156results about How to "Quick decision" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention relates to the technical field of traffic management, and discloses a road obstacle clearance rescue enterprise automatic scheduling method comprising the following steps: S1, through multi-source data fusion and a dynamic convolutional neural network, generating a three-dimensional road model, and predicting accidents and congestion; s2, dynamically adjusting the priority of the rescue task by using a multi-target genetic algorithm in combination with real-time data; s3, path planning and cooperative scheduling are optimized through deep reinforcement learning and game theory algorithms; s4, dynamically correcting the path by using a vehicle-mounted sensor, and optimizing the flow and the energy consumption; s5, in an emergency, an emergency module based on reinforcement learning starts a plan and adjusts resources; and S6, after the task, analyzing an optimization strategy through collaborative learning, and carrying out self-improvement. According to the method, multi-source dynamic data fusion is carried out, a dynamic convolutional neural network is used for processing data in real time, a high-precision three-dimensional road environment model is generated, accidents and congestion are predicted, and it is ensured that a rapid and accurate scheduling decision can be made in a complex and changeable road environment.

The invention relates to the technical field of AI computing, in particular to a knowledge reinforcement learning method and system based on multiple agents. Modifying parameters of the large language model by using a supervised fine-tuning training method for the pre-trained large language model; constructing a knowledge base of a large language model, and vectorizing, storing and retrieving texts in various file formats; a multi-agent workflow optimization information processing and decision-making process is developed, a real-time problem is decomposed, a logic chain for solving the problem is constructed, and internet texts are obtained, filtered and summarized. Compared with the prior art, the method has the advantages that the knowledge quantity of the pre-trained large language model in the specific field is increased, the knowledge base is high in flexibility, the obtained text is high in real-time performance, certain interpretability is achieved and the like, and then the ability of the large language model to solve problems in the specific field is improved.

The invention provides a distribution network line dynamic optimization method based on multi-fault mode analysis, and relates to the technical field of distribution network management. Comprising the following steps: acquiring electrical parameters and real-time monitoring data of each fault section in a distribution network line, and acquiring a topological structure, a load demand and historical fault data of the distribution network line; carrying out load distribution optimization on the distribution network lines by adopting a dynamic load optimization algorithm according to the topological structure and the load demand of the distribution network lines in combination with the electrical parameters and the historical fault data, and adjusting the load distribution of each distribution network line based on optimization; and when a distribution network fault occurs, positioning a fault point and implementing a repair measure by combining the topological information of the distribution network and the electrical parameters of the fault section. According to the invention, by constructing the multi-fault mode analysis model, the fault occurrence position of the distribution network is detected in real time, and the fault point is quickly positioned; and in combination with an optimized repair strategy, the fault recovery time is effectively shortened, and the distribution network recovery efficiency is improved.

The invention provides an energy management system based on a cloud and edge integrated optical storage system. The energy management system comprises a cloud module and an edge module, the EMS controller is used for collecting information of the optical storage equipment module and the load module and uniformly sending the collected data to the cloud module; the cloud module predicts future photovoltaic power generation power / electric quantity through a photovoltaic power generation prediction model by receiving data acquired by the EMS controller; predicting a future load through the load prediction model; analyzing peak and valley time periods of the electricity price through the electricity price management model, and sending prediction data to the edge module; and the edge module carries out energy management through the control logic of energy management according to the battery SOC, the chargeable amount, the photovoltaic predicted power and the generating capacity, and superposition peak-valley arbitrage and safe anti-countercurrent logic. Through the arrangement, the whole energy management system has high intelligence and automation levels, energy distribution and use are optimized, and the energy utilization efficiency is improved.

The invention discloses a big data platform system for emergency disposal of overwater oil spill and hazardous chemical substance leakage accidents, and the system comprises a data integration module which is used for collecting and integrating remote sensing data and environmental data from satellites, unmanned aerial vehicles, oceans and meteorological stations in real time; the monitoring module is used for dynamically displaying real-time diffusion conditions and affected areas of spilled oil and hazardous chemical substance leakage; the analysis and prediction module is used for predicting future diffusion paths of oil spilling and leakage based on the collected data by adopting an artificial intelligence algorithm; and the risk assessment module assesses the event risk and provides an emergency response strategy and a resource allocation suggestion. According to the invention, the multi-source data real-time acquisition and integration capabilities of satellites, unmanned aerial vehicles, ocean sensors, meteorological stations and the like are utilized, the data updating speed and the information integrity are improved, the real-time diffusion conditions of oil spill and hazardous chemical substance leakage are dynamically displayed through a map engine and a real-time data processing algorithm, and rapid decision making and resource allocation are supported.

