75results about How to "Facilitate decision-making" patented technology

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention provides a construction method, a control method and a system of a flight control network, and the method comprises the steps: employing a depth deterministic strategy gradient algorithm, building an attitude control network of an unmanned aerial vehicle in a flight process based on the state information and a target flight value of the unmanned aerial vehicle in the flight process, employing a depth reinforcement learning algorithm, and carrying out the attitude control of the unmanned aerial vehicle in the flight process. Based on the state information and the information of the environment where the unmanned aerial vehicle is located, a path planning network and an intelligent avoidance network of the unmanned aerial vehicle in the flight process are built respectively; based on the attitude control network, the path planning network and the intelligent avoidance network, constructing a hierarchical deep reinforcement learning network of the unmanned aerial vehicle in the flight process; and training the hierarchical deep reinforcement learning network by adopting historical flight data of the unmanned aerial vehicle in different flight environments and a preset flight reward mechanism to obtain a flight control network of the unmanned aerial vehicle. According to the invention, the adaptability and flexibility of the unmanned aerial vehicle in the flight process can be improved.

The invention relates to a network data leakage monitoring system and method, and the system comprises a data collection module, a data preprocessing module, an anomaly detection module, a risk assessment module and an alarm module. The data acquisition module is used for acquiring network data from a network data source in real time; the data preprocessing module is used for cleaning and standardizing the acquired network data to obtain preprocessed network data; the anomaly detection module identifies an anomaly mode and calculates a confidence score according to the preprocessed network data by using a pre-trained neural network model; the risk assessment module assesses the potential data leakage risk in real time based on the type of the abnormal mode and the confidence score, and generates a comprehensive risk assessment result; and the alarm module generates and sends alarm information when the comprehensive risk assessment result exceeds a preset threshold value. The system can accurately detect and evaluate the network data leakage risk in real time, and provides effective security alarm.

The invention provides a robot system based on self-adaptive cruise and remote operation and a control method thereof, and belongs to the field of robot inspection, the system comprises a six-axis mechanical arm, an intelligent inspection robot, an automatic inspection task module and a remote teleoperation control unit; the intelligent inspection robot carries a six-axis mechanical arm and an automatic inspection task module, the automatic inspection task module comprises a preset inspection task, the remote teleoperation control unit sends a control instruction of an operator to the intelligent inspection robot through wireless communication, and the control instruction comprises inspection path planning and operation tasks. The intelligent inspection robot performs an inspection task according to a preset inspection task of the automatic inspection task module or a control instruction sent by the remote operation control unit, and the six-axis mechanical arm is used for executing a target operation task. Through integration of advanced sensor technology, image processing technology, artificial intelligence algorithm and wireless communication technology, efficient, accurate and safe inspection of a complex environment is realized.

The invention relates to a data calibration method suitable for an atmospheric pollutant sensor deployed in an embedded terminal. The method comprises the following steps: acquiring original response data of different sensors with different concentrations in different atmospheric environments after temperature compensation, and corresponding temperature, humidity, air pressure data and sampling time; collecting real concentration values of detection objects corresponding to different sensors at corresponding moments given by a national control station or a provincial control station; preprocessing the obtained data to obtain a final data set; a random forest algorithm is used as a teacher model, a gradient elevator algorithm is used as a student model, and hyper-parameters of the gradient elevator algorithm and the random forest algorithm are optimized by using a hedera helix algorithm; substituting a hyper-parameter optimal solution found by optimization into a corresponding algorithm, and retraining the teacher model and the student model by using hyper-parameter combinations; and deploying, updating and displaying the model in the embedded equipment. And the model size is reduced to realize the deployment of the embedded end on the premise of ensuring the accuracy.

