322 results about "Stream analytics" patented technology

Power flow in a power distribution system is controlled using control commands generated based on values of system parameters. In this regard, generation prediction data, load prediction data, and real-time device status of equipment are utilized to generate scheduling data for power generation and power consumption and a network topology of the system is also obtained. The values of the system parameters are generated as an outcome of an unbalanced load flow analysis of the power distribution system using the scheduling data and the network topology. The unbalanced load flow analysis utilizes a compact multi-bus based nodal admittance model to represent relationships between nodal injected currents and nodal voltages at a plurality of non-overlapped phases of buses for a section formed by zero impedance branch connected with impedance branches in the power distribution system.

The invention discloses a maritime traffic flow analysis method based on big data, and relates to the technical field of information, and the method comprises the steps: collecting data related to maritime traffic, and carrying out the preprocessing; performing adaptive dynamic division on the sea area by using the processed data to generate a real-time updated water area boundary; based on the self-adaptive division result, analyzing a ship behavior mode to obtain an anomaly detection result; analyzing interaction among the ship groups according to an abnormal detection result, and predicting potential congestion and conflicts; based on the group behavior prediction result, ship path planning is optimized, and an optimal route suggestion is generated; by constructing a space-time diagram convolutional network model and comprehensively considering relative positions, speeds and rendezvous frequencies of ships, potential navigation conflicts can be accurately predicted; according to the method, the optimal route suggestion based on multi-objective optimization is provided for the ship by optimizing path planning, so that the sailing efficiency and the economical efficiency are improved to the maximum extent on the premise of ensuring the safety.

The invention relates to a multi-modal fusion real-time digital human driving method based on unified behavior vector mapping, and belongs to the technical field of human-computer interaction. The method comprises the following steps of: 1) extracting multi-modal features of voice, action and vision, and generating input features by adopting language feature-emotion decoupling, staged action modeling and macroscopic-micro expression flow analysis; (2) cross-modal collaborative alignment and conflict correction are carried out, and high-precision fusion is realized through knowledge graph constraint time sequence decomposition, particle-by-particle interactive fusion and a situation-driven modal dominant strategy; and 3) constructing a three-dimensional behavior vector space, mapping the multi-modal features to a unified coordinate by utilizing honeycomb grid projection, and driving the digital human to output in combination with a coordinate-action mapping table. According to the method, the problems of difficulty in multi-modal time sequence alignment, feature isomerism and out-of-control conflict are solved, real-time interaction of naturalization, emotional and scenarized is realized, and the expressive force and adaptability of the digital human are remarkably improved.

The invention discloses an intelligent archive opening and identifying method based on a large model, particularly relates to the technical field of archive data auditing, and is used for solving the problems of insufficient cross-modal data analysis capability, lagging rule updating and low man-machine cooperation efficiency in the prior art. Fusing cross-modal features of texts, images and metadata through a hybrid expert model to generate multi-modal feature vectors, and dynamically allocating the multi-modal feature vectors to a rule network, a semantic network and a domain network for cooperative processing based on attention weights; the rule network parameters are optimized through gradient projection constraint, and regulation-driven real-time adaptation is achieved; matching sensitive data in combination with a multi-dimensional feature matrix of auditing personnel, and optimizing task allocation accuracy; removing redundant links by utilizing value flow analysis to generate a lightweight process, and recording as a tamper-proof evidence chain through a block chain evidence storage solidification operation; the auditing efficiency and accuracy are improved, and the compliance traceability is guaranteed.

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

The invention discloses a space-ground integrated dynamic traffic flow analysis and road topology reasoning method and device. A cloud server generates and issues real-time traffic information through multi-level processing such as map matching, traffic flow analysis, prediction and early warning and road topology analysis and updating; specifically, firstly, the high-precision positioning and communication capability of a low-orbit satellite is utilized, data fusion of an inertial measurement unit and a wheel speed sensor is combined, and a multi-source fusion algorithm is constructed to improve the positioning precision; secondly, traffic flow dynamic analysis and prediction are carried out based on a Transform interaction analysis model, and real-time updating of a road topological structure is realized at a cloud end by using a clustering algorithm and a graph theory model; finally, the traffic flow and road topology information is sent to related vehicles and a traffic management system, and real-time monitoring, early warning and optimization of the traffic state are achieved. According to the invention, the accuracy and real-time performance of traffic information acquisition, processing and issuing can be effectively improved, and the intelligent level of traffic management and decision making is enhanced.

