84 results about "Real time analytics" patented technology

The invention relates to the technical field of carbon emission, in particular to an on-chain enterprise carbon emission accounting method and system. The method comprises the following steps: acquiring energy consumption, process flow and supply chain collaborative data of an enterprise in links of raw material acquisition, production processing, logistics transportation, equipment operation and recovery processing through Internet of Things equipment, and generating a carbon emission data set; based on a life cycle evaluation theory, boundaries of direct emission, indirect energy emission and supply chain associated emission are divided, and enterprise-level, product-level and supply chain-level carbon emission accounting models are constructed. A machine learning algorithm is utilized to analyze production fluctuation data in real time, carbon emission factor weights and accounting boundary parameters are dynamically adjusted, and an accounting result is optimized. And outputting a result to a green purchase decision-making system, a carbon asset management system and a supply chain collaboration platform. The system integrates industrial Internet of Things equipment, block chain nodes and enterprise resource planning system interfaces, collects and verifies full-link data in real time, solves the problem of data collection, and improves the accounting reliability.

The invention relates to a distributed storage cache hotspot prediction method and system based on an access mode. The method comprises the following steps: acquiring an access log to obtain original data; the gradient change of the access frequency between the adjacent data blocks is analyzed in real time to divide the hot spot range and identify continuous hot spot data blocks; constructing a time sequence based on the historical access record, and pre-judging an access hotspot in a future time period by applying a time sequence trend prediction algorithm; constructing a multi-dimensional feature vector and inputting the multi-dimensional feature vector into a machine learning prediction model to output a hotspot probability of the corresponding data object in a future time period; according to the hotspot probability and a popularity threshold value, scheduling the data predicted as the hotspot into a cache, and allocating corresponding cache levels or storage paths for the data objects with different popularity at the same time; and periodically updating the prediction model and the popularity statistics to obtain dynamic prediction and cache optimization of the hotspot data. According to the invention, the prediction of the hotspot data is realized, and the hit rate and the system performance of the distributed cache are improved.

The invention discloses a block file space topology aggregation and cross-modal indexing method based on HDF5, and relates to the technical field of electric digital data processing. The method comprises the following steps of point cloud space division, space data separation and storage, multi-modal data fusion and multi-level index construction. The method comprises the following steps: performing space division on a point cloud space through an octree algorithm to obtain octree nodes, analyzing the relevance between the space division and a memory in real time to determine whether to perform octree division dynamic adjustment, then encoding each octree node, performing space block data separation storage, and simultaneously performing point cloud data access optimization. According to the method, the multi-level index system of the point cloud space is established, then spatial attribute aggregation is performed to establish the spatial diagram, multi-modal data integration fusion is performed, and finally the multi-level index system of the point cloud space is established, so that the efficiency of accessing and retrieving the image block file space is improved, and the problem of low cross-modal retrieval efficiency of the point cloud space divided based on an octree algorithm in the prior art is solved.

The invention discloses an intelligent work order dynamic scheduling processing method based on multi-source data fusion. The method comprises the following steps: collecting Internet of Things data, resource data and personnel data through a data fusion layer; the intelligent decision-making layer analyzes the Internet of Things data in real time, creates a pre-diagnosis work order, or manually creates a fault work order and submits the fault work order to the intelligent decision-making layer; after a work order enters the system, an intelligent decision-making layer calculates a comprehensive scheduling score for each maintenance personnel meeting the condition, automatically distributes the work order to the personnel with the highest score, and pushes all information to a communication terminal of the intelligent decision-making layer; during dispatching, warehouse inventory is automatically checked through the resource collaboration layer; after maintenance is completed, results and time are recorded back to the system, and a self-learning closed loop is formed. According to the method, the mode of finding things by people and finding objects by people is changed into the mode of finding people by things and preparing objects, the method is a fundamental change of an operation and maintenance mode, the per capita efficiency can be improved, the cost can be reduced, and excessive dependence on personal experience can be avoided.

