle management module and a raw material demand prediction and inventory optimization module. An automatic purchase and supplier collaborative optimization module; the system uniformly manages data information of links of raw material purchasing, inventory, use and the like through a centralized data platform, forms a complete material management chain, avoids information dispersion and lag, realizes real-time sharing of data of each link, improves transparency of management and accuracy of decision making, and meanwhile, greatly improves management efficiency. The complexity and error rate of manual operation can be remarkably reduced through automatic purchase order generation, intelligent inventory management and supplier collaboration.

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 data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

The invention provides a risk control early warning method and system based on bank flow analysis, and relates to the technical field of computers, bank flow data of a target user in a bank system is collected, the collected bank flow data is fused with multi-source cross-domain data, and time sequence data of transaction behaviors is generated according to a time sequence; performing data feature extraction on the time sequence data to obtain transaction features; constructing a framework of a risk prediction model, and obtaining the risk prediction model through training and verification; real-time flow data of a target user in a bank system is monitored in real time, a trained risk prediction model is utilized to evaluate transaction behaviors of clients in real time, an anomaly detection result of an anomaly detection model is combined with output of the risk prediction model, a comprehensive risk score is formed to identify potential risks, and an early warning mechanism is triggered. Through deep analysis of bank flow data, real-time risk monitoring and early warning are realized, and potential financial risks can be effectively identified.

The invention discloses a data cooperative transmission method and system for a low earth orbit satellite Internet of Things terminal, and the method specifically comprises the steps: carrying out the classification cutting of environment data, and forming a plurality of environment data subsets; determining a plurality of cooperative sending terminals and respective optimal communication paths between the first low earth orbit satellite Internet of Things terminal and each cooperative sending terminal based on a communication topological structure relationship between the low earth orbit satellite Internet of Things terminals in the ad hoc network; transmitting the environment data subsets with different priorities to corresponding cooperative sending terminals through different optimal communication paths; and according to the environment data subsets sent by each cooperative sending terminal and received by the low-orbit satellite, splicing the environment data subsets and forwarding the spliced environment data subsets to a target terminal or a ground station or a data center. According to the invention, the cooperative effect among a plurality of low-orbit satellite Internet of Things terminals can be fully utilized, and stable and reliable data transmission under the requirements of large data volume and high real-time performance is realized.

The invention relates to a coal mine potential safety hazard characterization method and system based on a knowledge graph, and belongs to the technical field of coal mine safety. The method comprises the following steps: data collection: collecting coal mine daily safety inspection records including hidden danger types, occurrence places, time and related descriptions; data preprocessing: cleaning, classifying and standardizing the collected data so as to facilitate subsequent knowledge extraction and graph construction; and knowledge graph construction: based on the preprocessed data, identifying key entities, extracting relationships among the entities, and constructing a coal mine potential safety hazard knowledge graph. Through the hidden danger correlation analysis and risk assessment module in the knowledge graph, the system can assess the risk degrees of different hidden dangers in real time and generate corresponding early warning information according to the assessment result, and the dynamic early warning mechanism enables managers to master the safety condition of a coal mine in time and quickly respond to potential safety threats.

The invention provides a multi-modal digital twinborn scene building method and device based on image semantic fusion, and relates to the technical field of data processing and digital twinborn scenes. The method comprises the steps of obtaining an image data set and a text data set of a building construction site, and performing preprocessing; inputting the preprocessed image data set and text data set into a natural language processing model to obtain semantic description auxiliary data; inputting the preprocessed image data set into a target detection algorithm to obtain target extraction image data containing a target detection frame; fusing the output results of the two targets to obtain a fusion box of each target; then, a perspective projection algorithm is adopted to calculate a coordinate position corresponding to each target in the BIM, and three-dimensional world coordinates are obtained; and inputting the three-dimensional world coordinates into the BIM by adopting Dynamo to realize the building and real-time updating of the multi-modal digital twinborn scene. According to the method, the accuracy and robustness of scene recognition are remarkably improved, and the defects of a traditional method in the aspect of multi-modal data processing are overcome.

The invention relates to the technical field of industrial internet data acquisition, in particular to a data acquisition method and system based on an industrial internet operating system, and the method comprises the following steps: S1, detecting and recognizing data sources of a plurality of industrial devices and sensors; s2, collecting original data from a data source in real time, and preprocessing the original data; s3, distributing part of calculation tasks to a plurality of edge nodes; s4, storing the data in a distributed database or cloud storage in a classified manner; s5, performing fragmentation processing on the data in the cache; s6, performing data synchronization and consistency verification; and S7, converting the data into a corresponding format. According to the invention, by implementing efficient data fragmentation processing, a dynamic storage strategy and secure data consistency verification, the processing speed and storage efficiency of the industrial internet data acquisition system and the security and integrity of data are remarkably improved, and the requirements of high efficiency and high reliability of modern industrial production are effectively supported.

