7542 results about "Real-time data" patented technology

The invention provides an electric vehicle battery health state monitoring and service life estimation method and system, and the method comprises the steps: S1, obtaining a real-time data flow of a battery during the operation of a vehicle, extracting the original records of charging and discharging depth, temperature change, internal resistance parameters and capacity data from the real-time data flow, a dynamic monitoring sample is calculated through voltage, current and temperature values collected by a sensor; s3, calculating a capacity attenuation rule through the time sequence feature set, analyzing the decrease amplitude of the capacity after each charge-discharge cycle by adopting an exponential attenuation model, obtaining a long-term operation trend from historical data, and obtaining a dynamic curve of capacity attenuation; and S8, if the confidence interval of the final estimation probability distribution is greater than a preset threshold value, recollecting data with higher frequency for the dynamic monitoring sample, and performing iterative optimization on the life estimation model through the updated sample to obtain an adjusted life detection result.

The invention belongs to the technical field of coal mine risk early warning, and particularly relates to a coal mine goaf multi-risk comprehensive early warning method and system based on machine learning, and the method comprises the steps: collecting mine pressure, gas and hydrological real-time data in real time through a multi-temporal-spatial-scale sensor, and obtaining a dynamic coupling relation basic data set based on the real-time data; preprocessing noise and missing values according to the dynamic coupling relationship basic data set, and modeling node connection between a geological structure and mine pressure change by adopting a graph neural network to obtain space-time heterogeneous feature representation; non-linear features are analyzed through spatial-temporal heterogeneous feature representation, and a multi-scale dynamic mode is determined; acquiring a risk conduction path in the multi-scale dynamic mode, and acquiring an early recognition signal of a potential disaster chain; based on the early recognition signal, a long-short-term memory network is used for processing a sequential sequence, and the probability of the compound disaster is judged; a high-risk area is extracted from the composite disaster probability, and real-time early warning model parameters are obtained; and generating alarm output according to the real-time early warning model parameters.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses an unmanned ship multi-agent collaborative obstacle avoidance method and system for complex sea conditions, and relates to the field of unmanned ship multi-agent collaborative obstacle avoidance, and the method comprises the steps: obtaining the real-time data of each unmanned ship and the surrounding environment; generating a candidate obstacle target point cloud cluster list based on a density clustering algorithm; on the basis of a Kalman filter, real-time absolute motion state estimation of the candidate obstacles is obtained, and an obstacle feature list is output; determining a safety radius compensation amount required by autonomous obstacle avoidance of each unmanned ship; obtaining the safe sailing space of each unmanned ship at the current moment; constructing a global synthetic potential field, and generating a group of optimal alternative paths of the unmanned ship from the current position to the target point; on the basis of adopting a consensus binding algorithm, an unmanned ship multi-agent collaborative obstacle avoidance path reaching a consensus is obtained. The method has the advantages that safe, efficient, cooperative and consistent intelligent obstacle avoidance of a multi-unmanned-ship cluster in a real complex marine environment is realized.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention relates to the technical field of new energy automobile electric control, and discloses a new energy automobile electric control fault prediction system. The system comprises a real-time data acquisition module, a dynamic fault prediction model construction module, a fault difference calculation module, a multi-dimensional anomaly analysis module, a fault probability positioning module and a self-adaptive maintenance strategy module. The real-time data acquisition module acquires sensor data of the electric control system in real time; the dynamic fault prediction model construction module constructs a dynamic fault prediction model based on historical fault data; the fault difference calculation module inputs real-time data into the model and outputs theoretical fault indexes; the multi-dimensional anomaly analysis module compares the theory with the actually measured fault indexes to generate an anomaly difference matrix; the fault probability positioning module inputs the matrix into a space correlation network to generate a fault probability distribution diagram; and the adaptive maintenance strategy module configures maintenance parameters according to the distribution diagram. According to the system, the fault prediction accuracy and real-time performance can be improved, and stable operation of the new energy automobile electric control system is guaranteed.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a power distribution room anomaly detection system based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. In order to solve the problems of high network bandwidth pressure, insufficient edge computing capability, low anomaly detection accuracy, difficulty in multi-source data fusion and the like caused by the adoption of an end-cloud direct connection architecture in an existing power distribution room monitoring system, the system comprises: a data acquisition layer configured with various heterogeneous sensors to acquire operating parameters and environmental data in real time; the edge storage and calculation layer carries out local real-time processing, anomaly detection, model training and visual display, an anomaly detection module of the edge storage and calculation layer carries out research and judgment on real-time data to generate early warning information, and a prediction and detection linkage module monitors an anomaly probability trend and adjusts a sampling frequency; the edge gateway realizes protocol conversion and data forwarding; and the cloud decision-making layer aggregates multi-edge node data, optimizes a global model through federal learning, and issues and updates a local model. The system is used for improving the accuracy, real-time performance and reliability of anomaly detection of the power distribution room, reducing the operation and maintenance cost and realizing intelligent operation and maintenance.