The invention provides a carbon flow analysis traceability calculation method based on source network load storage integration, and relates to the technical field of data processing, and the method comprises the steps: obtaining the carbon emission data of various types of energy from a power supply side at a power grid side, and calculating the carbon emission in a power grid transmission process according to a carbon loss factor in the power grid transmission process; on the load side, carbon emission in the power grid transmission process is obtained, and the carbon emission on the load side is calculated according to the carbon consumption factors of the loads; and at the energy storage side, obtaining the carbon emission in the power grid transmission process and the carbon emission at the load side, and calculating the carbon emission at the energy storage side according to the charging and discharging efficiency of the energy storage equipment and the corresponding carbon emission factor. According to the method, the data processing efficiency is improved, errors caused by manual operation are reduced, and the data accuracy is improved.

The invention provides an AI-based carbon asset and carbon inspection management method and platform, and belongs to the technical field of carbon emission management, and the method comprises the steps: inputting the operation activity data of a target object into a preset energy efficiency analysis model, and obtaining an energy efficiency analysis result; based on the energy efficiency analysis result, performing energy flow modeling and analysis on the operation activity data of the target object to obtain an energy flow analysis result; the energy flow analysis result is input into a preset energy efficiency balance optimization model, an optimized energy distribution scheme and carbon emission prediction values, predicted based on the energy distribution scheme, of all links are obtained, and the energy efficiency balance optimization model is configured as follows: under the condition that the production constraint condition of the target object is met, the energy distribution scheme is optimized; optimizing energy distribution by taking minimization of the total carbon emission as a target; comprehensively calculating the carbon emission predicted value of each link to obtain a target total carbon emission predicted value of the target object; and generating a corresponding target carbon asset management strategy according to the target total carbon emission prediction value, the carbon transaction market data and the carbon sink data.

The invention discloses a space-time carbon emission prediction method, system and device and a medium. The method comprises the steps of obtaining load historical sequence data; carrying out load flow calculation by adopting a first load flow analysis algorithm to obtain a load flow calculation result; based on the load flow calculation result, carbon flow analysis is carried out through power transmission between the nodes, and the carbon emission coefficient of each node is obtained; constructing a time sequence diagram neural network model, and performing preliminary training on the time sequence diagram neural network model; performing hyper-parameter optimization on the preliminarily trained time sequence diagram neural network model by adopting a first hyper-parameter optimization technology to obtain an optimized time sequence diagram neural network model; and inputting load sequence data and unit power output data at the current moment into the optimized sequence diagram neural network model, and carrying out online prediction on a carbon emission coefficient. According to the method, the real-time prediction of the carbon emission coefficient is realized based on the characteristic learning of the historical operation and monitoring data of the power system.

The invention discloses a code reconstruction method and system based on data stream slicing and large language model driving, and belongs to the field of software engineering. The method comprises five parts of code preprocessing, data stream analysis, construction of cue words of a large language model, function testing and code review. According to the method, firstly, codes needing to be reconstructed are preprocessed and converted into a unified format; secondly, performing data stream analysis on the preprocessed codes, and dividing independent logic blocks; generating cue words according to a data flow analysis result and historical behaviors, and inputting the cue words into a large language model to obtain reconstructed codes; and finally, the codes before and after reconstruction are tested, and whether re-reconstruction is needed is detected, so that reconstruction of the long method codes is realized, and the accuracy and efficiency of code function division and method extraction are optimized.