The invention discloses a GNN and GRU-based bridge structure time-varying fire response prediction method and system, and the method comprises the steps: building a to-be-detected bridge structure FEM model, randomly generating a fire curve, and generating a data set through the combination of the FEM model and the fire curve; establishing a graph structure according to the FEM model, and embedding static and dynamic characteristics for temperature, stress and displacement prediction in nodes of the graph structure; a message transmission layer is constructed, the GNN is responsible for space information transmission, and the GRU is responsible for time information transmission; simulating a heat conduction formula by using a message passing formula combining the two; receiving node input vectors including temperature, stress and displacement prediction characteristics from the graph structure; determining a loss function and separately training GNN + GRU networks for temperature, stress and displacement prediction; and when the to-be-detected bridge structure encounters a fire, acquiring a fire response condition of the to-be-detected bridge structure through the trained model. The problem that a traditional GNN model can only predict non-time-varying boundary fire response is solved.

The invention provides a financial bill risk value calculation method and device, equipment and a storage medium, relates to the technical field of financial bill risk assessment, and can comprehensively and deeply mine potential risks in financial bills by integrating various anomaly detection models and classification models. The anomaly detection model can accurately identify the anomaly in the data, and provides a high-quality risk data sample for the classification model, thereby ensuring the accuracy and reliability of risk probability calculation. The risk probability is predicted by using the pre-trained risk classification model, so that the calculation efficiency is greatly improved. Compared with a traditional risk assessment method, the method does not need to manually carry out tedious data analysis and judgment, and the risk identification period is remarkably shortened. And the risk probability is converted into the corresponding risk value, so that the risk assessment result is more visual and easy to understand. Risk management and decision making of related financial departments are facilitated, and comprehensive control of financial risk conditions of enterprises is facilitated.

The invention relates to a power grid fluctuation optimization method and system based on air conditioner electric quantity prediction, and the method comprises the following steps: S1, collecting historical power consumption data, including historical weather data and user power consumption behavior data, and measuring and calculating the data of the use condition of an air conditioner in a user region; s2, selecting key weather characteristics influencing the use of the air conditioner, and constructing an air conditioner electric quantity prediction model based on the calculated historical air conditioner electric quantity to predict the future electric quantity demand of the air conditioner; s3, on the basis of the predicted future electric quantity demand of the air conditioner, power grid load optimization is carried out on the basis of a DQN algorithm model; and S4, performing energy scheduling optimization by adopting a heuristic algorithm, maximizing the use of renewable energy sources and the utilization rate of the energy storage system, and providing personalized incentive measures and adjusting peak power load by utilizing user behavior analysis. Electric power fluctuation caused by air conditioner use is effectively optimized, and the stability and efficiency of a power grid are improved.

The invention relates to the technical field of environment monitoring, in particular to a pollution source online monitoring and analysis system and method, and the system comprises a map building unit which is used for carrying out the three-dimensional map modeling of a target monitoring area; the man-machine interaction unit is used for issuing a monitoring control command; the acquisition monitoring unit is used for calculating data sampling density and acquiring environment data; the threshold value early warning unit is used for comparing data to judge whether the pollution source is a suspected pollution source or not; the pollution confirmation unit is used for setting an identification framework for the received environmental data of the suspected pollution source so as to judge whether the suspected pollution source is a real pollution source or not; and the diffusion analysis unit is used for matching the environmental data of the real pollution source with the pollutant diffusion model to obtain the diffusion range of the real pollution source. According to the invention, through environment data acquisition of the unmanned ground vehicle, multiple environment data of a water area and soil can be obtained in real time in a target monitoring area, so that pollution conditions can be found and responded in time, and potential hazards caused by environment pollution are reduced.

The invention belongs to the field of intelligent manufacturing, and provides an intelligent recommendation method and system based on a shared robot, and the method comprises the steps: receiving a picture and a processing demand provided by a user; determining the processing attribute of the target workpiece according to the picture; determining a processing sub-task according to the processing attribute; determining an optimal shared robot combination according to the processing subtasks and the processing requirements; and displaying the optimal shared robot combination to the user.