The present disclosure describes systems and methods for detecting and preventing data misconfigurations within a security-focused data fabric platform. The system integrates an advanced script migration engine designed to streamline the translation of security rules and scripts across different scripting languages while ensuring alignment with the fabric's unified schema. The method involves receiving inputs from data sources, mapping these inputs to entities of a target schema, monitoring real-time data changes, and simulating impacts on operational dependencies to detect misconfigurations proactively. Leveraging AI-driven mechanisms, including Large Language Models (LLMs), the system dynamically identifies breaking changes in third-party data streams, issues alerts, and provides suggested fixes. The script migration engine further enhances the platform's functionality by automating cross-platform script translations and enabling faster onboarding of security tools. Together, these innovations ensure scalable, accurate, and resilient integration and management of security data across heterogeneous sources, strengthening operational integrity and minimizing security risks.

The invention provides a new energy automobile battery thermal management method and system based on big data. The method comprises the steps that real-time data flow and a historical temperature change curve in a battery pack are collected in real time through a distributed sensor array; inputting a pre-trained time sequence prediction model, outputting a predicted temperature change curve and calculating a real-time temperature rise slope; obtaining an environment comprehensive compensation amount according to the environment temperature and the battery health state, and subtracting the environment comprehensive compensation amount from the basic safety threshold value to obtain a dynamic safety threshold value; determining a dynamic temperature compensation amount through a preset slope grading mechanism, and subtracting the dynamic temperature compensation amount from the dynamic safety threshold to obtain an advanced intervention temperature point; and when the temperature of the battery pack reaches the advanced intervention temperature point, a graded cooling system is started, and cooling power grades are dynamically switched according to the growth interval where the real-time temperature rise slope is located. The battery temperature is accurately controlled, and the energy consumption is remarkably reduced.

The invention discloses a precision compensation method of a composite numerical control machine tool, and particularly relates to the technical field of precision control of the numerical control machine tool, which comprises the following steps: synchronously acquiring temperature, vibration and force information under the driving of a unified clock through multiple types of sensors deployed at key nodes of the machine tool, and generating a synchronous multi-source sensing data set with aligned timestamps; inputting the data set into a multivariable coupling error model obtained through data driving training, and calculating and generating a comprehensive space volume error predicted value of a tool nose point of the tool; performing inverse calculation based on a machine tool kinematic chain model, and generating a multi-dimensional micro-compensation instruction set containing the compensation amount of each motion axis and the sequential relationship; and finally, dynamically writing the instruction set into a servo control ring of the numerical control system through a real-time data interface, and driving each motion shaft to execute synchronous compensation motion. According to the method, comprehensive compensation of multi-physics field coupling errors such as temperature, vibration and force load is achieved, and the machining precision and stability of the numerical control machine tool under complex working conditions are improved.

