63results about How to "Enable predictive maintenance" patented technology

The invention provides an intelligent operation and maintenance method and system for a photo-thermal power generation system based on the Internet of Things, and relates to the technical field of information technologies, and the method comprises the steps: obtaining temperature, humidity and vibration environment parameter data collected by a sensor disposed on power generation equipment; and receiving the environmental parameter data, comparing the environmental parameter data with a preset threshold value, and judging whether the environmental parameters exceed a normal range or not. According to the method, environmental parameters such as temperature, humidity and vibration of the equipment are collected in real time by arranging the sensors, when the parameters exceed the preset threshold value, performance parameter collection is triggered, weighted average calculation is carried out in combination with the environmental parameters, and the equipment health index is obtained; according to the method, intelligent assessment and predictive maintenance of the health state of the power generation equipment are realized, the operation reliability and the maintenance efficiency of the equipment are improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

The invention provides an intelligent operation and maintenance method and system for an air conditioning system based on the Internet of Things technology, and the method comprises the steps: processing operation data through employing an intelligent sensor and an edge calculation node, and generating a comprehensive operation information packet through combining with environment parameters; next, simulation analysis is carried out through a cloud digital twin model, performance prediction and fault point evaluation are generated, the fault probability is evaluated by using a Bayesian network algorithm, and personalized operation and maintenance suggestions are generated; secondly, optimizing operation parameters by adopting a genetic algorithm, and smoothly adjusting the setting through a self-adaptive control theory; and when an abnormality is detected, the fault severity is judged by using a multi-level threshold value and fuzzy logic, an early warning mechanism is triggered in time, and an emergency response scheme is pushed. And finally, recording a maintenance process and user feedback, updating the digital twin model, forming a closed-loop operation and maintenance system, and ensuring efficient and stable operation and service quality improvement.

The invention relates to the technical field of battery testing, and discloses a truck lithium battery testing method and device based on Bluetooth communication. The method comprises the following steps: performing intelligent remote matching processing on Bluetooth identification information of a truck lithium battery management system to obtain a remote Bluetooth control key, and performing lithium battery data acquisition protocol test processing to obtain a dual-channel acquisition instruction set; performing dynamic voltage, current and temperature cooperative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery working condition characteristic spectrum; remote parameter linkage calibration processing is carried out on the multi-dimensional battery working condition characteristic spectrum, an intelligent calibration control sequence is obtained, remote charging and discharging management test processing is carried out, and a battery running state monitoring record is obtained; and performing remote health assessment processing on the battery running state monitoring record to obtain a lithium battery performance test report. According to the lithium battery testing scheme, physical connection is not needed, operation is easy and convenient, and the real-time monitoring capability is achieved, so that the testing efficiency and flexibility are improved.

The invention discloses a digital twin-driven photovoltaic module monitoring and self-purification system, belongs to the technical field of photovoltaic module cleaning, and realizes intelligent management and maintenance of a photovoltaic system through mutual cooperation of a sensing layer, an information capturing layer, an information transfer layer, an intelligent analysis layer, a decision support layer and an interactive experience layer. The sensing layer deploys various sensors to collect key data of the photovoltaic module; the information capturing layer transmits the data to a back-end processing system in real time; the information transfer layer ensures efficient transmission of data in the system; the intelligent analysis layer predicts a maintenance demand by using a machine learning algorithm, realizes predictive maintenance, and maintains the optimal working state of the component through an intelligent cleaning mechanism; the decision support layer provides decision support based on data analysis for an operator of the photovoltaic power station, and optimizes a maintenance plan and a productivity plan; and the interactive experience layer provides a visual management interface, so that the component state can be remotely monitored conveniently.

The invention discloses a pump station unit equipment predictive maintenance method based on a dynamic threshold surface. The method comprises the following steps: acquiring unit operation historical data information of a pump station unit; carrying out data cleaning and treatment on the obtained unit operation historical data information, and dividing the data into a plurality of data sets according to data types and working conditions; establishing a polynomial relationship between the state parameters and the operation condition based on the plurality of divided data sets to obtain dynamic thresholds of different state parameters; combining the obtained dynamic threshold value with an actual experience threshold value and a standard threshold value, displaying in a three-dimensional space, and constructing a dynamic threshold value model; and inputting the obtained real-time state monitoring data into the dynamic threshold model to obtain the running state of the current pump station unit equipment, and outputting a predictive maintenance result. According to the method, the dynamic threshold surface model combining historical health state sample data statistics and actual operation experience is used for evaluating the state of the pump set equipment in time, and predictive maintenance of the equipment is achieved.