The invention relates to the technical field of territorial space planning, in particular to a territorial space planning index calculation method and system based on a large language model. Deriving and outputting the name of the calculation tool and the actual value of the input parameter of the calculation tool through the large model, and generating a corresponding space calculation instruction; executing the space calculation instruction to obtain a corresponding index calculation result; and inputting the index calculation result and the corresponding first task cue word into the large model, and interpreting through the large model to obtain a corresponding territorial space planning result. By implementing the method, the accuracy, timeliness and reliability of land space planning index calculation can be improved.

The invention belongs to the technical field of power system protection and control, particularly relates to a short-circuit simulation test method and system for a subway direct-current power supply system, and aims to solve the technical problems of insufficient flexibility, difficulty in adapting to complex actual working conditions and lack of adaptivity in the prior art. The method comprises the following steps: presetting and adjusting various short-circuit fault parameters to generate a short-circuit working condition; determining a transient fluctuation separation result based on the short-circuit working condition; constructing a system topology model by utilizing a graph neural network, generating a long-term protection plan in combination with an optimized double-Q learning strategy, and generating an initial risk assessment result by adopting a neural network based on physical information and Monte Carlo simulation; constructing a hybrid risk assessment model in combination with a Bayesian network and fuzzy logic to obtain a target risk assessment result, and generating an emergency response plan based on the target risk assessment result by using a natural language generation technology and a planning algorithm; according to the invention, the precision and reliability of the short-circuit simulation test of the subway direct-current power supply system are improved.

The invention relates to the technical field of safety engineering, in particular to an intelligent high-altitude and fire operation safety management system based on risk assessment, which comprises a safety helmet module, a data acquisition and transmission module, an edge calculation and processing module, a risk assessment and decision module, an emergency response module and a data analysis and optimization module. The intelligent safety helmet worn by each operator is integrated with a flame sensor and a height sensor, and flame information in an operation environment and height changes of the operators can be collected in real time. Through the low-power-consumption wireless communication technology, the collected data is transmitted to the edge computing device in real time, and the edge computing device calculates the comprehensive risk index through multi-sensor data fusion. And when the risk index exceeds a preset threshold value, the system automatically triggers alarm, sends early warning information to operators and a management platform, and takes emergency measures, so that the accident rate in high-altitude operation and fire operation can be effectively reduced, and the safety management level of the operation environment is improved.

The invention relates to the technical field of diabetes, in particular to a diabetes risk prediction model based on a multi-mode large model. The system comprises a multi-data fusion acquisition unit, a model construction selection unit and a user classification unit. According to gene detection and family history data, different models are allocated to predict the diabetes risk, it is ensured that the prediction result is closer to the actual situation of an individual, the family history data are combined, the individual risk can be evaluated more comprehensively, targeted health management suggestions are provided for family members, the family data are integrated into the models, and the risk of diabetes is predicted. The method is beneficial for discovering a family genetic mode and improving the prediction accuracy, and can improve the prediction accuracy through family history data even if no gene signal exists, so that the existing information is fully utilized.

The invention provides a fan fault diagnosis method and system, and the method comprises the steps: installing a sensor at a key part, collecting an acceleration signal, and carrying out the filtering processing; performing time domain and frequency domain analysis, and extracting a root-mean-square value, an amplitude entropy value and an instantaneous frequency value; establishing a model based on the instantaneous frequency to calculate a vibration complexity index; a fault diagnosis model is established in combination with the indexes to analyze the component fault probability; setting a risk threshold, comparing the analysis result and generating a diagnosis conclusion; according to the invention, the accuracy and the real-time monitoring capability of fan fault diagnosis can be improved, the maintenance cost is reduced, the reliability and the intelligent level of equipment are improved, and the user experience is enhanced.

The invention discloses an automatic driving system extreme environment decision-making method based on a neural evolutionary game. The method comprises the following steps: S1, collecting and constructing a real-time environment data set around a vehicle through an automatic driving system; s2, generating an environment feature vector; s3, based on the environment feature vector, analyzing the environment feature data through an extreme environment identification module; s4, activating a corresponding multi-target dynamic reward mechanism based on the extreme environment label, and setting a dynamic reward weight for the neural evolution process; s5, gradually evolving to generate an optimal neural network structure and parameter configuration suitable for different extreme environments; s6, generating a multi-target game strategy in combination with the environment feature vector; s7, generating an optimal driving path, vehicle speed adjustment, obstacle avoidance mode and direction control decision; and S8, inputting real-time feedback data into the neural evolution model, and further adjusting the structure and parameter configuration of the neural network. The automatic driving system can make a safer, more accurate and more efficient driving decision in an extreme environment.