The invention relates to the technical field of supply and demand data matching, and discloses a supply and demand data dynamic optimization system and method based on a full life cycle, and the system comprises a decision module, a data analysis module, a supply module and a storage and transportation module. Analyzing the supply portal information and the appeal portal information in real time to obtain layout analysis data appeal information; evaluating and sorting the layout analysis data according to the appeal information to obtain a plurality of supply sorting tables; constructing an initial supply and demand plan according to the supply sorting table and the demand information, and inputting the initial supply and demand plan into a supply portal for rationality analysis to obtain dynamic adjustment data; the initial supply and demand plan is updated according to the dynamic adjustment data, the supply portal constructs a production list according to the target supply and demand plan, and the appeal portal generates storage data and a transportation plan according to the production list. According to the method, all data of the supply and demand parties are matched and optimized by constructing a full-period supply and demand management architecture, so that the supply and demand matching efficiency is improved.

The invention relates to the technical field of rail transit equipment safety, and discloses a rail transit vehicle domestic chip door controller fault diagnosis method based on deep learning, which comprises the following steps: step 1, through a vibration sensor, a temperature and humidity sensor and a door control system working state sensor which are deployed in a rail transit vehicle door control system, a fault diagnosis result is obtained; collecting operation data, environment data and external disturbance factor data of the door controller in real time; step 2, starting a data preprocessing module, performing cleaning and formatting operation on the data collected in real time in the step 1, and integrating the data from different sensors by adopting a multi-dimensional data fusion technology; according to the technical scheme, the deep learning model is adopted to analyze multi-source sensor data in real time, the technical effects of rapid fault recognition and early warning are achieved, compared with a traditional relay control mode applied in the prior art, the defects that response is slow and faults are not found in time are overcome, and the safety of passengers getting on and off the bus is improved.

The invention belongs to the technical field of data mining, and provides a GUI proxy data mining method and system. The method comprises the following steps: based on a given initial user intention and an initial GUI (Graphical User Interface) state, constructing and maintaining an intention-trajectory tree through an improved MCTS (Monte Carlo Tree Search) algorithm; in the simulation stage of the MCTS algorithm, the MLLM is utilized to analyze the state of the current expansion node in real time, and a reward signal used for evaluating the value of the node is directly output; after the intention-trajectory tree is searched, all trajectories from a root node to a leaf node in the tree are extracted, and filtering is carried out based on a preset quality standard; for the filtered trajectory, automatically generating a corresponding natural language intention description, and forming an intention-trajectory pair; and performing iterative training on the MLLM used in the process by using the intention-trajectory pair subjected to intention recovery processing. According to the method, GUI proxy data mining of diversified high-quality data can be realized.

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system that may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor to execute code from a computing memory. The code may comprise an adaptor to push the received raw data to a database to processed data; an extractor to extract the processed data from the database; predictive analytics to receive the extracted processed data and apply thereto a predictive model comprised of target data for the at least one machine, and to provide feedback to the at least one machine to modify performance of the at least one machine based on the application of the predictive model; and a visualizer capable to provide at least a visualization of the feedback and the performance.

Systems and methods for automating and optimizing cross-institutional Request for Information (RFI) processing are described including generating and applying a RFI template, identity management, and multi-modal communication channels. A dynamic channel selection engine routes RFIs based on real-time analytics and compliance needs, while adaptive privacy controls protect sensitive data. Machine learning-driven workflow optimization predicts efficient processing steps and automates routine tasks. A plug-and-play integration layer enables seamless adoption with existing systems, and a unified audit framework ensures regulatory compliance.