The invention relates to a house building construction stage carbon emission dynamic estimation method and system based on full life cycle data, and the method comprises the steps: dividing a construction region based on a BIM model, laying a multi-source sensor network, fusing static BIM data and a dynamic Internet of Things monitoring flow, building a four-dimensional space-time label data set, and carrying out the construction stage carbon emission dynamic estimation based on a double-layer dynamic correction model. Kalman filtering is utilized to eliminate instantaneous interference, LSTM transfer learning is combined to predict mechanical efficiency attenuation, a dynamic correction coefficient matrix of a mechanical aging rate, operator skills and environmental factors is embedded, and a carbon emission value is calibrated in real time; when abnormal emission is detected, a key pollution source is traced and positioned through a graph neural network, a three-dimensional thermodynamic diagram is generated in the digital twinborn model, and component-level carbon flow analysis and early warning are performed, so that the carbon emission estimation precision is remarkably improved, the abnormal traceability time consumption is shortened, and the timeliness is remarkably improved.

The invention relates to the technical field of data processing, and discloses a BNCT digital twinborn model construction method, a BNCT digital twinborn model dynamic optimization method, BNCT digital twinborn model dynamic optimization equipment and a medium, and the construction method comprises the following steps: carrying out data assimilation processing on obtained multi-physical field simulation data to generate equivalent measured data matched with physical characteristics of historical measured data, generating a data assimilation training set based on the historical measured data and the equivalent measured data; wherein the multi-physical field simulation data represents simulation data of the accelerator and the beam shaping device in various physical processes; based on the data assimilation training set, a first sub-model used for representing the physical process of the accelerator and a second sub-model used for representing the neutron beam analysis process are obtained through training; and constructing the BNCT digital twinborn model according to the first sub-model and the second sub-model obtained by training. According to the technical scheme provided by the invention, the calculation precision and the processing complexity of the BNCT digital twin system can be improved, and real-time response is ensured.

The multi-source data automatic collection method based on the large language model comprises the steps that feature distribution of historically collected data is analyzed, and an optimal collection strategy is dynamically generated in combination with the state of a current data source; scheduling the acquisition task in real time to obtain a dynamically scheduled data stream; performing real-time anomaly detection and self-adaptive compensation on the dynamically scheduled data stream to obtain a self-fault-tolerant data stream; analyzing entity association and service logic of the self-fault-tolerant data stream, and predicting potential data demand points; automatically expanding a data acquisition range to obtain knowledge-enhanced data assets; identifying protocol features of the data transmission stream of the data assets, and analyzing the data by an analyzer based on protocol feature mapping to obtain protocol-independent standardized data; and performing semantic noise filtering and cross-modal cleaning to obtain semantic pure data. According to the invention, the defect that protocol adaptation and adaptive strategy optimization cannot be carried out in the existing multi-source data acquisition technology is overcome.

Power flow in a power distribution system is controlled using control commands generated based on values of system parameters. In this regard, generation prediction data, load prediction data, and real-time device status of equipment are utilized to generate scheduling data for power generation and power consumption and a network topology of the system is also obtained. The values of the system parameters are generated as an outcome of an unbalanced load flow analysis of the power distribution system using the scheduling data and the network topology. The unbalanced load flow analysis utilizes a compact multi-bus based nodal admittance model to represent relationships between nodal injected currents and nodal voltages at a plurality of non-overlapped phases of buses for a section formed by zero impedance branch connected with impedance branches in the power distribution system.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention relates to the technical field of micro-grid scheduling, and discloses a time-sharing carbon flow-based micro-grid scheduling method, system and device and a storage medium, and the method comprises the steps: inputting power load data and characteristic data into a carbon flow calculation model, and calculating the carbon flow of a micro-grid; according to the energy consumption of the enterprise, calculating other energy carbon flow of the enterprise, and according to the power consumption period, adding the micro-grid carbon flow and the other energy carbon flow to obtain a time-sharing carbon flow data sequence; and performing carbon flow analysis on the time-sharing carbon flow data sequence, determining a scheduling period, constructing an optimal scheduling model of the micro-grid according to the scheduling period and the carbon flow calculation model, and solving the optimal scheduling model to obtain an optimal scheduling strategy. Optimized scheduling is carried out according to carbon emission conditions of different time periods, organic unification of operation of the micro-grid and operation of an enterprise is achieved under the low-carbon target, and carbon emission of the enterprise is indirectly reduced by improving the participation degree of new energy elements at the specific moment and optimizing the energy structure.