The invention provides a wind turbine generator fault diagnosis method and system based on multiple types of symptoms, and the method comprises the steps: carrying out the structural expression of the obtained knowledge of a wind turbine generator based on an ontology theory, and storing data through a graph database, so as to generate a fault diagnosis knowledge base; real-time operation parameters of the unit are detected, and threshold type symptoms and trend type symptoms are extracted from the multiple target parameters through a preset GMM-based symptom extraction model and a BiLSTM network-based symptom extraction model; inputting the multiple fault symptoms into a knowledge base to carry out deterministic reasoning based on a fault knowledge ontology, and carrying out uncertainty reasoning based on evidence theory fusion by utilizing the multiple fault symptoms; and determining a fault mode and a fault reason of the unit by combining diagnosis results of the deterministic reasoning and the uncertainty reasoning. According to the method, multiple fault symptoms and reasoning modes are combined, and the fault mode and the fault reason of the unit under the complex working condition can be quickly and accurately diagnosed.

The invention relates to the technical field of data processing, and discloses a BIM-driven steel structure collaborative design method, device and equipment. The method comprises the steps of performing parameterization processing on building information data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data; performing intelligent structure analysis processing on structure information in the collaborative design data to obtain optimized structure data; performing adaptive component design processing on the optimized structure data to obtain customized component data; performing virtual construction simulation processing on the customized component data to obtain construction optimization data; intelligent operation and maintenance processing is carried out on the construction optimization data and the actual construction data, full-life-cycle management data are obtained, and the full-life-cycle management data comprise real-time monitoring data, performance evaluation results and predictive maintenance suggestions. The efficiency and accuracy of steel structure collaborative design based on BIM driving are improved.

The invention discloses an equipment management method and system based on deep learning, and relates to the technical field of equipment maintenance, and the method comprises the steps: collecting equipment operation data; preprocessing the equipment operation data to obtain a sample data set; training a deep learning model by using the sample data set, wherein the deep learning model can learn a change rule of an equipment state; using the trained deep learning model to analyze the newly collected equipment operation data, and predicting the residual service life of the equipment; and performing equipment management based on the predicted residual service life of the equipment. In the specific implementation process, the equipment operation data comprises sensor data, environment data and maintenance data. According to the method, the equipment operation data are collected, and the fault occurrence time of the equipment is predicted through the deep learning model, so that predictive maintenance is realized, and the availability and reliability of the equipment are improved.

The invention discloses an intelligent extension control module compatible with a Siemens Sinumek ONE system, the intelligent extension control module is integrated in a Siemens Sinumek ONE numerical control system, historical data and real-time data generated in a machining process are analyzed based on a machine learning algorithm, and the numerical control machining process is optimized by adjusting machining parameters; the intelligent expansion control module acquires system data in real time through a Sinumerik ONE communication interface, and performs data processing and analysis by using edge computing equipment of Siemens or a cloud platform to realize dynamic parameter adjustment; the method is not only suitable for optimization of single equipment, but also can realize global optimization and predictive maintenance in a multi-factory and multi-equipment cooperative working environment, so that the flexibility, the stability and the production efficiency of a production system are greatly enhanced.

The invention discloses a regional carbon emission reduction potential analysis method based on electric power big data, and relates to the technical field of carbon emission reduction potential analysis. Comprising the following steps: S1, a data acquisition step: acquiring electric power related data, including electric power energy consumption data, electric power energy production data and carbon emission data, of a to-be-analyzed area within a preset time period based on electric power big data; s2, a data preprocessing step: constructing an energy consumption intensity learning curve based on the power related data obtained in the S1; s3, an optimization step: optimizing parameters of the energy consumption intensity learning curve obtained in S2 based on a particle swarm optimization algorithm improved by a sparrow algorithm; and S4, based on the optimized energy consumption intensity learning curve obtained in S3, analyzing the carbon emission reduction potential of the target area. The analysis and application of the electric power big data provided by the invention promote the digital transformation of energy management, and through an intelligent tool and system, the refined management and predictive maintenance of energy consumption can be realized, and the energy efficiency is further improved and the emission is reduced.