The invention discloses a coal mine hidden disaster-causing multi-source factor superposition disaster-causing risk prediction method, and relates to the technical field of coal mine construction risk prediction. The method comprises the following steps of S01, information acquisition and processing, S02, risk assessment model construction, S03, risk early warning result display, S04, accident risk assessment, S05, risk information three-dimensional visualization, S06, emergency rescue resource management and S07, monitoring and early warning technology application. According to the method, through utilization of a GIS information acquisition platform, multi-source data of a coal mine can be effectively integrated, and the risk of the coal mine can be effectively analyzed; comprising production monitoring, geographic information, safety management and control and the like; the method overcomes the defect of incomplete data acquisition in the prior art, and ensures that the risk assessment can be performed based on comprehensive and accurate data, thereby improving the monitoring capability of the hidden disaster-causing factors of the coal mine.

The invention discloses a traffic meteorological data fusion method based on inverse distance weight interpolation, and relates to the technical field of traffic meteorological data fusion. The traffic meteorological data fusion method based on inverse distance weight interpolation comprises the following steps: acquiring longitude values, latitude values and visibility measurement values of a plurality of time points of each traffic meteorological station in a set area; each traffic weather station in the set area is screened and analyzed based on the visibility measurement values of the plurality of time points to obtain a plurality of effective traffic weather stations, and the data from different time points and places are integrated and analyzed, so that the data utilization rate and accuracy are remarkably improved; the time sequence analysis of the visibility helps to understand the behavior pattern of each station changing with time, the spatial analysis ensures the continuity and consistency of data in geography, and the integration method optimizes the data processing flow, so that the visibility prediction is more accurate, and the complex traffic and meteorological condition analysis is effectively supported.

The invention discloses a coal mine fireproof simulation training method based on a virtual reality technology, relates to the technical field of coal mine fireproof simulation training, is used for solving the problem that a virtual scene and student operation are asynchronous due to sensor data delay or misalignment in the training process, and comprises the following steps: collecting action data of a student in real time; comprise posture change, hand movement and position coordinate information. Seamless synchronization of the sensor and the virtual scene is ensured through the time synchronization algorithm and the dynamic compensation model, and the safety and effectiveness of training are improved. In combination with a sliding window, a prediction algorithm and a redundancy detection mechanism, the system has high fault-tolerant capability, and training continuity and stability are guaranteed. The dynamic path planning under the fire spreading trend is realized by adopting the A * algorithm, and the guidance is quickly adjusted and corrected when the trainee deviates from the path, so that the real-time response capability and the immersion of training are improved, and the trainee is helped to quickly make a decision and act in an emergency.

The invention relates to a target detection method of a tactical AR helmet. Comprising the following steps: a head posture sensing and space positioning module: after a helmet is started, an inertial measurement unit IMU sensor acquires acceleration and angular velocity data of a head through a gyroscope, an accelerometer and a magnetometer; integrating the data of the multiple sensors by using a sensor fusion algorithm including Kalman filtering; creating space mapping between the virtual scene and the real scene; triggering of a target detection module: inputting a real-time video frame captured by a camera into a pre-trained convolutional neural network (CNN) model; the CNN model performs feature extraction according to different detection tasks including target classification and object boundary detection; performing multi-scale detection on a target through multi-layer convolution and pooling operation; generating a plurality of candidate boxes using a regional proposal network (RPN); real-time target tracking and calibration: a detected target position is transmitted to a target tracking algorithm, and tracking is initialized; the size and the position of the tracking frame are adjusted according to the visual angle change of a helmet wearer.

The invention relates to a building supervision system for supervising the real-time construction progress of a commercial complex building, and relates to the technical field of building construction management, the system comprises a real-time acquisition module, a data processing module, a scheduling system module, a display interaction module and an alarm notification module, and the real-time acquisition module, the data processing module, the scheduling system module, the display interaction module and the alarm notification module are automatically acquired through an RFID tag and other automatic acquisition devices. Worker card punching, material use and mechanical equipment working states are monitored in real time, data comprehensiveness and accuracy are ensured, data acquisition efficiency is improved, human errors are reduced, an integrated data processing center analyzes construction progress in real time, management personnel can make decisions quickly, the decision process is accelerated, management efficiency is improved, and work efficiency is improved. The system automatically adjusts resource configuration according to real-time data and preset rules, optimizes working efficiency, avoids resource waste, reduces cost, automatically gives an alarm and notifies related personnel when the efficiency is reduced, ensures that problems are solved in time, reduces delay risks, and displays the construction progress through a visual user interface.