Disclosed in the present invention is a people flow analysis system based on Internet of Things information acquisition. The system comprises a data acquisition module, a data storage module, a people flow statistics module, an obstruction analysis module, a coupling analysis module and a predictive analysis module. The data acquisition module acquires all people flow data of a laboratory server room by means of an image acquisition apparatus; then, the people flow statistics module separately performs crowd density analysis, wherein an average distance is determined by means of hierarchical and clustering analysis of head labeling points, and further a Gaussian function is used to determine the size of heads, thus solving the problem of uncertainty caused by a nearest neighbor method when crowd densities are low and uneven; the obstruction analysis module performs feature fusion on features of an obstacle in a coverage space and neighboring crowds to obtain a new data set; the predictive analysis module performs overall people flow predictive analysis on the basis of the new data set and people flow data of crowds not adjacent to the obstacle. Therefore, the accuracy of people flow analysis and control efficiency are greatly improved.

The invention discloses a deep reinforcement learning optimization method for injection molding process parameters, and belongs to the technical field of intelligent manufacturing. The method comprises the following steps: constructing a dynamic causal graph network through information entropy flow analysis and transfer entropy calculation, and revealing a causal relationship and time delay characteristics among process parameters; manifold learning is adopted to map a high-dimensional parameter space to a low-dimensional manifold, and Riemannian metric guide optimization search is constructed based on the quality gradient; generating enhanced state representation fusing causal association and manifold geometric information; identifying a production element state and selecting a corresponding optimization strategy; a geodesic line is planned in a manifold space to obtain an optimal parameter adjustment path; historical experience is utilized through memory retrieval and case adaptation; cross-task knowledge migration is realized; adopting a depth deterministic strategy gradient algorithm to optimize the decision; and online learning is realized through elastic weight consolidation. According to the method, the problems of black box decision, slow convergence, difficulty in knowledge reuse and the like in the prior art are solved, the optimization efficiency and the interpretability are improved, and the method has the capability of quickly adapting to new tasks.

The invention relates to the technical field of ERP code generation, and discloses an ERP code generation method and system for business flow analysis, and the method comprises the steps: carrying out the formalized processing of an enterprise business flow through employing a business flow modeling symbol, and obtaining a business flow chart; performing serialized representation on the business flow chart, and performing similarity measurement on the first business flow sequence; and performing enhancement processing on the business process sequence in combination with the similarity, and matching the enhanced business process sequence with an ERP code template to obtain a business process ERP code. According to the central relation of enterprise business nodes in the business flow chart, the importance degree of each modeling result in the business flow chart is calculated, traversal based on the importance degrees is carried out, multiple groups of business flow sequences are obtained, word vector sequence representation, enhancement processing and analysis are carried out on the business flow sequences, and the business flow chart is obtained. And obtaining a corresponding code attribute category, and selecting a corresponding ERP code template to carry out ERP code matching to obtain an ERP code corresponding to the service flow.

The invention discloses an intelligent data storage management system which comprises an intelligent data management platform, the intelligent data management platform comprises a pooling storage identification module, a data classification module, a data migration module and a flow-saving analysis module, and the invention relates to the technical field of data management. According to the intelligent data storage management system, hot data, cold data and cloud backup are matched, the data are stored in a proper position according to the access frequency and the use requirement of the data, the data storage efficiency and the access speed are improved, meanwhile, the safety and the reliability of the data are ensured, and through access frequency monitoring, frequency sorting and dynamic standard drawing, the data storage efficiency is improved. According to the method, classification can be automatically carried out according to the access frequency of the data, the accuracy and reliability of the data are ensured while the working efficiency is improved, powerful support is provided for intelligent migration of the data, and intelligent management of data storage is achieved while the method has the advantages of being high in storage efficiency, high in access speed, intelligent in backup and intelligent in migration.

