13145 results about "Smart control" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention relates to a thermal management control method and system for an energy storage power supply, and the method comprises the steps: obtaining the real-time temperature data of the energy storage power supply, and carrying out the heat source distribution analysis, and obtaining the corresponding temperature distribution information; heat dissipation operation parameters of the energy storage power supply are obtained, adjustment analysis is carried out in combination with the temperature distribution information, and a corresponding initial heat dissipation control strategy is obtained; acquiring real-time monitoring data of the energy storage power supply, and performing charge and discharge dynamic analysis in combination with the temperature distribution information and the initial heat dissipation control strategy to obtain corresponding charge and discharge adjustment information; based on the real-time temperature data and the thermal management strategy, hot spot identification is carried out, corresponding potential hot spot information is obtained, early warning processing is carried out, and a corresponding hot spot early warning result is obtained; and performing comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hot spot early warning result to obtain a corresponding global thermal management control scheme. According to the invention, comprehensive and intelligent control of thermal management of the energy storage power supply can be realized.

The invention provides a control method based on differential rotation speed torque distribution, which relates to the technical field of intelligent control and comprises the following steps: performing difference calculation on an initial input quantity and a preset rotation speed instruction to generate a rotation speed deviation signal and a deviation change rate, and establishing a dynamic membership function library through a fuzzy controller; mapping the rotating speed deviation and the change rate thereof into a fuzzy language variable, performing reasoning operation according to a preset fuzzy control rule base, and outputting a frequency regulation quantity and a torque compensation coefficient of the frequency converter; and reading the generated torque compensation coefficient and shaft system torque values actually measured by the two torquemeters, calculating a dynamic difference value between the current total load torque and the target torque, establishing a torque distribution weight model according to the transmission characteristics of the differential mechanism, and generating differential torque correction instructions of the two motors in combination with the real-time rotating speed values. The control performance of differential drive is improved.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention relates to the technical field of intelligent control, in particular to a control method and system based on intelligent collaborative production. Comprising the following steps: aiming at a task cut-in influence range, traversing related nodes in a production network graph model, and detecting a device resource occupation conflict with an existing task to obtain a resource conflict detection result; recursively tracking the affected downstream process from the time delay predicted value along the dependence edge of the production network graph model to generate an influence propagation path; according to a weight dynamic quantification result, summarizing weight values of all affected nodes, generating a global view evaluation score, and determining an overall influence degree of new task cut-in; if the global view evaluation score exceeds a preset threshold, adjusting the cut-in node and time of the new task, and re-executing resource conflict detection to obtain an optimized task cut-in scheme; and extracting a process execution sequence and a resource allocation plan from the optimized task cut-in scheme, updating the production network diagram model, and generating a final collaborative production scheduling scheme.

The invention provides a power grid multi-agent large model safety evaluation index calculation method, and relates to the technical field of intelligent control. The method comprises the following steps: acquiring a voltage change rate and a frequency change rate, dividing four stages according to disturbance starting and extreme points, pairing curve trends, judging delay and synchronization characteristics, extracting a continuous change structure, and generating a disturbance subsection evolution structure; according to the method, through staged analysis of node voltage and frequency change trends and combination of trend consistency judgment and abnormal segment identification, dynamic structure reconstruction and trend mapping of a disturbance process are realized, and coherence and precision of index response state identification are improved through trend boundary collection and frequency classification; cooperative characteristic identification is enhanced through the coupling direction and delay analysis of inter-node multi-parameter change, the node adjustment priority is defined through regulation and control response sorting, an adjustment path under multi-dimensional linkage is constructed, and the refined management and control capacity of disturbance response and the pertinence of control decision are improved.

The invention relates to the technical field of intelligent control, in particular to a self-adaptive camera pose intelligent adjusting system which comprises a dynamic recognition module, a pose adjusting module, a mechanism control module, an information filtering module and a feedback association module. According to the method, the foreground pixel brightness gradient and the boundary continuity in the image frame are extracted, the target displacement path can be recognized, the tracking information is constructed, the target coherent perception under rapid scene change is guaranteed, the attitude parameters and the target motion direction and speed are combined, the offset correction instruction is generated, the response stability of the dynamic target is improved, and the target tracking accuracy is improved. The execution structure feedback is used for correcting control output, reducing the driving error accumulation risk, eliminating non-target disturbance through image boundary definition and motion consistency, improving data purity and combining a view angle execution state and path information to realize synchronous control of tracking and view angle adjustment; and the response efficiency and the following robustness of the system in a dynamic environment are enhanced.

