533 results about "Analytical control" patented technology

The invention discloses a control method and a control system of a data center liquid cooling heat dissipation system, and relates to the technical field of liquid cooling heat dissipation, the system realizes real-time state monitoring of server operation through a data acquisition and monitoring module, integrates multiple data such as temperature, flow velocity and pressure difference, forms a detailed comprehensive heat dissipation data set, and improves the heat dissipation efficiency. And a database is constructed for storage. And meanwhile, a cooling liquid flow velocity analysis control module of the system generates a server heat dissipation coefficient CN through the server operation environment parameter C1 and the server operation parameter C2, the server heat dissipation coefficient CN is compared with a preset heat dissipation threshold C for evaluation, and the cooling liquid flow velocity is adjusted in real time. Therefore, when the load of the server is increased, the cooling intensity can be automatically increased, the chip temperature is reduced, and stable operation of the server is kept; and when the load is reduced, the cooling intensity is reduced, the energy consumption is reduced, and the cooling efficiency and the energy utilization rate of the system are remarkably improved.

The invention discloses an intelligent operation system for aquaculture management, which relates to the field of aquaculture and comprises a water quality detection module for detecting water quality data in an aquaculture unit, a meteorological detection module for detecting meteorological data of the position of the aquaculture unit, and an ultrasonic detection module for detecting quantity data of fishes in the aquaculture unit, the image detection module detects weight data and growth stages of fish in the breeding unit, and the analysis control module analyzes standard water quality and standard feed feeding amount in the breeding unit through an AI analysis model according to meteorological data, quantity data, weight data and growth stages; the analysis control module compares the difference between the water quality data at the current moment and the standard water quality, controls the oxygenation equipment to oxygenate and controls the water treatment equipment to change water; according to the standard feed feeding amount, a control signal is sent to control feeding system equipment to feed. According to the invention, powerful support is provided for modernized development of aquaculture, and more efficient, real-time and intelligent aquaculture management is realized.

The invention provides a metro driver abnormal behavior recognition method based on multi-modal data fusion. The method comprises the steps that metro driver abnormal behaviors are recognized based on multi-modal data fusion, track positioning and platform screen door sensor data are fused to detect the position of a train, and a station entering confirmation instruction is generated; according to the data of the seat pressure distribution sensor, driver off-seat inspection is judged; generating an outbound completion event in combination with a track circuit signal, an ATO instruction and the like; analyzing console touch pressure, voice instructions, hand actions and the like to verify operation compliance, and triggering an alarm when the operation is abnormal; aggregating results at edge computing nodes, encrypting and transmitting the aggregated results to a central scheduling system, dynamically mapping driver behaviors in a digital twin cockpit, and automatically triggering hierarchical alarms and associating data snapshots when the driver behaviors are abnormal; according to the invention, accurate recognition and real-time early warning of subway driver behaviors can be realized by means of multi-modal data fusion and intelligent analysis, and the subway operation safety and the emergency response efficiency are greatly improved.

The invention discloses an intelligent log retrieval and analysis system based on a model context protocol MCP, and relates to the technical field of log analysis. The system comprises an MCP log semantic conversion and retrieval engine, a log format irrelevant feature extraction and indexer, a dynamic log format recognition and context enhancement system, a real-time retrieval analysis and aggregation controller and a multi-level semantic retrieval and visualization engine. According to the context enhancement system, unknown formats can be identified, semantic tags can be complemented, and semantic consistency and behavior tracking capability can be improved. The retrieval analysis controller supports semantic expression analysis, strategy generation and feedback closed loop, and strategy scheduling and multi-dimensional aggregation analysis are achieved. And finally, outputting a structured semantic result by the system, and visually displaying the structured semantic result through components such as a semantic composition device and a context expander. All the modules are managed in a unified mode through a capability registration mechanism, dynamic arrangement and upstream and downstream closed-loop linkage are supported, and a log intelligent analysis framework with high semantic driving and a clear structure is formed.

The invention relates to the technical field of carbon emission, in particular to a full-life-cycle carbon footprint tracking management platform for power equipment. The platform collects carbon emission parameters in the whole process from raw material input to scrap recovery in real time through a monitoring equipment network and a sensor array to generate an original monitoring data stream; performing multi-source fusion and deviation correction on the data stream to generate a standardized carbon footprint sequence; a carbon emission trend model is constructed based on sequence extraction correlation features, and a dynamic adjustment strategy is generated when an integration fracture risk or emission abnormity is detected; transmitting an abnormal signal through a mobile terminal interface, receiving an adjustment instruction, and generating a closed-loop control signal; storing the extended carbon footprint data to construct a full life cycle file; estimating emission fluctuation to generate a stability strengthening strategy; and a monitoring-analysis-control-prediction four-order optimization link is formed. According to the invention, accurate tracking and dynamic optimization of carbon emission are realized, data integrity and management timeliness are improved, and technical support is provided for green manufacturing of power equipment.