The invention discloses a mechanical arm fault prediction system based on digital twinning, and belongs to the technical field of mechanical arms. The system comprises a digital twin model construction module, an experimental simulation module, a data collection module, a model training module, a model test module, a fault prediction module, a prediction result output module, a result analysis module, an alarm prompt module, an aging prediction module, a durability analysis module, a digital model display module and a durability identification module for each part. The mechanical arm fault prediction platform is constructed on the basis of the digital twinning technology by means of cooperation of the multiple component state identification modules, the pre-arranged plan execution module and the pre-arranged plan setting storage module, and the faults, the aging degree and the durability of all the components of the mechanical arm can be predicted.

The invention relates to the technical field of data analysis and support, and discloses a landscaping intelligent decision support system based on big data analysis. The system specifically comprises a greening data acquisition module used for acquiring growth state data and soil state data of greening plants in a transition area in a garden, and a morphological evolution degree evaluation module used for analyzing and evaluating morphological change conditions of the greening plants in the transition area in the garden, the resource balance degree evaluation module is used for analyzing and evaluating resource occupation conditions among green plants in a transition area in a garden; the decision support model construction module is used for constructing a decision support model; and the decision suggestion output module is used for converting decision support coefficients generated by the decision support model into maintenance suggestions of landscaping. The problem that in the prior art, when plant varieties need to be replaced in landscaping, the growth state and soil conditions of greening plants in a garden transition area cannot be comprehensively monitored in real time is solved.

The invention belongs to the technical field of greenhouse gas emission evaluation, and discloses a method for evaluating pasture digestion and ruminant livestock greenhouse gas emission of grazing type artificial mixed sowing grassland. According to the system, the pasture digestion efficiency of the grazing type mixed sowing grassland and the greenhouse gas emission of ruminant livestock can be accurately evaluated, and the accuracy and timeliness of data are improved. And automatic data collection and analysis reduce the requirements of manpower and physical resources, simplify the operation process and reduce the management cost. The implementation of the system improves the efficiency of grassland management, and helps farmers or managers to quickly make data-based decisions. Environmental influence evaluation and management improvement: by evaluating the environmental influence of different grazing strategies and grassland combinations, the system is helpful for optimizing grazing management and reducing greenhouse gas emission, thereby making a contribution to ecological environment protection.

The invention provides an unmanned aerial vehicle intelligent adaptive scheduling method and system based on a large model, and the method comprises the steps: carrying out the type selection of a large model according to the specific task demands and computing resources of an unmanned aerial vehicle system, and carrying out the fine adjustment of a specific task field of the selected large model; integrating a plurality of professional models for executing different task requirements in the unmanned aerial vehicle system; for different task scenes, large model analysis judges whether a specific professional model needs to be activated or not, and the professional model is selected for task processing when needed; the large model confirms a professional model needing to be called, and automatically extracts and transmits required parameters to the professional model based on the collected environment information and task instructions; and the professional model receives the parameter information transmitted by the large model, executes the corresponding subtask, and feeds back an execution result to the large model, so that the large model performs continuous learning optimization. According to the invention, the unmanned aerial vehicle can complete the task more quickly, and the accuracy and reliability of task completion are improved.

The invention discloses a civil aviation fault monitoring method and system based on artificial intelligence. The method comprises the steps of data acquisition, preprocessing, delay function construction, civil aviation fault monitoring model establishment and civil aviation fault monitoring. The invention belongs to the field of fault monitoring, and particularly relates to a civil aviation fault monitoring method and system based on artificial intelligence, and the method comprises the steps: introducing a dynamic delay dynamic neural network equation, and simulating the dynamic behavior of a civil aviation system; a plurality of delay intervals are adopted to describe delay characteristics under different working conditions, a more comprehensive state monitoring means is provided for civil aviation, and the safety and stability of civil aviation operation are ensured; whether the civil aviation system is in a normal operation state or not is judged through a delay passivity criterion, and fault monitoring fast decision is achieved; by combining delay, dynamic adjustment and passivity criteria, the state of the civil aviation system can be comprehensively evaluated, the capability of coping with a complex fault mode is provided, and then the fault monitoring efficiency is improved.