The invention provides an oil smoke real-time concentration monitoring and intelligent adjusting method and system, and relates to the technical field of intelligent control, and the method comprises the steps: collecting real-time air quality data of a catering area through a plurality of environment sensors, including oil smoke concentration, PM2.5 concentration, carbon monoxide concentration, temperature and humidity; and the control center calculates an oil smoke concentration spatial and temporal distribution field based on real-time data in combination with spatial interpolation, considers smoke exhaust energy consumption, smoke exhaust efficiency and regional oil smoke concentration, and determines an optimal cooperative control strategy by utilizing a multi-target optimization algorithm, and the optimal cooperative control strategy comprises the fan rotating speed of each range hood, the smoke exhaust direction and the air supply parameters of the fresh air system. And the control center issues the control strategy to a range hood controller to adjust operating parameters of the range hood. Dynamic optimization control over the air quality of the catering area can be achieved, the oil smoke concentration is reduced, energy is saved, and the smoke exhaust efficiency is improved.

The invention discloses an intelligent linkage control management system based on air conditioner application, and relates to the technical field of intelligent control and energy management, and the system comprises a load prediction module which obtains an air conditioner load demand prediction value through an intelligent load prediction model based on preprocessed environment energy consumption data; the optimization scheduling module is used for optimizing the current air conditioner operation mode through the building energy management system based on the air conditioner load demand prediction value, and obtaining an optimized air conditioner operation scheduling instruction; the intelligent adjusting module is used for intelligently adjusting the operation parameters of the air conditioner system based on the optimized air conditioner operation scheduling instruction, adaptively adjusting the energy efficiency of the air conditioner and obtaining operation feedback data; and the fault management module is used for identifying potential fault risks of the air conditioner and formulating fault risk prevention measures through a time sequence analysis method based on the operation feedback data. According to the invention, through innovation of an intelligent load prediction and fault prevention mechanism, the energy utilization efficiency and the system stability are effectively improved.

The invention relates to the technical field of intelligent control, in particular to an unmanned aerial vehicle cluster collaborative operation method and system, and the method comprises the following steps: collecting geometric distribution data and task density information of an unmanned aerial vehicle cluster operation region, carrying out the point set coordinate screening of the geometric distribution data, calculating the distance and spatial relevance between point set distributions, and calculating the task density information of the unmanned aerial vehicle cluster operation region; and obtaining a task density distribution set. According to the method, through comprehensive extraction of task completion time, priorities and position sequences, task windows are divided in combination with time periods and priorities, ordered execution and real-time optimization of tasks are achieved, the flexibility and collaboration of task scheduling are improved, and the task scheduling efficiency is improved based on sequence allocation and time interval optimization of the priorities. Reasonable allocation of time and resources in the task execution process is further ensured, non-critical delay and conflicts are avoided, the operation state and the residual electric quantity of the unmanned aerial vehicle are monitored in real time, and the efficiency, the flexibility and the stability of unmanned aerial vehicle cluster collaborative operation are improved.

The invention discloses a charging pile intelligent control management platform, relates to the technical field of charging pile intelligent control, and can identify the peak and valley periods of the power grid load in advance by collecting and analyzing the power grid load data and the operation state of a charging pile in real time and combining a regression model of a load prediction and demand analysis module. The power output of each charging pile is dynamically adjusted through the charging pile scheduling and power adjusting module, the power grid resource distribution is optimized, the overload phenomenon is avoided, and the stability of power grid operation is improved. By monitoring and feeding back the charging behavior data of the user, the platform can reasonably plan the working state of the charging pile. Therefore, too fast loss of some charging piles due to continuous overload is avoided, resource waste of idle charging piles is reduced, the utilization efficiency of equipment is improved on the whole, and the service life of the charging piles is prolonged. By using the load prediction and demand analysis module, the platform can pre-judge the charging demand peak of the user and optimize the scheduling strategy in advance, thereby shortening the waiting time of the user.