The invention discloses a refrigerator temperature equalization method and system based on dynamic wind speed control, and relates to the technical field of refrigerator temperature equalization, and the method comprises the following steps: monitoring the standard temperature of each region under the optimal operation state of a refrigerator, and obtaining the temperature equalization data of the refrigerator; acquiring load temperature influence data and fan processing influence data which influence the temperature equalization of the refrigerator; carrying out machine learning on the load temperature influence data and the fan processing influence data to obtain a temperature change suitable model; and controlling the output wind speed and output stop of the fan according to the output of the temperature change suitable model in combination with the temperature uniformity data. According to the invention, the wind speed is accurately controlled, so that the problem that the temperature nonuniformity is influenced by over-high or over-low regulation of the wind speed is solved.

The invention discloses a real-time optimal power flow method based on a generative pre-training model (GPT) and deep reinforcement learning, relates to the technical field of intelligent regulation and control of a power grid, and utilizes technical breakthrough in the field of large language models to directly model non-quantifiable natural language stipulations as targets and constraints in an OPF problem. The method comprises the steps of establishing comprehensive performance indexes, setting contexts and problems, converting GPT agent feedback into reward signals, carrying out iterative sampling, outputting GPT agent results in a JSON format and importing the GPT agent results into a DRL agent, carrying out offline training and online application and the like. Three deep neural networks are used in the DRL process, a cost function is optimized through a primitive-dual method, a series of constraint conditions including language specification constraints are set, and the method can be applied online to solve the OPF problem in real time.

The invention discloses a single-face array low-altitude radar target recognition system based on deep learning, and the system comprises a data collection module which obtains the radar echo data of different targets in various environments, a receiver has a specific sampling frequency and a dynamic range, the collection time is set according to the characteristics of the targets, and the data is compressed and stored; the data preprocessing module is used for denoising, clutter suppression and normalization; the feature extraction module extracts features by using CNN; the target recognition module classifies and recognizes a target; the model training and optimizing module is used for training a model and optimizing parameters; and the visual display module displays the result. According to the invention, data can be accurately collected and efficiently preprocessed, target features are accurately extracted and recognized by using deep learning, the model is optimized to improve the accuracy, the result can be visually displayed, the accuracy, real-time performance and reliability of low-altitude radar target recognition are effectively improved, and the monitoring requirements are met.

The invention discloses an underground pipeline visual analysis method and system based on unmanned aerial vehicle surveying and mapping, relates to the technical field of unmanned aerial vehicle surveying and mapping, and effectively improves the precision of underground pipeline positioning. According to the method, the city outer surface model is established according to the city outer surface image, the laser reflection signal frequency spectrum is divided into a plurality of laser reflection signal segments, and the city underground area point cloud distribution model is established according to each laser reflection signal segment. Connecting the urban underground area point cloud distribution model with the urban outer surface model to obtain an urban visual point cloud distribution model, and dividing a plurality of point cloud data areas with different colors in the urban visual point cloud distribution model according to the absorptivity of each underground substance to laser signals with different frequency bands; and the urban three-dimensional visual pipeline is divided in the point cloud data area.

The invention discloses a financial service platform and method supporting dynamic product access and data backflow, belongs to the technical field of data transmission, and aims to solve the technical problem of how to quickly and flexibly access different financial institution products and realize efficient data backflow. Comprising a user interaction interface, a product access configuration module, a dynamic access engine, a data analysis module, a data backflow module, a security convergence module and a database, rapid access of financial institution products is achieved in a configuration mode, and the development time and cost are greatly reduced; the dynamic access engine can adapt to product interface changes of different financial institutions, and the flexibility and expandability of the system are improved; the data backflow module realizes efficient backflow of data, provides rich data support for a financial service platform, and facilitates data analysis and decision making.

The invention discloses an equipment maintenance management method and device. The method comprises the following steps: collecting vibration data of equipment in different motion states; performing feature extraction and labeling on the vibration data; a training sample is generated according to the extracted feature values and corresponding labeling information, the labeling information is used for indicating equipment states corresponding to the vibration data, and the equipment states comprise a normal state and a specific fault state; training a fault diagnosis model by using the training sample; and performing fault diagnosis on the equipment by using the real-time monitoring data of the equipment and the fault diagnosis model, and determining the current state of the equipment. By means of the scheme, the real-time performance and intelligence of equipment vibration monitoring can be improved, and normal operation of equipment can be better guaranteed.