The invention provides a method and a system for generating an operation report based on enterprise index assets, electronic equipment and a storage medium, and aims to solve the problems that the analysis of the operation report is time-consuming and incomplete, and the index cannot be selected autonomously, the method comprises the following steps: carrying out index storage on business index data of an enterprise through a graph database, the incidence relation between the indexes is stored; identifying a user intention and recommending a diagnosis direction and a diagnosis index according to the user intention in an intelligent question and answer interaction mode; and outputting a diagnosis conclusion by utilizing LLM according to the recommended diagnosis indexes, and generating a visual diagnosis report by adopting an AIGC technology and index graphical assets. The diagnosis indexes can be quickly determined according to the user intention, and the conclusion can be diagnosed through the LLM, so that business personnel can perform comprehensive diagnosis based on the indexes more flexibly, and the diagnosis report can be generated more efficiently.

The invention relates to the technical field of financial intelligent systems, and provides a digital employee intelligent system based on a financial system, and the system comprises a data collection and processing module which is used for enabling a client to act firstly when the client initiates a transaction request, consults a question or has other business demands; the natural voice processing module is used for converting the voice of the client into a text through voice interaction and a voice recognition function, then analyzing the text content through a text understanding function, and extracting the intention and key information of the client; and the intelligent decision-making module is used for performing judgment and processing by a rule engine according to a preset service rule when facing a simple service request of a client, and performing risk assessment and client classification by a machine learning algorithm according to historical data when facing a complex service request of the client. Through the intelligent decision-making module, various complex service requests can be quickly and accurately processed, manual intervention is not needed, and the decision-making efficiency and the decision-making accuracy are greatly improved.

The invention discloses a power transmission line external damage detection method based on integrated target detection and incremental learning, and the method comprises the following steps: obtaining abnormal object picture data of a power transmission line, carrying out the data enhancement of the abnormal object picture data, and recording the data as enhanced data; establishing a hybrid model, and recording the hybrid model as an integrated model; training the enhanced data by adopting an integrated model, and recording the data as integrated data; deploying the integrated data to an edge server for detecting an abnormal object in a real-time image on the power transmission line; picture data with abnormal objects in the edge server is uploaded to a cloud server to serve as a new training sample for incremental learning, and the integrated model is updated; repeating the steps to realize continuous optimization of the system; if no abnormal object picture data exists, ending. According to the invention, through combination of incremental learning and an integrated target detection technology, accurate and timely detection of abnormal objects near the power transmission line is realized.

The invention provides a wireless communication method and a battery management system based on wireless communication. The method comprises the following steps: generating a plurality of communication paths according to the communication quality between a plurality of battery cell management units and a battery management unit; wherein each battery cell management unit is used for collecting working data of a battery pack and sending the working data to the battery management unit; controlling the battery management unit to generate a control instruction based on the received working data, and forwarding the control instruction to the battery cell management unit through the communication path; wherein the battery management unit is in wireless communication with each battery cell management unit; controlling the cell management unit to perform cell management on the connected battery pack based on the received control instruction; wherein the battery cell management unit is connected with at least one battery pack, and each battery pack comprises a plurality of single battery cells. According to the invention, the reliability of communication between the battery management units can be improved.

The invention relates to the technical field of information security, and discloses an AI-based information security dynamic analysis system and method, and the system comprises an instruction driving module, a data driving module and a dynamic analysis module which are in communication connection in sequence, and an analysis log module which is in communication connection with the instruction driving module, the data driving module and the dynamic analysis module. The method corresponds to the system. According to the invention, systematic integration and collaboration of an information security dynamic analysis process are realized; an analysis rule is continuously updated based on a time sequence, so that the system can timely adapt to the change of a safe environment, and accurate analysis is carried out according to the change; through monitoring of the security nodes and intelligent instruction generation, rapid decision making and action taking can be ensured at the key nodes, by means of recording and logic presentation of the operation process, traceability of system operation and continuous optimization of security strategies are achieved, accuracy and timeliness of dynamic analysis of information security and overall protection efficiency are greatly improved, and the system has a wide application prospect. And complex and changeable network security threats can be effectively dealt with.

The invention relates to the technical field of ship intelligent management, and provides a ship control management method and system based on intelligent analysis, and the method comprises the steps: S1, deploying designated equipment, including data collection equipment, in a divided region of a ship, so as to monitor the operation environment of the ship and the behaviors of sailors in real time, and collect corresponding ship data; s2, the ship control terminal and the ship equipment realize cooperative work to receive ship data, carry out data two-way transmission with a shore command center or surrounding ships, receive and send an instruction and carry out remote monitoring; and S3, the ship control terminal receives and analyzes the ship data, identifies a safety warning in real time, evaluates an operation risk and generates a corresponding response measure. The problems that an existing ship management system is low in data exchange speed and limited in risk prediction and automatic response capacity are solved, the automation level of ship operation and the accuracy of real-time risk assessment are remarkably improved, and the safety and efficiency of ship operation are effectively enhanced.