The invention relates to the field of power system monitoring, and discloses a fault diagnosis and positioning lightning protection system for a power plant line, and the system comprises distributed optical fiber sensors which are disposed along the power line and are used for monitoring the temperature, strain and sound data of the line in real time; an optical time domain reflectometer for collecting backscattered signals along the fiber optic line to determine the location of loss and reflection events; the data processing unit is provided with a digital signal processor and a machine learning model, and is used for analyzing the data collected from the optical fiber sensor and identifying a fault mode; and the fault positioning module is used for fault positioning based on time difference. By integrating an advanced optical fiber sensor and an artificial intelligence algorithm, the physical state of the power line can be monitored in real time, and the system not only can identify a current fault, but also can predict possible problems in the future. The high-precision and quick-response fault detection capability greatly reduces the loss and risk caused by fault response delay.

The invention discloses an intelligent building management analysis system based on an AI large model, and relates to the technical field of management analysis systems.The intelligent building management analysis system comprises a statistics module, an analysis module, a link module and a function module, the statistics module is used for collecting virtual information, space information and user information in an intelligent building; the link module is used for performing interface API adjustment and data transmission path regulation and control on the management analysis system of the intelligent building; the function module is used by a user for direct application of the intelligent equipment and overall management of the management analysis system; according to the intelligent building management analysis system, seamless mutual linkage and data transmission between the intelligent building management analysis system and other different AI large models can be achieved through the conversion interface, a user can select the needed AI model to operate and use all intelligent devices in the intelligent building where the user is located, and multiple requirements of modern city development are met.

The invention relates to the technical field of power systems and communication, and discloses an intelligent power grid data transmission system and method of a narrowband Internet of Things, and the system comprises a network coverage enhancement module, a low-delay data processing module, a low-power-consumption management module, and an intelligent load management and data analysis module. The method and the device are used for realizing the stability of data transmission in a signal-limited environment. By introducing the multi-network hybrid transmission architecture and deploying the intelligent antenna and the small base station, the signal coverage range and the transmission stability can be remarkably improved in a signal limited or complex environment, and the multi-network hybrid transmission architecture can determine an optimal network according to real-time signal quality, delay and packet loss probability, so that the stability and the reliability of data transmission are ensured, and the data transmission efficiency is improved. The beam forming of the intelligent antenna and the optimized arrangement of the small base station effectively solve the problem that the signal of the traditional NB-IoT is weak in a building dense area or a remote area by improving the signal gain and the coverage depth.

The invention relates to the technical field of wind driven generators, in particular to a shaft current early warning method applied to a wind driven generator, and the method comprises the steps: arranging an acquisition module for a generator shaft in a wind turbine generator to obtain the operation data of the generator shaft, and carrying out the preprocessing of the operation data of the generator shaft; extracting multivariate features of the generator shaft operation data, wherein the multivariate features comprise a mean value feature, a standard deviation feature, a kurtosis feature and a skewness feature; and a shaft current early warning degree model is defined, the shaft current early warning degree model is solved by combining the multivariate features of the generator shaft operation data, the early warning degree of the generator shaft current is obtained, and graded alarm of the generator shaft current is executed. Through multivariate feature extraction and the early warning degree model, intelligent and accurate monitoring of the shaft current abnormity is realized, predictive maintenance of the wind turbine generator is realized, the reliability of equipment is improved, and the service life of the equipment is prolonged.

The invention relates to the technical field of early warning disposal systems, in particular to a bridge monitoring and early warning disposal system. Comprising a multi-sensor beam body structure state data sensing unit, a multi-mode high-reliability data transmission communication unit, a cloud data storage unit, a cloud data analysis unit, a cloud data early warning unit and an on-site emergency concurrent multistage processing unit. The multi-sensor beam body structure state data sensing unit is used for sensing beam body structure state data; the multimode high-reliability data transmission communication unit is used for transmitting data and realizing signal communication; the cloud data storage unit is used for storing data at the cloud; the cloud data analysis unit is used for performing data analysis at the cloud; the safety of bridge operation and driving in special weather is ensured, a reliable and effective monitoring and early warning means is provided for the bridge service condition, emergency disposal of an emergency scene of the bridge service condition is achieved, and the idea of integration of bridge pier and bridge structure operation monitoring and emergency disposal is innovated.