The invention provides an adjustable spectrum-based liquid crystal display system and a control method thereof, relates to the technical field of display systems, and aims to overcome the limitations of poor environmental adaptability, easiness in causing visual fatigue, single user experience and the like of an existing display. The system creatively integrates a liquid crystal panel, a high-precision multi-main-color Micro-LED backlight, a pixel inner sensing array, an external multi-mode sensing module, a dynamic addressable micro-optical array, a dynamic polarization modulation layer and an optional transparent energy collection layer. The intelligent controller carries out fusion processing on the real-time biological state, environment information and application context data of the user obtained by the multi-source sensor, and carries out accurate state evaluation and future trend prediction. And determining an optimal space-time-direction-spectrum-polarized light field output characteristic and an intelligent energy management strategy through a multi-objective optimization decision engine, and driving each optical regulation and control component to execute in real time. The visual comfort and health level are remarkably improved, and the information fidelity and task performance in a complex environment are ensured.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention relates to the technical field of laser cutting, in particular to a laser cutting device and a control method thereof. The method comprises the following steps: collecting data of a laser cutting machine to obtain data of the laser cutting machine; cutting head moving speed data of the laser cutting machine data are extracted, and the cutting head moving speed data are obtained; performing power calculation on the moving speed data of the cutting head to obtain laser power data; spatial modeling is carried out based on the cutting head moving speed data and the laser power data, and a laser cutting machine three-dimensional model is generated; performing cutting path identification on the three-dimensional model of the laser cutting machine to generate cutting path data; and extracting first cutting data of the laser cutting machine data to obtain the first cutting data. Through the Internet of Things technology, the data mapping technology, the data analysis technology and the intelligent control technology, self-adaptive laser cutting control over different materials and thickness conditions is achieved, and therefore the cutting quality and efficiency are improved.

The invention discloses an intelligent optimization control system and method for a battery material recovery and preparation process, and relates to the technical field of intelligent control. Training sample data and a training quality prediction model in the battery material recovery and preparation process in a test environment are collected, and then a state space, an action space and a reward function of a deep reinforcement learning model are constructed; fusing the prediction result of the quality prediction model and the monitoring data of the sensor in a test environment through a feature fusion strategy, generating a fusion process representation vector, inputting the fusion process representation vector into a deep reinforcement learning model, training the deep reinforcement learning model, and obtaining a fusion process representation vector; based on the real-time sensor data, the quality prediction model and the deep reinforcement learning model, obtaining an action output by the deep reinforcement learning model, and performing parameter regulation and control on the to-be-monitored recovery preparation process by taking the action as an optimization strategy; the production energy consumption is effectively reduced, and the resource utilization efficiency is improved.

According to the thermal management method and system of the modular power supply, heating elements are identified, temperature sensors are installed to evaluate thermal resistance and thermal capacity under different load conditions, the thermal coupling effect between modules is evaluated, an isolation layer and a thermal bridge are designed to optimize heat flow, load distribution is dynamically adjusted, an intelligent control mechanism is implemented, temperature stability is ensured, and the reliability of the system is improved. An external environment is monitored, a heat dissipation strategy is automatically adjusted to adapt to different conditions, a temperature prediction model is established by using historical data, measures are taken in advance to prevent overheating, and the whole system comprises a temperature monitoring and data analysis module, a thermal coupling effect evaluation and optimization module, an intelligent load distribution and control module and an external environment monitoring and adaptation module. And the temperature prediction and prevention module ensures that the modular power supply can effectively dissipate heat under various working conditions, so that the reliability and efficiency of the system are improved.

The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for continuous basalt fiber drawing forming. The method comprises the following steps: acquiring temperature measurement point data including temperature data and flow data; temperature control, energy supply strategy adjustment and bushing partition heating parameter adjustment are conducted according to the collected temperature measurement point data, and a fuzzy self-tuning algorithm is adopted to conduct linkage adjustment on the temperature of an uptake, the temperature of an access and the temperature before bushing; dynamically adjusting an energy supply strategy based on the energy supply sequence; and the partition heating parameters of the bushing are adjusted by using a PID control algorithm. According to the invention, a longitudinal partition heating mode and accurate temperature control are adopted for the bushing, and a strategy of dynamically adjusting the wire drawing speed and the partition temperature according to the average value of the wire diameter is combined, so that the wire diameter of basalt fibers drawn by the whole bushing can be effectively ensured to be more uniform.

The invention relates to the technical field of vehicle-mounted temperature control, and discloses a method and system for intelligently controlling the temperature and humidity of a vehicle-mounted cold chain and a storage medium. The method comprises the following steps: acquiring and preprocessing temperature and humidity data through a sensor; performing state evaluation based on a three-way decision model; constructing a temperature and humidity trend analysis model to predict a change trend; constructing a control decision system by adopting multi-agent reinforcement learning; executing a multi-mode control strategy; and adaptively updating the control system according to the execution effect. According to the application, the future temperature and humidity change trend is predicted according to the historical data and the real-time state, collaborative optimization control of temperature and humidity is realized, self-learning and continuous optimization capabilities are provided, and the accuracy, stability and energy utilization efficiency of cold chain temperature and humidity control are effectively improved.