The invention relates to the technical field of smart power grids, and discloses a regional electric vehicle charging pricing method and device. By analyzing an information interaction mechanism between an agent and an electric vehicle user and a dynamic feedback mechanism under an incompletely symmetrical information condition, a dynamic game structure which takes the agent as a dominant and makes a user follow a response is constructed, and a dynamic relation between a charging behavior of the electric vehicle user and an agent pricing strategy is clarified. And based on a dynamic game structure, constructing a regional electric vehicle charging pricing optimization model, and by taking user cost minimization and agent income maximization as targets, performing game solving by using the regional electric vehicle charging pricing optimization model to obtain an optimal pricing strategy and an optimal charging strategy. The method effectively improves the pricing flexibility of the agent and the charging cost optimization of the user, can achieve the reasonable distribution of charging resources and the optimization management of the operation of a power grid, has a good application value, and can be used for the operation and management of an actual charging network.

The invention relates to the technical field of semiconductor thermal management, and provides a thermal management system of a TMBS semiconductor device, which comprises an acquisition module, a temperature reading module, a simulation module, an analysis module and a strategy execution module, the acquisition module is used for acquiring data related to thermal management when the TMBS semiconductor device runs, the data source comprises but is not limited to temperature sensors, and the temperature sensors are arranged in key regions of running of the TMBS semiconductor device; the temperature reading module is used for reading and processing temperature data of the temperature sensor; the simulation module is used for establishing a thermal model of the TMBS semiconductor device and obtaining a temperature distribution diagram of the TMBS semiconductor device during operation; the analysis module is used for obtaining a temperature distribution diagram marked by colors; the strategy execution module is used for adjusting a heat dissipation system and power output; according to the invention, through cooperative work of a plurality of modules, accurate monitoring and adjustment of the temperature of the TMBS semiconductor device are realized, so that thermal management during operation of the TMBS semiconductor device is ensured to be more efficient and stable.

The invention relates to the technical field of rail transit monitoring, in particular to a rail transit tunnel monitoring and operation and maintenance method and system based on an autonomous robot, and the system comprises the autonomous robot, a sensor network, a data processing center and an operation and maintenance management platform. According to the rail transit tunnel monitoring and operation and maintenance method and system based on the autonomous robot, all-around monitoring including crack detection, leakage monitoring, temperature and humidity measurement, harmful gas detection and the like can be carried out on the structure, environment, equipment and the like in the tunnel, and the functions of autonomous navigation, obstacle avoidance, environment perception and the like are achieved; the system can freely move in a tunnel and carry out various monitoring and operation and maintenance operations, optimizes daily manual inspection, improves the inspection efficiency and safety index, reduces the danger and burden of manual inspection, improves the monitoring reliability and inspection efficiency through big data analysis, reduces the operation and maintenance cost, and is suitable for popularization and application. And tunnel management is promoted to develop towards a more intelligent and unmanned direction.

The invention relates to the technical field of power grid systems. The invention provides a power transmission and distribution line lightning protection equipment quality evaluation method, which comprises the following steps of: pre-defining and setting a unified standard for evaluating the performance of lightning protection equipment through a quality control system, and establishing a real-time monitoring system of the lightning protection equipment; historical data of the lightning protection equipment are collected, a historical data set is established, and a quality evaluation model is constructed through a quality control system; the real-time monitoring system collects related data of the lightning protection equipment to be evaluated and feeds back the related data to the quality control system to obtain a real-time data set; based on the real-time data set, performing quality evaluation on the to-be-evaluated lightning protection equipment through a quality evaluation model, and obtaining a quality evaluation result; and making corresponding operation and maintenance measures according to the quality evaluation result, and feeding back the quality evaluation result and the corresponding operation and maintenance measures to the user, thereby solving the problems of non-uniform evaluation standards, lack of real-time performance in the evaluation process, low evaluation efficiency, high error rate and low evaluation accuracy of the existing lightning protection equipment quality evaluation method.