The invention discloses an intelligent working place dust monitoring and early warning system. The system comprises a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface. The dust sensor detects the dust concentration in the air in real time, dust with different particle sizes is included, the data processing module carries out concentration calculation, trend analysis and acceleration monitoring, and dynamic threshold adjustment and trend prediction are supported. The system has a multi-stage alarm function, and when the dust concentration reaches a preset threshold value, early warning signals with different intensities are triggered to remind a user to take protective measures in time. The system not only improves the real-time performance and accuracy of dust monitoring, but also has automatic learning and intelligent early warning functions, is suitable for various high-dust environments such as mines, construction sites, workshops and the like, and remarkably improves the safety management level of workplaces.

The invention relates to the technical field of engineering project management, in particular to an engineering project construction quality management method and system, and the method comprises the following steps: extracting multiple items of design information of a target building based on project construction drawing information, and simulating and analyzing the stress distribution of a building structure under multiple loads through building a building information model. And evaluating the stability of the target building design, identifying and marking design defects, and obtaining a simulation analysis result. According to the method, by extracting the design information, constructing the building information model, evaluating the stability of building design, recognizing the design problem in the early stage, calculating the actual solidification rate of concrete and detecting the concrete quality, reworking and overhigh cost caused by the concrete quality problem are reduced, and defects in prefabricated parts are detected through ultrasonic waves; the construction safety and quality are improved, the construction deviation is quantified through measurement of an actual construction image and comparative analysis of the actual construction image and a design drawing, and efficient management of the construction quality of a target project is achieved.

The invention discloses a point cloud data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring initial point cloud data to be sent; dividing the initial point cloud data into a group of point cloud blocks; performing graphical representation on the basis of the point cloud data in each point cloud block in the group of point cloud blocks to generate a group of graph structure data, one graph structure data in the group of graph structure data corresponding to one point cloud block in the group of point cloud blocks, and the graph structure data being used for representing features of the corresponding point cloud block; and encoding the group of graph structure data to obtain a group of data packets, and sending the group of data packets to a target terminal so as to perform decoding reconstruction at the target terminal to obtain target point cloud data. The technical problems that the processing efficiency of the point cloud data is low and the effect cannot be quickly achieved in the related technology are solved.

The embodiment of the invention provides a file resource reuse library management method and device based on a database, and efficient management of project resources is realized by constructing a distributed column family database cluster and dividing the distributed column family database cluster into a plurality of special column families. A multi-dimensional index mechanism is innovatively designed and comprises reverse indexing, reverse indexing and version linking, and flexible resource retrieval is supported. A distributed version lock and a transaction manager are introduced to process concurrent access, and a multi-user cooperation scene is intelligently processed through a conflict detector. And the system automatically processes resource import and tool chain configuration, so that the consistency and reliability of resource reuse are ensured. According to the method, the limitation of traditional file resource management is broken through, and an efficient resource reuse solution is provided for large-scale project development.

The invention, which relates to the technical field of social emotion analysis, discloses a university social negative emotion characterization analysis system comprising a data acquisition and preprocessing module, a feature extraction and anomaly detection module, an intelligent perception and negative emotion characterization module, and an anomaly risk assessment and response module. Emotional feature information is acquired in real time based on a multi-dimensional data source, and the acquired emotional feature information is cleaned and preprocessed. According to the method, by calculating the data consistency offset coefficient and the strong correlation coefficient, the multi-collinearity problem among multi-dimensional data features is solved, and instability of the model regression coefficient is avoided. And the system identifies abnormal correlation in real time and combines risk threshold classification processing to ensure robust model output. An analysis set is established by continuously collecting data, long-term emotion trend analysis is supported, personalized responses such as reminding, intervention or emergency measures are provided according to risk levels, and accurate management and timely adjustment of student emotional states are achieved.

The invention provides an explosion-proof power distribution cabinet control method and system based on situation awareness, and relates to the technical field of power distribution cabinets, and the method comprises the steps: obtaining the associated power equipment information of a target power distribution cabinet; analyzing the connection relation of the same-level equipment to generate an equipment network topology structure; acquiring use scene information, and generating an operation configuration parameter set; acquiring an operation condition data set; judging whether a running configuration parameter set is met or not; if yes, performing real-time state monitoring to obtain state monitoring information of the target power distribution cabinet; inputting a situation awareness prediction model to carry out abnormal state prediction to obtain an abnormal state prediction coefficient; and judging whether an abnormal state prediction coefficient threshold value is met or not, and if yes, generating a first emergency processing instruction to perform emergency processing. According to the method, the problems of low data acquisition and processing efficiency and poor timeliness and safety of power distribution cabinet control due to lack of accurate prediction of abnormal states in a traditional method are solved.