The invention relates to the technical field of high-voltage switch cabinet temperature measurement, in particular to a wireless multi-point temperature measurement system and method for a high-voltage switch cabinet, and the system comprises a housing, and an acquisition module, a control module, a transmission module and an energy storage module which are used for protecting the interior. The acquisition module is used for accurately acquiring temperature data from each temperature measurement point in the high-voltage switch cabinet and transmitting the temperature data to the control module for processing; the control module is used for acquiring the temperature data acquired by the acquisition module and processing the temperature data; the transmission module is used for transmitting the acquired temperature data to a remote monitoring center; and the energy storage module is used for obtaining electric energy from the high-voltage power grid through electromagnetic induction and carrying out energy storage and management. The beneficial effects of the invention are that the system can achieve the real-time monitoring of the temperatures of a plurality of key parts of the high-voltage switch cabinet, and completely covers all important parts. By adopting the wireless sensor and the communication technology, the system avoids the complex wired wiring requirement, and the installation process is greatly simplified.

The invention discloses a nitrification machine multi-dimensional state monitoring and health management method and device, and relates to the field of nitrification machine state monitoring and health management. A full-process technical framework for multi-dimensional state monitoring and health management of the nitrification machine is provided, and equipment state monitoring parameter selection, multi-dimensional data acquisition, weak fault feature extraction, fault diagnosis, fault early warning, health state evaluation and technical verification are covered; a nitrification machine fault diagnosis model combining deep learning and an attention mechanism is provided, data and a fault mechanism are fused, and the interpretability of a fault diagnosis result is improved; a frequency spectrum combined beta-VAE model is proposed to carry out nitrification machine fault characteristic signal difference degree detection, and intelligent early warning is realized; a health state evaluation model fusing a fault mechanism is provided, and a reference is provided for health degree evaluation of fire-related equipment; predictive maintenance of the nitrification machine is achieved, work of man-machine isolation, machine replacement and black light factories is promoted, and intelligent transformation of fire-related enterprises is accelerated.

The invention relates to the field of equipment detection, in particular to an automatic charging and discharging and parameter monitoring system and method for a direct-current storage battery. The system comprises a storage battery unit group which comprises at least one single storage battery and is used for providing a direct-current power supply; the standby storage battery unit is connected with the storage battery unit group in parallel; the monitoring modules are mounted at the positive and negative ends of each single storage battery; the intelligent control switch is matched with the monitoring module, receives an instruction of the central control system, and is used for controlling the switching operation of putting in and quitting the single storage battery; the charging and discharging control module is matched with the intelligent control switch and is used for carrying out automatic charging and discharging operation on the withdrawn single storage battery; and the central control system is used for receiving the monitoring data, controlling the intelligent switch and the charging and discharging control module, and sending an operation instruction. According to the invention, automatic charging and discharging operation of the single storage battery is realized, the efficiency of maintenance work is obviously improved, manual intervention is reduced, and the maintenance cost is reduced.

The invention belongs to the field of vehicle-mounted tire pressure monitoring, and discloses a vehicle-mounted tire pressure monitoring platform and method based on the Internet of Things. Comprising the following steps: collecting comprehensive state data of each tire, and constructing a joint structure chart of the tires; calculating the difference data of the joint structure chart at the current time point and the previous time point to form a difference packet, and transmitting the difference packet by using an improved difference technology; building and training a monitoring and early warning model, and importing the difference packet into the trained monitoring and early warning model to obtain early warning information; and the early warning information is displayed through a vehicle-mounted interface, and the vehicle is adjusted, so that intelligent, high-efficiency and high-quality monitoring of the vehicle-mounted tire pressure based on the Internet of Things is realized.