The invention discloses a logistics warehouse scheduling system and method based on big data, and relates to the technical field of logistics warehouse management, and the scheduling system comprises a data collection and preprocessing module which is used for collecting various types of data in a warehouse and preprocessing the various types of data; the emergency recognition module is used for establishing a recognition model based on a BP neural network for recognizing emergencies; the scheduling decision module is used for formulating an optimal scheduling scheme after receiving the trigger signal of the emergency identification module; the execution monitoring module is used for monitoring the execution condition of the scheduling scheme formulated by the scheduling decision module and feeding back information in time; according to the logistics warehouse scheduling system and method based on the big data, the identification model based on the BP neural network is combined with the big data processing technology, warehouse data can be analyzed in real time, emergencies can be accurately and rapidly identified, and the hysteresis of manual identification of a traditional system is overcome.

The invention discloses an electric power intelligent warehouse management method and system based on a large model, and belongs to the technical field of intelligent warehousing, and the method comprises the steps: S1, obtaining the basic information and dynamic data of electric power materials in real time; s2, cleaning and integrating the basic information and the dynamic data to generate a structured database; s3, constructing a data analysis model based on the pre-trained large model, calling dynamic data and basic information, generating a demand prediction result and outputting a quality risk assessment result; s4, adjusting a preset inventory strategy according to the demand prediction result, and generating a processing suggestion according to the quality risk assessment result; and S5, feeding back the adjusted inventory strategy and the processing suggestion to a preset power intelligent warehousing operation system. The system comprises an acquisition module, a generation module, a prediction and evaluation module, an early warning module and a feedback module. According to the method, the future demand quantity of the electric power materials is predicted by constructing the data analysis model, and real-time data analysis and demand trend prediction are realized.

The invention discloses an Internet of Things intelligent safety helmet sensing and early warning positioning method and system, and the method comprises the steps: continuously collecting the motion and environment data of a user through a built-in multi-sensor array of a helmet, employing a self-adaptive wireless communication technology, intelligently selecting a transmission mode according to a network condition, and guaranteeing the reliable transmission of the data to a cloud platform, the cloud end adopts a distributed time sequence database and a stream processing engine architecture to perform efficient storage and real-time analysis on mass data, performs matching through multi-time window feature extraction and a multi-level security rule base to realize accurate state evaluation and danger level judgment, and after a structured early warning message is generated, a high-availability message-oriented middleware cluster is utilized to perform real-time analysis on the mass data. Different service quality and pushing strategies are selected according to message priorities, it is ensured that early warning information is timely and reliably distributed to a monitoring terminal, and therefore an all-around safety protection system integrating real-time sensing, intelligent analysis, accurate early warning and closed-loop feedback is constructed.

An object of a system according to an embodiment is to provide an optimal training plan according to a fitness level, a constraint, and a goal of a user.SOLUTION: A system according to an embodiment includes a user information collection unit, a training plan generation unit, a data analysis unit, and a correction proposal unit. The user information collector collects the user's fitness level, constraints and goals. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. The data analysis unit analyzes user data and sensor data during training in real time. The correction proposal unit corrects the training plan based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

The application discloses a kind of nuclear power plant operation event trend analysis and prediction method and device, for the accuracy of existing statistical analysis method is insufficient in complex nuclear power environment, lack of multivariate nonlinear consideration and real-time analysis capability is short of etc., the application fuses deep learning and big data analysis technology, realizes event trend identification and high-risk area prediction.It includes: collecting operation log, sensor data and other multi-source event data, is stored after cleaning, standardization preprocessing;Extract the key features of event occurrence time, type, severity, etc., and construct a feature set;Through the analysis of time series data based on standardized event data extraction multi-LSTM based on standardized event data extraction multi-neural network model, identify long-term trends and periodic changes;Combined with deep learning and regression analysis, predict the probability of event occurrence and severity, locate high-risk period and area;Generate real-time warning information and provide prevention suggestions.

An AI-powered personal branding system for real-time analysis and strategic optimization, the system comprising: a user onboarding module configured to securely register a user and assign one or more digital assets associated with a personal brand; a data acquisition module configured to collect real-time and historical digital data from multiple sources, including content data, interaction metrics, and behavioral signals from one or more online platforms; an AI analytics engine that includes machine learning and natural language processing models configured to analyze the collected data and calculate a brand score representing branding performance; an optimization and recommendation module configured to generate strategic brand recommendations based on the brand score and analysis results; and a feedback and adaptive learning module configured to update the AI ​​analysis engine based on performance results, The system operates in a closed control loop to continuously optimize personal branding in real time.