The invention discloses a laser processing parameter autonomous generation system and method based on digital twinning, and relates to the field of digital twinning, and the method comprises the steps: collecting and preprocessing processing data in real time through a multi-source sensor; processing and analyzing the task instruction by using a natural language, extracting a constraint condition and forming a structured demand; a processing parameter candidate set is generated through a Transform model in combination with historical data transfer learning, and virtual processing is performed by means of multi-physics field simulation; an improved non-dominated sorting genetic algorithm is adopted to dynamically optimize the parameter weight, and a global optimal parameter combination is obtained through digital twin iteration verification; and after full-process virtual processing verification and task demand comparison, parameters are adaptively corrected, and model parameters are continuously optimized according to physical and simulation data deviation after actual processing. The method has the advantages that the digital twin is used as a core, multi-source real-time data, the AI algorithm and multi-physical field simulation are fused, and autonomous generation, multi-target optimization and virtual-real closed-loop iteration of laser processing parameters are achieved.

The invention belongs to the technical field of decision optimization, and relates to an enterprise-level intelligent risk control decision system combined with a real-time data stream, and the system comprises a heterogeneous data distribution module which is used for obtaining multi-source heterogeneous data streams inside and outside an enterprise; the behavior time sequence splicing module is used for executing cross-system user ID association and time sequence recombination on the real-time data flow to generate a user behavior chain with continuous time stamps; the feature fusing calculation module is used for receiving the user behavior chain, performing feature extraction and outputting a real-time feature vector with a quality flag bit; the incremental model updating module is used for respectively generating a baseline risk score and a dynamic risk score; and the dynamic weight decision module is used for generating final decision parameters. And the risk control processing execution module responds to the final decision parameter to trigger a processing action, and configures a manual auditing arbitration channel and a feedback data generation unit. According to the method, the problems that a dual-check algorithm is not deeply coupled with a business index, and abnormal data which passes hash check but has logic violation flows into a real-time channel are solved.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention provides a performance optimization and regulation method for a high-temperature sintering furnace, and belongs to the technical field of intelligent regulation, and the performance optimization and regulation method comprises the following steps: arranging sensors in each heating area of the high-temperature sintering furnace, and monitoring the heating areas in real time; constructing a heating area operation state database, comparing the collected real-time data with a preset sintering process parameter range, and analyzing to obtain a performance analysis set of each heating area; establishing a three-dimensional heat conduction model, simulating heat flow distribution of each heating area under different heating powers and temperature settings, determining a thermal coupling effect of each heating area in different stages, and determining a heat transfer coefficient between adjacent heating areas; and an initial strategy of the high-temperature sintering furnace is determined, the initial strategy is optimized based on the auxiliary parameters of all the heating areas, and all the heating areas of the high-temperature sintering furnace are regulated and controlled according to the optimization strategy. The problems of inaccurate temperature control, single parameter analysis, insufficient thermal coupling effect processing and the like in the traditional regulation and control technology are effectively solved.

Disclosed is a digital twin-based fault diagnosis method for logistics mobile equipment, capable of achieving online monitoring of equipment state and fault diagnosis by means of fusion of digital twin technology and sensor data, thereby allowing for proactive fault prevention and precise maintenance, improving the reliability and operation efficiency of logistics equipment and prolonging the service life thereof. The technical solution of the method comprises: step 1, on the basis of logistics mobile equipment, establishing a corresponding digital twin model; step 2, acquiring real-time data of a battery entity for adaptive model updating; step 3, inputting fault information into a bidirectional GRU model having a multi-head attention mechanism; step 4, performing outlier detection on the basis of an improved fuzzy isolation forest algorithm, comprising introducing a type-2 fuzzy theory into a fuzzy isolation forest algorithm and making improvements; and step 5, using the obtained fault data to implement corrective measures on the basis of risk levels.