This invention belongs to the field of artificial intelligence technology, specifically disclosing an intelligent rule conflict detection method, system, and program product. By transforming the traditionally tedious and time-consuming manual rule review into intelligent rule conflict detection, it greatly simplifies the verification process for business personnel in rule design and significantly improves the efficiency of rule review. It can comprehensively and deeply analyze the rule system, automatically identifying logical errors, overlaps, and contradictions that are difficult for the human eye to detect, effectively reducing the risk of rule defects and potential financial losses caused by human negligence. It deeply embeds artificial intelligence technology into the business operation process of rule configuration, ensuring the logical validity of rules once they are created, and promoting the upgrade of enterprise rule management from reliance on manual experience to intelligent automatic detection. Furthermore, it can intuitively display potential problems and conflicts between rules, making the entire rule verification process clearer and easier for users to understand and make decisions.

The present invention relates to a quality detection device for oral liquid of Chinese patent medicine, which integrates pretreatment, infrared spectrum, fingerprint spectrum, ultraviolet visible spectrum detection module and cloud analysis feedback system. Intelligent pretreatment is used to ensure sample suitability, multi-spectral technology is used to comprehensively analyze components, cloud database and random forest algorithm are combined, and types are quickly subdivided according to absorbance, peak position shift and area change are quantified in detail, and finally the quality grade is determined and feedback is given to users in real time. The system improves detection efficiency and accuracy, and strengthens the intelligent level of quality control of Chinese medicine.

The invention discloses a multi-area-array phased array radar target trajectory prediction method and system, and the system comprises a data collection module which is used for receiving target data detected by a multi-area-array phased array radar, a data preprocessing module which carries out the cleaning and preprocessing of the collected target data, removes an abnormal value and noise interference through a data cleaning algorithm, and carries out the prediction of the trajectory of the target. The trajectory prediction module is used for performing trajectory prediction on the preprocessed target data by using a pre-trained deep learning model, the result output module is used for outputting a trajectory prediction result to a user, the trajectory prediction result comprises graphic display, data report form and intelligent optimization, and the trajectory prediction result is obtained according to the feedback of the user on the prediction result and the effect in practical application. And the multi-source fusion module can integrate data of the multi-area phased array radar and other sensors or data sources. The target trajectory is accurately predicted through multi-module cooperation and a deep learning model, result output is visual and diversified, and the accuracy, practicability and user experience of target trajectory prediction are improved.

This application provides a method for predicting the wear state of steel-based ceramic wear-resistant components, relating to the field of material life assessment technology. The method includes the following steps: emitting excitation eddy currents containing a first frequency and a second frequency into a steel substrate, and receiving the response signal modulated by the state of the steel substrate and the ceramic-steel interface; processing the response signals corresponding to the first and second frequencies respectively, extracting the first phase change of the first reference signal corresponding to the initial health state of the component relative to the first frequency, and the second phase change of the second reference signal corresponding to the second frequency; calculating the difference between the first and second phase changes to characterize the change in thermal conductivity state of the ceramic-steel interface; obtaining the real-time operating temperature of the wear-resistant component, and based on the real-time operating temperature and the difference, assessing the current state level of the ceramic-steel interface, and predicting the remaining safe service time of the ceramic wear-resistant layer by combining the trend of the difference over time.

The present invention proposes a BIM-based prefabricated lightweight construction control optimization method and system, the method includes: determining the data source to collect data and preprocessing, integrating the preprocessed data and integrating it into a central database; designing a CAC-GAN model and deploying and optimizing it on the central database; building a DPT model to analyze the output of the CAC-GAN model; designing a reinforcement learning algorithm, using an adaptive learning mechanism to optimize the construction strategy through reward feedback; designing an ICAS model; building a DBMF model; building an SHMPS model; building a decision support model to provide decision support based on fusion data analysis, predicted data and real-time feedback; designing a user interface to access information and interact, and at the same time establishing an adaptive learning model based on a continuous learning mechanism to self-optimize and update according to real-time data and feedback. The present invention greatly improves the efficiency and quality of construction management, and is expected to bring significant technological innovation and benefit improvement to the construction industry.