The invention relates to the technical field of business and financial fusion, in particular to a collaboration method and system for containerized mixed deployment of a database and financial software. The method comprises the following steps: respectively packaging a database and financial software into independent containers and carrying out mixed deployment on a container arrangement platform; establishing a cooperative communication mechanism between the two to realize data synchronization and resource dynamic cooperative distribution; a deep learning model is integrated to analyze business data and system load in real time, and container resource allocation and data access strategies are dynamically optimized. According to the method, deep coupling of business and financial applications and the underlying database in a deployment layer is realized through a containerization technology, intelligent resource management and optimization are realized by utilizing deep learning, the problems of data islands and performance bottlenecks are effectively solved, the requirements of'business and financial fusion 'on real-time performance, consistency and high efficiency are met, and the method is suitable for popularization and application. And the resource utilization rate, the automatic operation and maintenance level and the overall performance of the system are remarkably improved.

The present technology includes systems and processes that relate to the optimization of healthcare supply chain management through advanced data integration, real time analytics, and the strategic application of machine learning and artificial intelligence to enhance operational efficiency and decision-making across healthcare entities. It enhances decision-making processes through the use of machine learning and artificial intelligence, enabling healthcare providers to anticipate supply needs, mitigate risks of shortages or surpluses, and ensure the timely availability of essential medical supplies. This technology significantly reduces operational costs and improves patient care by optimizing the flow of goods and information across the healthcare supply chain.

Implementations for providing automated insights in note applications are provided. One example includes a computing system (100) for providing automated insights in a note application (101), the computing system (100) comprising: processing circuitry (102) and memory (104) storing instructions that, during execution, causes the processing circuitry (102) to: analyze content of a note in real-time in the note application (101) using a large language model (LLM) (114), wherein the LLM (114) identifies content search query with retrieval-augmented generation (RAG) in a database; retrieve content from the database; summarize the retrieved content from the database using the LLM (114); display the summarized retrieved content (206) in real-time in the note application (101); and upon receiving input from a user to incorporate the summarized retrieved content (206), incorporate the summarized retrieved content (206) into the note.

The invention discloses a meta-universe-based user digital cloned behavior pattern generation method, and relates to the technical field of digital cloned learning, and the method comprises the steps: carrying out the processing of collected user social data, meta-universe behavior data and behavior context, and obtaining user behavior event data; performing multi-channel text feature model processing on the user behavior event data to obtain a text feature vector, performing multi-channel task processing to obtain a text classification probability, and constructing a mixed kernel embedded learning model to obtain a text classification result; and mapping the text classification probability and the behavior context to obtain user behavior mapping data, and finally analyzing the user behavior mapping data and the current meta-universe environment to obtain user behavior parameters, so that personalized autonomous behaviors are kept when the user is offline, and deep behavior preferences and decision logic of the user are effectively analyzed in real time.

The application discloses a kind of whole process state analysis methods of grounding resistor, comprising the following steps: S1.grounding resistor state acquisition device collects the real-time running condition data of grounding resistor, and the real-time running condition data is transmitted to embedded processor;S2.embedded processor stores and processes the real-time running condition data of grounding resistor, and the real-time running condition data of grounding resistor is uploaded to station end server;S3.cloud server carries out offline analysis and test to the state of grounding resistor, obtains the state transition data of grounding resistor, and updates offline database;S4.according to the real-time running condition data of grounding resistor and state transition data, the state of grounding resistor is analyzed in real time by station end server, and real-time database is updated.Temperature evaluation is used for early warning of abnormal state, resistance value evaluation is used to determine whether the current state of resistor is stable, energy loss is used to determine the service life of resistor, to improve the safe and stable operation of distribution network.