The invention provides a dam leakage intelligent identification method based on multi-modal fusion and knowledge enhancement, and the method comprises the steps: collecting real-time data of a multi-source sensor disposed at a key part of a dam in a preset monitoring time period, and generating seepage characteristic data; identifying a seepage form entity based on the seepage characteristic data and extracting an instantaneous characteristic entity, and associating the entity into a structured knowledge unit according to a space-time proximity principle; knowledge units are classified according to spatial positions and influence ranges, association rules of the knowledge units are complemented, and knowledge graph construction is achieved; and then, a map inference engine is triggered in a real-time feature matching mode, and graded early warning is implemented. According to the method, physical enhanced seepage characteristics are constructed, seepage forms, dynamic characteristics and inducements are deeply associated by utilizing a knowledge graph technology, accurate diagnosis and reasoning from data abnormity to seepage types, causes and risk levels are realized, and finally, the seepage characteristics are analyzed and analyzed through a dynamic conflict resolution and self-evolution mechanism. And a reliable dam leakage intelligent identification and decision-making system is formed.

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to the technical field of electric power intelligent management, and discloses a cooperative scheduling method and system for a virtual power plant, and the method comprises the following steps: S1, collecting the real-time data of each distributed power supply, each load and an energy storage system in the virtual power plant, and carrying out the ultra-short-term prediction, and obtaining a prediction parameter; s2, dynamically calculating the dynamic operation boundary of the energy storage system based on the real-time state of the energy storage system; s3, on the day before the current operation day, generating a pre-scheduling plan through collaborative decision making of a multi-target fuzzy satisfaction function; s4, in the current running day, taking the pre-scheduling plan as a reference, updating boundaries and prediction parameters in a rolling manner, and generating a real-time scheduling instruction through model prediction; and S5, monitoring the deviation between the actual output of each resource and the real-time scheduling instruction in real time, and when the deviation exceeds a threshold value, starting a collaborative deviation compensation mechanism to carry out power balance. According to the invention, fine cooperative scheduling of different types of distributed resources can be realized in a complex environment with high uncertainty.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention discloses a bus duct full life cycle health management system based on digital twinning, and relates to the technical field of health management. The multi-source sensor collects operation parameters and static information, the digital twin modeling and mapping module constructs a physical model and associates real-time data, and a thermal-electric coupling equation is used for simulating temperature; the state monitoring and fault diagnosis module compares data to judge states and diagnoses faults by means of methods such as a fault tree, the health assessment and decision making module constructs an index system to assess health and makes a maintenance decision, the data management and interaction module stores data and realizes visual interaction and system integration, and the intelligent optimization module performs intelligent optimization based on operation and maintenance data. And optimizing model parameters and a decision strategy. According to the invention, intelligent health management of the bus duct is realized, multi-source data acquisition is accurate and comprehensive, fault diagnosis is more accurate and prediction is more timely through combination of digital twinning and an algorithm, and intelligent assessment assists scientific maintenance decision; and the operation and maintenance efficiency and the power transmission stability are improved through system integration and edge calculation.

The invention provides an equipment state deviation identification method based on self-supervision and incremental learning, and the method comprises the steps: S1, obtaining time sequence data of equipment in a fault-free state, and constructing a normal state model; s2, during operation, deviation detection is carried out on real-time data through the normal state model, and a deviation degree index is obtained; s3, comparing the deviation degree index with a preset threshold value, and judging an abnormal event; s4, determining new normal state data by manually verifying the abnormal event; and S5, updating the normal state model according to the new normal state data. The method does not need to depend on a fault sample, establishes an equipment normal behavior model through self-supervised learning, introduces a deviation index to quantify a state difference, and combines manual feedback and incremental learning to form a closed loop, so that the model has self-adaptability and long-term evolution ability.