The invention discloses an interrupt-driven program defect detection method based on neural symbol fusion, and relates to the technical field of interrupt-driven program defect detection, which comprises the following steps: step 1, a concurrent analysis stage of supporting interrupt: aiming at a program P to be analyzed, carrying out parallel analysis on the program P; a program point set S with defects is recognized through preliminary static analysis, an information set Q which is difficult to process accurately through static analysis is collected, and a foundation is laid for subsequent analysis; the method specifically comprises the following three sub-steps: sensitive point analysis: positioning a program point set S with defects in a program by utilizing a data flow analysis technology according to a defect type T to be detected; according to the interrupt-driven program defect detection method based on neural symbol fusion, the semantic comprehension ability of a large language model and the comprehensive path coverage ability of static analysis are fully combined, and the limitation of a traditional detection method in the aspects of processing loop logic, array subscript analysis, bit operation solving and the like is effectively overcome.

The invention discloses an ARM (Advanced RISC Machines) many-core-oriented x86 instruction conversion data synchronization optimization method, which comprises the following steps of: when a multi-thread executable file is executed in a dynamic instruction conversion mode, constructing a data synchronization optimization module in an ARM many-core system, inserting a synchronization instruction by a program flow analysis unit according to a program flow analysis result, and executing the multi-thread executable file according to the synchronization instruction. A data synchronization window manager inserts a synchronization instruction for a function and a related shared data instruction according to data synchronization related function execution time, and a synchronization instruction combination optimization unit combines the inserted synchronization instruction to realize data synchronization and optimization of an ARM instruction sequence obtained by conversion. According to the method, the performance and the efficiency of data synchronization of the x86 program in the ARM many-core system are improved, the synchronization overhead is reduced, the correctness of program execution and the data consistency are ensured, and an innovative solution is provided for efficient operation of a cross-architecture program.

The invention relates to the technical field of data analysis, in particular to a portable energy storage power supply evaluation method and system, and the method comprises the following steps: collecting the voltage, current, temperature and charge and discharge frequency data of a power supply through a sensor, recording a data flow in a local database in real time, analyzing the current working state of the power supply, and generating real-time performance monitoring data. According to the invention, the fine change of the battery capacity can be more accurately captured by monitoring the power supply performance and environmental factors in real time, so that the future capacity change of the energy storage power supply can be more accurately predicted, and meanwhile, the environmental temperature and humidity data are taken into consideration, so that the power supply is allowed to be subjected to more accurate performance evaluation and optimization; key external variables influencing the battery performance can be recognized, so that the key external variables are considered when a power supply use strategy is designed, optimal operation of a power supply system is ensured, dynamic adjustment of a charging and discharging strategy reflects quick response to real-time data feedback, the overall performance of the power supply is optimized, and the service life of the power supply is prolonged.

The invention relates to a static analysis-based Pascal-like binary program structured information extraction method, which specifically comprises the following steps of: (1) preliminarily reading basic attributes in a binary file compiled by a Pascal-like by a script and other tools, (2) finding all functions for creating class objects through an obtained construction characteristic function and a control flow analysis technology, and (3) extracting the structured information of the Pascal-like binary program. (3) recursively identifying a member object definition relationship in a binary file and an inheritance relationship between classes by taking a basic object address as a base address and combining a data analysis technology, (4) confirming a corresponding construction function name and a corresponding parameter type and name by combining a result of script automatic generation, and (5) searching a corresponding class interface according to an analysis result, and performing classification and classification on the corresponding construction function. And the code restoration logic is consistent with the original binary file behavior. Accurate restoration of class structures, interfaces and functions is achieved, and therefore the efficiency and accuracy of reverse analysis are improved.

The invention provides a strategic key mineral resource comprehensive evaluation and decision support service method and system, and the method comprises the steps: S1, carrying out the data collection and preprocessing, and constructing a digital twinborn model; s2, carrying out whole industrial chain material flow analysis; s3, estimating the waste quantity and the recovery quantity of resource products, and calculating the stock quantity; and S4, performing comprehensive evaluation and early warning according to the calculated content, and visually displaying the data. According to the invention, digital modeling and simulation of the whole process of strategic key mineral resources from exploration and exploitation to recycling are realized, a data barrier of the whole industry chain is broken through, and a system solution of the whole life cycle is provided.