The invention discloses an aviation virtual-real joint platform construction system and method based on digital twinning and a platform, and relates to the field of aviation virtual-real joint platform construction. The aviation virtual-real combined platform construction system comprises a virtual model construction module, a physical attribute simulation and rule setting module, a platform visualization module, a virtual mapping module, an operation state monitoring model construction module, a performance evaluation module and a state prediction module. The aviation virtual-real joint platform comprises a perception transmission layer, a data layer, a service layer and an application layer. The sensing transmission layer collects data through various sensors and transmits the data to the data layer through big data, the Internet of Things and cloud service; the data layer comprises a static database. According to the aviation virtual-real combined platform established by the invention, potential faults and performance reduction can be found in advance through real-time monitoring and predictive analysis, so that preventive measures are taken, and the performance and reliability of an aircraft are improved.

The application discloses a kind of digital intelligence powder type free matching variable viscosity fracturing fluid skid-mounted equipment and control method thereof, belong to oil and gas field development technical field.The equipment includes stirring mechanism, feeding mechanism, water feeding mechanism and discharging mechanism, and is centrally controlled by control box.Feeding mechanism uses first screw conveyor to deliver solid material to stirring tank body;Water feeding mechanism supplies water to tank by water feeding pump and water outlet pipe with electromagnetic valve, and water quantity is monitored by water inlet flow sensor;Discharging mechanism outputs mixed liquid by discharging pump, and viscosity sensor detects fracturing fluid viscosity in real time.Control box connects each sensor and executing component, realizes automatic adjustment and closed-loop control of material proportioning by collecting flow and viscosity data.The application realizes accurate and automatic preparation of fracturing fluid, effectively solves the problem of inaccurate and poor stability of traditional manual proportioning, and significantly improves the quality of liquid preparation and operation efficiency.

The invention relates to the technical field of industrial control, and provides an equipment predictive maintenance method and device, electronic equipment and a storage medium. According to the method, equipment use information is obtained, a detection plan is determined according to the use information, equipment label point information is obtained based on the detection plan, data analysis is performed on the equipment label point information, and when it is determined that equipment meets maintenance conditions based on the equipment label point information, a maintenance work order is generated and distributed. Therefore, predictive maintenance of the equipment is realized, pertinence of equipment maintenance is improved, resource waste is reduced, and maintenance efficiency is improved.

The invention discloses a concrete pump pipe fixing system and an assembling and monitoring method thereof. The system comprises a vertical fixing device, a bent pipe fixing device, a horizontal fixing device and a monitoring and control system. The assembling method comprises the following steps of positioning and paying off, installing the vertical fixing device, installing the bent pipe fixing device, installing the horizontal fixing device, installing the pipeline, and clamping and fixing. The monitoring method comprises five main steps of system initialization, data acquisition, edge calculation processing, cloud analysis and early warning, and decision support and feedback. According to the invention, lossless installation, intelligent monitoring, active early warning and predictive maintenance are realized, the construction safety is remarkably improved, the service life of equipment is remarkably prolonged, and the management efficiency is remarkably improved.

This utility model relates to the field of film winding technology, and more particularly to an intelligent electric film winding machine, comprising: a mechanical drive mechanism, which includes a motor housing, a winding motor housed within the motor housing, a gearbox connected to the output end of the winding motor, and a rotating shaft driven to rotate by the gearbox; and a control mechanism, which includes a control housing and a control motherboard disposed within the control housing, on which a signal processing unit and a 4G communication module are integrated. This utility model, by setting up a control motherboard integrating a 4G communication module and a signal processing unit, solves the technical problem that traditional equipment cannot be remotely intelligently controlled and interact with data, achieving the technical effect of building the core of the Internet of Things and realizing comprehensive remote management.

The utility model provides centrifugal air compressor testing equipment, and relates to the technical field of air separation systems, and the centrifugal air compressor testing equipment comprises a mounting seat which is used for fixing a centrifugal air compressor; one end of the air inlet monitoring pipe is connected with the air outlet end of the air supply mechanism, and the other end is connected with an air inlet of the centrifugal air compressor; an air inlet temperature sensor, an air inlet humidity sensor and an air inlet flow sensor are mounted on the air inlet monitoring pipe; the gas supply mechanism is used for adjusting the temperature, humidity and flow of gas; and the air outlet monitoring pipe is installed on the installation base and connected with an air outlet of the centrifugal air compressor, and an air outlet flow sensor is installed on the air outlet monitoring pipe. According to the method, the equipment rule is summarized through testing, predictive maintenance can be realized, stable operation of the equipment can be maintained, and the maintenance difficulty is reduced.