The invention discloses a treatment system and a treatment method for chemical production waste gas, and relates to the technical field of industrial waste gas treatment. The system comprises a waste gas collection unit, a pretreatment unit, a multi-stage purification unit and an intelligent control unit. The intelligent control unit monitors data through a sensor network, the data processing and analysis module evaluates the processing effect and predicts the trend, the intelligent decision and adjustment module optimizes the parameters of each unit in sequence, and the remote monitoring and alarm module monitors and alarms in real time. The system can efficiently treat waste gas, realizes automatic intelligent control, ensures the stable treatment effect, reduces the risk of environmental pollution, and is widely applied to the field of chemical production waste gas treatment.

The invention relates to the technical field of battery protection control, and discloses a battery charging protection method and system based on intelligent control and a storage medium. The method comprises the following steps: acquiring and calculating battery parameters to obtain a state value; constructing a neural network model based on the state value to output a charging parameter; generating a charging strategy according to the charging parameters; the charging strategy is input into the control circuit for execution; executing multi-stage safety protection to obtain a result; and updating neural network model optimization control parameters according to a result. According to the invention, the charging control model based on the back-propagation neural network is constructed, and the real-time battery state monitoring, the self-adaptive charging strategy generation, the multi-stage safety protection mechanism and the self-learning optimization function are combined, so that the precise control and the comprehensive protection of the battery charging process are realized, the service life of the battery is effectively prolonged, and the charging safety is improved.

The invention provides a multi-mode control method and system for intelligent variable-frequency cooking equipment, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining a food material image and weight data through an image collection device, extracting feature information through a deep learning processor based on a pre-trained food material feature recognition model, and generating a feature data packet containing food material type, weight and volume information. The controller matches initial cooking mode parameters according to the data packet, collects real-time operation data such as temperature, pressure, heating power and stirring motor rotating speed in the cavity, and calculates power-temperature and motor rotating speed-torque response curves. And the controller generates a frequency conversion control instruction according to the response curve and a preset multi-mode switching judgment rule, and controls the heating unit and the stirring motor to perform cooperative adjustment. In the cooking process, real-time operation data are continuously collected, multi-mode self-adaptive switching is carried out, and the frequency conversion control instruction is optimized until cooking is completed.

The invention provides a management method and system for coal preparation quality intelligent control, and the method comprises the steps: firstly obtaining raw material quality, equipment operation and environment state index sets of a target production line, then carrying out the dynamic association processing of the index sets, and generating a technological process feature set; then calling a preset optimization decision model to carry out parameter prediction on the technological process feature set to obtain a process regulation parameter set, generating a quality optimization instruction set based on the process regulation parameter set, transmitting the quality optimization instruction set to a production control unit to execute dynamic regulation operation, and finally, according to a quality feedback index set after regulation execution, carrying out dynamic regulation operation. And the parameter weight distribution of the optimization decision model is updated, intelligent control and continuous optimization of the coal dressing quality are realized, and the stability of coal dressing production and the product quality are improved.

The invention relates to the technical field of water pump intelligent control, in particular to a vacuum pump and water supply pump intelligent control system. A water supply pump liquid conveying adjusting module collects operation parameters and liquid state information of a water supply pump and analyzes the boundary layer thickness of an inner wall area of a water supply pump connecting pipeline; and judging the influence of the boundary layer thickness change on the liquid flow fluctuation, adjusting the rotating speed of the water supply pump and the valve opening, and generating a water supply pump initial operation adjustment result. According to the method, the operation parameters of the water supply pump and the liquid state information are collected in real time, the flow velocity and the flow velocity gradient of the liquid on the inner wall of the pipeline are measured to calculate the thickness of the boundary layer, meanwhile, the relation between the thickness and the pressure gradient is analyzed, the regulation and control capacity of fluid flow fluctuation is effectively improved, and the liquid conveying stability is enhanced. In the liquid conveying process, the influence of liquid viscosity is analyzed through temperature changes, the conveying state is monitored in combination with the pressure difference, the abnormal condition can be rapidly recognized and marked, and the safety and the fault response speed are improved.