The invention belongs to the technical field of petroleum coke blending process optimization, and particularly relates to a petroleum coke blending process optimization system, which comprises a multi-dimensional raw material database construction module used for collecting, integrating and standardizing multi-dimensional data including physical and chemical properties, production places, production processes, historical use effects, environmental adaptability and the like of petroleum coke raw materials, a comprehensive raw material database is formed; a deep learning-driven intelligent blending decision-making module which uses a deep learning algorithm to train a blending prediction model, combines a multi-objective optimization algorithm, comprehensively considers product quality, cost benefits, environmental protection requirements and market trends, and generates an optimal blending scheme; advanced technologies such as deep learning, a multi-objective optimization algorithm and automation equipment integration are introduced, so that the whole optimization system can automatically complete the steps of raw material proportioning, conveying, blending and the like in the petroleum coke blending process, and the intelligent level and the production efficiency of the blending process are remarkably improved.

The utility model discloses a large-scale hoisting machinery station foundation settlement monitoring system, which comprises static force level gauges, a liquid storage tank, a data acquisition instrument and a computer, the static force level gauges are continuously arranged in a foundation treatment area at equal height, the adjacent static force level gauges are sequentially connected through a cable, a liquid hose and a gas hose, and the data acquisition instrument is connected with the computer. Each static leveling instrument is matched with the position of a roadbed box at the upper part of the foundation treatment area and corresponds to the middle part of the roadbed box; a foundation pier is arranged outside the foundation treatment area; a static force level gauge is independently arranged on the foundation pier, the static force level gauge is connected with a cable, a liquid hose and a gas hose which extend out of the foundation treatment area, the cable is connected with a data acquisition instrument, the liquid hose is connected with a liquid storage tank on the foundation pier, and the tail end of the gas hose is communicated with the external atmosphere. The utility model aims to provide a monitoring system which does not need manual measurement and indoor calculation, can realize automatic acquisition of multi-point settlement of a foundation, and can calculate differential settlement of associated points.

The invention discloses a multi-system interaction-based distribution network fault graphical command method and system, and relates to the technical field of distribution network fault processing, and the method comprises the steps: automatically detecting the fault information of a distribution network through an intelligent sensing technology, and pushing the fault information of the distribution network to a dispatcher; establishing a unified view model of the power distribution network, and integrating key data for reference of a dispatcher; and automatically processing a fault through an automatic driving interface, and generating an operation ticket. According to the method, the timeliness of fault processing is improved, the power supply interruption time possibly caused by fault processing delay is shortened, a quick response basis is provided for subsequent steps, the information processing efficiency is improved, the control ability of a dispatcher to the state of a power grid is improved, the decision process is optimized, and the decision efficiency of the dispatcher is improved. According to the method, the misjudgment risk caused by information dispersion is reduced, the fault processing accuracy, the fault recovery speed and the safety are improved, human errors are reduced, and stable operation of a power grid is ensured.

The present invention relates to the technical field of roadside sensors, and specifically discloses an automatic joint calibration method for roadside sensors based on a grid map. The roadside sensor includes a millimeter wave radar and a camera. The millimeter wave radar is used to receive radar points of a target, and the camera is used to receive an image. By establishing a millimeter wave radar grid map A and an image grid map B, and by associating the millimeter wave radar grid map A with the image grid map B, the joint calibration of the roadside sensors is achieved. The present invention can automatically complete the calibration without manual work, and has high efficiency; the method of the present invention does not require characteristic markers, and can automatically perform road test sensor calibration by detecting vehicle targets traveling normally on the road, and has low cost.