This invention relates to the field of transformer condition monitoring technology, specifically a transformer condition monitoring and protection system utilizing a wireless sensor network. The system includes a sensor optimization deployment module for determining the optimal physical installation location of each wireless sensor node within the wireless sensor network based on thermal imaging analysis of the transformer; and a data acquisition module for collecting transformer condition data through multiple wireless sensor nodes deployed at the optimal physical installation locations. This condition data includes at least temperature data, vibration data, and electrical parameter data. This invention obtains a continuous temperature change map of the transformer through thermal imaging analysis and employs algorithms for calculating the deflection angle and distance. Using the hotspot area with the largest temperature change rate as a benchmark, and combining this with the physical offset of the thermal field propagation, the optimal sensor installation location is calculated, overcoming the shortcomings of traditional manual sensor placement that relies on experience.

The invention discloses a built-in ultrasonic detection safety assessment method for a U-shaped tube bundle of a high-pressure heater of a power plant. The method comprises the following steps: S1, sampling a core detection area determined based on a flow field and a structure in the U-shaped tube bundle of the high-pressure heater; s2, cleaning the inner wall of the sampling tube bundle and calibrating an ultrasonic detection system; s3, performing spiral scanning on the sampling tube bundle by adopting a built-in rotary ultrasonic detection system to obtain wall thickness distribution data; s4, identifying a defect type based on the scanned image, determining the minimum residual wall thickness, and calculating a safety coefficient in combination with a bursting pressure formula; and S5, making a hierarchical maintenance decision based on the safety coefficient. Compared with the prior art, the built-in ultrasonic detection safety assessment method for the U-shaped tube bundle of the high-pressure heater of the power plant has the advantages of integrating area positioning, accurate detection, defect diagnosis and safety assessment.

The invention provides a road unmanned inspection system and inspection method based on multi-modal perception and a large model. The system comprises a vehicle end system used for multi-modal data acquisition, automatic driving control, real-time perception fusion and edge reasoning of a lightweight large model; the cloud platform is used for cross-modal depth identification, disease knowledge base management, GIS visual display, inspection task management and large model training iteration; the scheduling end is used for task issuing, result auditing, and visual interaction of human-aided decision making and road health assessment. On the basis of the three-layer architecture, a large model collaboration framework is further constructed, and the framework runs across a vehicle end, a cloud end and a scheduling end, is responsible for reasoning collaboration, intelligent supplementary shooting, task priority generation, structured report generation and interpretable reasoning chain construction of a multi-mode large model, and is a key mechanism for realizing closed-loop routing inspection of an intelligent collaboration system.

The application belongs to the technical field of electrical automation equipment, and provides an intelligent control device for electrical automation equipment, which comprises a sensor module, a central processing module, a communication module, an execution control module and a power management module; the application collects the temperature, current and voltage parameters of the electrical equipment in real time through the sensor module, and processes data and makes decisions based on the intelligent control algorithm run by the central processing module based on an embedded processor; the communication module supports wired / wireless mode to interact with the cloud or the terminal, realizes remote data transmission and monitoring; the execution control module adjusts the running state of the electrical equipment according to the instruction of the central processing module; the power management module supplies power for the equipment and provides overload and short-circuit protection functions; meanwhile, the device adopts modular design, and the modules are connected through standard interfaces, which is convenient for system expansion and function upgrade, thereby improving the running efficiency, safety and intelligent level of the device, and reducing the operation and maintenance cost.

The application discloses a kind of vehicle-mounted unmanned aerial vehicle charge-discharge management system and method, including the full-amount state parameter of acquisition unmanned aerial vehicle, hangar, power supply and environment, constructs system state vector, dynamically calculates charging trigger threshold and judges charging trigger condition, exports charging trigger signal or standby instruction or low-power value guard instruction;Construct reinforcement learning scheduling system and complete charging channel, power level, power supply type Dynamic decision, output optimal charging scheduling instruction;Precise regulation and control to charging voltage, charging current is realized through double closed loop artificial neural network, compensates wireless charging coupling coil offset, exports battery real-time operation data in charging process;Battery health status is analyzed, battery remaining useful life is predicted in combination with battery state parameter collected in whole process, dynamically adjusts charging trigger threshold and optimal charging scheduling instruction.