The invention relates to the technical field of lithium battery safety management, and discloses a lithium battery fault diagnosis method based on a BMS, and the method comprises the steps: obtaining multi-dimensional battery operation data and real-time data, firstly extracting a local feature vector, generating a preliminary fault signal indication, then extracting an abnormal feature vector according to the preliminary fault signal indication, and carrying out the fault diagnosis according to the abnormal feature vector; and if the preset threshold is exceeded, compressing and transmitting to the adjacent management unit to form a shared data packet. According to the local feature vector and the shared data packet, updating a diagnosis model parameter to obtain an optimized fault recognition model for analyzing real-time data and calculating a fault matching degree, and determining a potential fault type if a threshold value is exceeded; and generating a collaborative query request to the distributed network to obtain a historical fault empirical data set, integrating the historical fault empirical data set, refining parameters to obtain an accurate fault probability, activating an alarm and recording a log if a warning threshold is exceeded, and finally updating the global shared knowledge base. According to the method, the problem of insufficient lithium battery fault diagnosis accuracy in a distributed scene is solved, and collaborative optimization of diagnosis accuracy and distributed collaboration is realized.

The invention relates to the technical field of construction control, and particularly discloses a method and a system for dynamically regulating and controlling the installation precision of an inclined-span steel box arch bridge based on machine learning. Wherein the real-time data comprises real-time stress of the arch rib, real-time deformation of the arch rib, real-time environment wind speed and real-time hoisting parameters; constructing a prediction model capable of analyzing the coupling effect of the wind load and the hoisting unbalance load based on a large amount of historical construction data; inputting real-time data into the prediction model to obtain a real-time coupling effect analysis result; outputting a hoisting sequence optimization instruction based on the decision engine, the coupling effect analysis result and a preset mechanical constraint condition of the obliquely-spanning steel box arch bridge; based on the deviation between the real-time deformation amount of the arch rib and a preset installation precision threshold value, a cable force grading adjustment scheme is generated; construction efficiency of the inclined-span steel box arch bridge is effectively improved, construction safety and installation precision are guaranteed, and stability and reliability of the bridge structure are guaranteed.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention belongs to the technical field of multi-source heterogeneous data processing and intelligent strategy self-adaption, and discloses a real-time data stream classification and security strategy self-adaption system based on an AI model, which comprises the following steps: acquiring a multi-source heterogeneous data stream, injecting five-dimensional semantic tags, complementing an implicit relationship among the tags through association reasoning, and generating a standardized enhanced data stream; the optimal AI model is matched to execute real-time data flow classification, credibility grading is carried out, and a candidate strategy set is generated through matching; matching an association rule through a rule relation graph, and combining system real-time state mirror image execution strategy influence chain rehearsal to generate a rehearsal verification strategy set; cross-domain transactions are packaged according to cross-domain transaction description specifications, and transaction execution states and physical feedback data are collected in real time through four-stage submission protocol execution; life cycle management is implemented through rule efficiency evaluation, and a rule evolution instruction and sample enhancement data are obtained by combining full-link auditing and are fed back to a preorder link to form a closed loop.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention discloses a software development task intelligent allocation method and system based on a multi-dimensional capability portrait, and the method comprises the following steps: 1, obtaining multi-source development data generated by a developer in a development process and demand description data of a to-be-allocated software development task, and carrying out the preprocessing, and forming a structured data set; according to the method, a multi-dimensional ability portrait covering technical ability, project experience, collaboration attributes and performance is constructed, a privacy-protected distributed learning mechanism is adopted for dynamic updating, dominant and implicit requirements of tasks are analyzed in combination with natural language processing, complexity and dependency are calculated, and the performance of the performance is improved. A dynamic task feature vector corresponding to a capability feature vector dimension is constructed, and meanwhile, a self-adaptive adjustment mechanism based on real-time data monitoring and online learning is designed to form data closed-loop feedback, so that the problems of one-sided capability evaluation, staticizing task demand analysis and lack of the self-adaptive adjustment mechanism are comprehensively solved; and accurate and intelligent distribution of software development tasks is realized.