A system is provided.SOLUTION: A system comprising: a sensor unit provided in a terminal and configured to acquire acceleration, speed, and position information of a vehicle; a data transmission unit configured to transmit the acquired sensor data to a server at regular intervals; a generation-type artificial intelligence unit provided in the server and configured to analyze the received sensor data in real time; a recommendation generation unit configured to transmit eco-driving advice generated by the generation-type artificial intelligence unit to the terminal; and a user interface unit configured to notify a user of the eco-driving advice transmitted by the recommendation generation unit.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to analyze the emotion, values, and lifestyle of a consumer in real time and improve products, services, and marketing strategies based on the analysis.SOLUTION: A system includes a data collection part, an analysis part, an improvement part, and a strategy part. The data collection unit collects the emotion, values, and lifestyle of the person. The analysis unit analyzes, in real time, the emotion, values, and lifestyle of the person collected by the data collection unit. The improvement unit improves the product or the service based on the insight obtained by the analysis unit. The strategy component refines the marketing strategy based on the insights obtained by the analysis component.SELECTED DRAWING: Figure 1

Arrangements for multi-party verification are provided. In some aspects, a computing platform may receive a request to initiate a transaction. The computing platform may retrieve, from a plurality of entities, data related to connectivity between accounts involved in the transaction and / or users thereof. The computing platform may analyze the data in real-time and determine whether connectivity exists. If so, the requested transaction may be processed. If not, additional data may be retrieved from one or more public sources. The computing platform may generate an adjacency matrix based on the retrieved data that may indicate a likelihood of validity of a recipient account to the transaction (e.g., based on adjacency determined from the matrix). The computing platform may compare the likelihood of validity to a threshold and, if the threshold is met, the transaction may be processed. If not, the transaction may be denied.

The invention relates to the technical field of infectious disease prediction, in particular to an infectious disease transmission risk prediction method and system based on big data analysis, and the method comprises the steps: firstly collecting multi-source heterogeneous data of population flow, environmental meteorology, historical epidemic situation, social media and public health resources, and then carrying out the cleaning, fusion and space-time alignment of the data, constructing a standardized database, training a dynamic propagation model based on machine learning, iteratively optimizing parameters by combining an SEIR improved model and real-time data, and finally outputting a regional risk level prediction result and a visual early warning map; according to the method, the multi-source heterogeneous data are fused, and the dynamic machine learning model is used for real-time analysis, so that complex influence factors of virus transmission can be more comprehensively captured, prediction deviation caused by single data or update delay in a traditional method is remarkably reduced, the risk early warning precision is improved, and the risk early warning efficiency is improved. And minute-level data processing and risk level updating are realized.

The invention provides a vehicle-mounted double-unmanned aerial vehicle sewage draining exit identification device and method, and the method comprises the steps: carrying out the preliminary recognition of a water surface pollution sign through a first detection unmanned aerial vehicle carrying a hyperspectral and thermal infrared camera; the second unmanned mother machine carries the underwater robot, goes deep into the water to detect the sewage draining exit, collects water quality data and analyzes the water quality data in real time; the data processing system is combined with an AI deep learning algorithm and a GIS technology to intelligently identify a pollution source and generate a monitoring report; the method comprises the steps of task planning, preliminary investigation of the first investigation unmanned aerial vehicle, suspected sewage draining exit calibration, detection of the second unmanned aerial vehicle mother vehicle, data analysis and confirmation and remote transmission. The system supports real-time data return, realizes high-precision sewage draining exit calibration in combination with infrared laser and Beidou positioning, and performs long-term pollution monitoring through cloud storage and data mining. The system has the core innovation points of double-unmanned-aerial-vehicle air-underwater cooperation, edge calculation and AI fusion, automatic battery changing and endurance and the like, and is suitable for monitoring sewage draining exits in urban inland rivers, lakes and offshore areas.