The invention discloses a blade meridian flow field prediction method based on explicit-implicit mechanism mining coding, and relates to the technical field of turbine blade design, the method comprises the following steps: obtaining meridian plane grids and design working condition parameters of a blade runner on an axial-flow compressor described by using a graph data structure; utilizing the trained blade meridian flow field prediction model to predict physical field data of each node of the meridian plane grid of the blade flow channel according to the meridian plane grid of the blade flow channel and the design working condition parameters; according to the method, the implicit mechanism mining mechanism is used for mining the implicit differential association relationship in the physical data as the data feature in the data feature extraction process of the graph neural network model, and the turbine flow analysis mechanism is explicitly encoded into the model loss function; based on the model loss function, a strategy for optimizing the blade meridian flow field prediction model through back propagation is trained, the blade meridian flow field prediction precision is improved, and the model generalization and the result confidence coefficient are improved.

The invention discloses a cross-province stream pulling analysis method, which comprises the following steps of: determining provinces suspected of active stream pulling and passive stream pulling by analyzing the inflow and outflow of IDC (Internet Data Center) and MAN (Mobile Area Network) of each province; for the province suspected to actively pull the flow, determining an address field suspected to pull the flow by analyzing a downlink flow rate TOPn address field of the province suspected to actively pull the flow; for the suspected pull flow address field, flow velocity and flow proportion analysis is carried out, and a suspected pull flow address pair is determined; carrying out pull flow message feature analysis on the suspected pull flow address pair; and if the pull flow message characteristics are met, determining that the address pair has the pull flow behavior. According to the method, the pull flow behavior is determined according to the settlement rule, the change of the flow and the message characteristics of the pull flow, and the accuracy of cross-province pull flow analysis is improved.

The invention relates to the technical field of intelligent navigation, in particular to a position guide device and method based on mobile cloud passenger flow data, and the device comprises a cloud server which is used for collecting and processing anonymous passenger flow data from a mobile terminal in real time and generating a dynamic thermodynamic diagram; the indoor and outdoor positioning module determines the current position of the user through a multi-source fusion positioning technology; the navigation terminal is used for generating an optimal navigation route through a path planning algorithm according to the user preference and the real-time passenger flow density and outputting navigation guidance; according to the method, the real-time passenger flow analysis and dynamic path planning technology is adopted, the congestion area can be accurately predicted and avoided, the navigation efficiency is greatly improved, and special requirements such as barrier-free navigation are supported by combining an AR (Augmented Reality) navigation interface with a personalized recommendation algorithm, so that different user groups can obtain customized navigation experience.

A system and method for privacy-preserving workflow analysis that monitors user interactions with an application interface while maintaining data privacy. The system generates event data that excludes sensitive content while preserving workflow sequence information, enabling reconstruction and analysis of user workflows without exposing protected information. Event data is stored and used to reconstruct workflow sequences, which can be presented through a user interface for analysis, training, and compliance monitoring. The system supports multiple privacy contexts and can substitute synthetic data during workflow reconstruction, allowing organizations to analyze business processes without compromising sensitive information. The system enables comprehensive workflow monitoring and analysis while addressing privacy requirements in regulated environments, supporting use cases including quality assurance, training, and process optimization.

A method for providing automated agent performance improvement includes obtaining a ranking for each agent of a plurality of agents; obtaining customer-agent interactions for each agent; determining a task label for each of the customer-agent interactions; selecting a first set of customer-agent interactions from the customer-agent interactions corresponding to a first task label; receiving application event streams corresponding to the first set of customer-agent interactions and comprising agent application usage data; analyzing the application event streams for statistically relevant differences for one or more activities performed by a first group of agents compared to a second group of agents; determining at least one activity of the one or more activities that is performed by the first group of agents differently than the second group of agents; generating guidance corresponding to the least one activity for the second group of agents; and deploying the guidance to the second group of agents.