The invention discloses a soothing lamp intelligent control method and system based on voice interaction, and relates to the technical field of data encryption, and the method comprises the steps: receiving a voice command inputted by a user through a voice interaction device, awakening a control system, and starting a voice emotion recognition unit; the voice emotion recognition unit is used for performing emotion analysis on the voice command, extracting emotion characteristics and judging real-time emotion of the user; on the basis of continuous voice input of a user, voice signals are collected regularly, the emotional state is updated dynamically, and light setting is optimized in combination with environmental data; by recording historical voice interaction data and environment data of a user, an emotion mode and light preference of the user are learned, and light setting is continuously optimized. Light setting is continuously optimized through a self-learning mechanism, it is ensured that the system can conduct self-adaptive adjustment along with changes of user requirements, and light control is more intelligent and personalized.

The invention provides a load current and inverter load discharge combined remote discharge method and system, and relates to the technical field of discharge control, and the method comprises the steps: collecting the parameters of a battery pack through a remote monitoring system, evaluating the health state and dischargeable capacity of the battery pack based on a fuzzy control algorithm, and calculating the optimal discharge power and duration according to a prediction correction algorithm; and generating a load and inversion discharge power curve. When the battery pack is healthy and the dischargeable capacity is sufficient, the load and the inverter discharge unit are remotely controlled, the duty ratio of a load switch and the switching frequency and output voltage of an inverter are controlled according to respective power curves, and two discharge loops are formed. And collecting discharge current and battery pack parameters in real time, and stopping discharging when the state of charge of the battery pack reaches a preset threshold value. According to the invention, by combining the load current and the inverter load for discharging and performing intelligent control based on the battery state, efficient and safe remote discharging is realized, and the service life of the battery pack is prolonged.

The invention discloses a high-precision drilling control method and system for a multi-layer PCB, and relates to the technical field of intelligent control. The method comprises the following steps: scanning and analyzing to obtain layered material characteristics and structural size information of a multi-layer PCB, and carrying out clamping fixation and positioning hole processing on the multi-layer PCB to obtain a PCB positioning hole; a drilling machining three-dimensional coordinate axis is constructed; a PCB drilling strategy space is constructed, the PCB drilling strategy space is adopted to conduct drilling analysis on the layered material characteristics and the structure size information in sequence according to the PCB drilling machining requirement, and layered drilling strategy parameters are obtained; and the layered drilling strategy parameters are mapped to a drilling processing three-dimensional coordinate axis for space route fusion, layered drilling processing parameters are determined, and high-precision drilling and quality detection closed-loop control are executed on the multi-layer PCB. The technical problem that in the prior art, the positioning precision is low in the drilling process of the multi-layer PCB is solved, and the technical effect of improving the drilling machining precision of the multi-layer PCB is achieved.

The invention relates to the technical field of intelligent control, in particular to a numerical control lathe cutting precision optimization method and system for metal part machining, and the method comprises the steps: constructing a multi-mode key feature set with aligned timestamps; the initial compensation amount in the cutting feeding direction is obtained from the tool compensation lookup table; searching a corresponding historical cutting state from the processing state matrix, and inputting the key temperature distribution gradient of the cutter side, the key vibration absolute peak value of the lathe spindle and the corresponding historical cutting state into a weight coefficient model to obtain a temperature compensation weight and a vibration compensation weight of the current cutting state; calculating a dynamic correction factor, and correcting the initial compensation amount in the cutting feeding direction through the dynamic correction factor to obtain a cutting feeding direction compensation amount with a timestamp; the compensation rate of the cutting feeding direction compensation amount and the target feeding displacement under the same timestamp is calculated; and the cutting precision is optimized according to the comparison result of the compensation rate and the cutting key feeding relative deviation rate. And the requirements of high precision, low cost and fast response are met.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention relates to the technical field of intelligent control and optimization of a photovoltaic power generation system, and discloses a self-adaptive photovoltaic module inclination angle adjusting method and system based on environmental conditions, and the method comprises the steps: obtaining short-term weather forecast data through a meteorological data interface, analyzing the weather forecast data, and predicting the environmental change in several hours in the future; monitoring pollution data of the surface of the photovoltaic module in real time through a sensor, and judging whether the pollution degree affects the power generation efficiency or not; according to the prediction result and the pollution degree, the inclination angle of the photovoltaic module is dynamically adjusted, the power generation efficiency under the upcoming illumination condition is optimized, the inclination angle of the photovoltaic module is dynamically adjusted in combination with short-term weather forecast data obtained by the meteorological data interface, and the power generation efficiency of the photovoltaic system under the complex environment is remarkably improved. The system can quickly respond to environmental changes, and efficiency loss caused by adjustment lag is avoided. And multi-factor collaborative optimization is realized, and the maximum power generation efficiency of the photovoltaic system under various conditions is ensured.