A system is provided.SOLUTION: The system according to claim 1, further comprising: means for detecting a customer harassment by performing a natural language process on the basis of the converted text data; means for automatically switching from the operator to a AI response when the customer harassment is detected; and means for notifying the customer of the introduction of the system in advance.SELECTED DRAWING: Figure 1

The invention provides an electric shovel production and loading efficiency data acquisition and analysis method, device and equipment. The method comprises the following steps: acquiring electric shovel operation data in real time through a sensor integrated by data acquisition equipment; processing the operation data of the electric shovel in real time to obtain an efficiency index of the electric shovel in each operation link; the efficiency bottleneck of the electric shovel is analyzed according to the efficiency indexes, and time consumption abnormity in the operation process is recognized. According to the application, the data acquisition equipment integrated with various sensors is adopted to acquire various data in the operation process of the electric shovel in real time, and the acquired data is analyzed in real time in combination with the efficient data processing unit, so that the accurate evaluation of the operation efficiency of the electric shovel is realized. The time consumption of the operation links is calculated, the efficiency bottleneck in the operation is identified, the links with abnormal time consumption are found, and intelligent optimization suggestions are provided, so that fine management and continuous optimization of the electric shovel operation are realized, and the overall efficiency and production capacity of the mine operation are remarkably improved.

The invention provides a cloud desktop resource adaptive allocation method considering network bandwidth constraints, and belongs to the technical field of cloud computing and virtual resource management. Aiming at optimization management of various types of resources in cloud computing and virtualization environments, intelligent and refined collaborative scheduling is realized. According to the method, the allocation priority of resources is dynamically judged by analyzing the actual service type and historical bandwidth use characteristics of a user in real time, and multi-resource joint optimization is carried out in combination with a bandwidth sensitivity model, so that self-adaptive efficient allocation of resources such as a CPU (Central Processing Unit), a memory and bandwidth is realized. The system has self-learning and real-time adjustment capabilities, and can effectively improve the resource utilization rate, reduce service delay, slow down network congestion, and improve the overall operation efficiency of the platform and the user experience. The method is suitable for the field of resource management and scheduling in a cloud platform, a big data center and a new-generation virtualization system.

The invention relates to an influence factor-driven multi-model target detection method in a complex dynamic scene, which comprises the following steps of: systematically constructing a quantitative system covering multi-dimensional scene influence factors, and creating an evaluation data set with different factor combination labels according to the quantitative system; performing comprehensive performance evaluation on all candidate models in a pre-constructed lightweight model library by using the data sets so as to determine an optimal model for each typical influence factor combination, and establishing a mapping relation table; for a new target detection task, the scene of the new target detection task is analyzed in real time and quantized to obtain a specific influence factor combination, and then the optimal lightweight model can be quickly matched and loaded to execute detection by querying the mapping table. According to the method and the device, self-adaptive and low-delay model selection and calling of a complex and changeable scene on the end side equipment with limited resources are realized, and the overall performance and robustness of a target detection system in a changeable environment are remarkably improved.

The invention discloses an electronic perspective drawing prompting method based on a knowledge graph, and belongs to the technical field of computers. The method comprises the following steps: performing deep semantic analysis on an electronic perspective drawing request input by a user to extract a keyword set; establishing a dynamic adjustable reference grid in a three-dimensional space; determining a target entity corresponding to each keyword, and determining a plurality of drawing prompt words according to the target entities corresponding to all the keywords; according to the dynamic adjustable reference grid, carrying out spatial relation processing and scale proportion adjustment on the determined drawing cue word; based on the knowledge graph and the dynamic adjustable reference grid, an intelligent perspective correction algorithm is executed, and an accurate correction suggestion is generated; analyzing a drawing behavior and a drawing sequence of the user in real time, and inferring a drawing intention of the user in combination with an expert experience mode in the knowledge graph; and displaying the processed drawing prompt words, correction suggestions and intention inference results on the client in an augmented reality mode.