The invention provides an unmanned aerial vehicle consistency control method based on a Stackelberg-Nash game, and relates to the technical field of multi-agent consistency and game optimization control. The method comprises the following steps: establishing a motion attitude nonlinear dynamic model of each unmanned aerial vehicle in a multi-unmanned aerial vehicle system, and defining a communication network topological relation; constructing a consistency error and a performance index function; the method comprises the following steps: establishing a layered Stackelberg-Nash game mechanism with a plurality of participants; utilizing a Bellman optimality principle to construct a coupling HJB equation to solve an optimization control strategy of the leader and the follower; and constructing a single evaluation network to estimate the optimization control strategy of the leader and the follower in each execution so as to realize an optimization control target. A favorable tool is provided for analyzing a series of control problems of master-slave consistency of multiple unmanned aerial vehicles in the control field, and the reliability of a control system can be enhanced to a certain extent.

The invention relates to the technical field of high-pressure hydrogenation reactor control, and discloses a multi-stage pressure optimization control method and system for a high-pressure hydrogenation reactor, and the method comprises the steps: obtaining temperature, pressure and flow monitoring data, generating original parameter data, and deploying a distributed control network; collecting and analyzing multi-stage pressure sensor data, and generating a pressure change influence factor; sensing a valve state, and generating a valve change influence factor; collecting data of a flow sensor, and dividing a control area after calibration; and calculating a pressure optimization index based on the control area data, integrating multiple factors to generate comprehensive control data, and further generating a pressure control signal and outputting an instruction. The system comprises a control network deployment module, a pressure analysis module, a valve analysis module, a control area analysis module and a pressure control signal generation module. Through multi-dimensional data fusion, a distributed control network and dynamic optimization calculation, the accuracy and stability of pressure control of the high-pressure hydrogenation reactor are improved, and the method is suitable for efficient and safe production in the field of petrochemical engineering.

The invention relates to the technical field of carton production detection, and discloses a carton production line intelligent detection system comprising an industrial camera module used for collecting carton appearance images; the dynamic speed sensor is used for monitoring the conveyor belt speed; the mechanical vibration sensor is used for detecting vibration interference of the conveyor belt; the image processing module is used for correcting and analyzing the acquired image; the control module is used for feeding back and controlling the detection result in real time; and the storage module is used for data storage and model training. Through dynamic multi-physical coupling modeling, the influence of optical distortion, speed fluctuation and vibration interference on size detection is comprehensively considered. Through a distributed optimization algorithm and a real-time feedback control mechanism, various errors in a dynamic environment are effectively compensated, the size detection precision reaches the micron level, a high-speed sensor and an industrial camera module are adopted for data acquisition, a detection algorithm is optimized through a distributed parallel computing technology, and the size detection precision is improved. And the response time of the system is controlled within milliseconds.

The invention relates to the technical field of farm disinfection, in particular to an intelligent disinfection system for a farm. The system comprises an initial determination module, a periodic disinfection module, an environment monitoring module, a first analysis control module, a second analysis control module and a feedback correction module. According to the method, the initial disinfection parameter cluster of each breeding area in the target breeding farm is determined according to the actual growth stages of the breeding targets and the actual number corresponding to each actual growth stage, each breeding area is periodically disinfected according to the initial disinfection parameter cluster, and each breeding area is monitored in real time so as to be singly judged; and performing first-class adjustment and second-class adjustment on the initial disinfection parameter cluster based on a first verification result and a second verification result in combination with the first verification result and the second verification result to determine whether the initial disinfection parameter cluster is subjected to first-class adjustment and second-class adjustment. And according to the first analysis control result and the second analysis control result, determining whether to perform feedback correction on a preset initial disinfection period of each breeding area.

The invention discloses an industrial big data platform system for intelligent supervision, which comprises a management control module, an analysis processing module and a transmission storage module, and is characterized in that the management control module is in network connection with the analysis processing module, and the analysis processing module is in data connection with the transmission storage module; the management control module is used for carrying out login management control on an industrial control system, the analysis processing module is used for carrying out analysis control processing on industrial supervision, and the transmission storage module is used for carrying out transmission storage management on data information. The management control module comprises a login management module, a mutual authentication module and a bill confirmation module, the login management module is in data connection with the mutual authentication module, the mutual authentication module is in network connection with the bill confirmation module, and the login management module is used for managing and controlling user login. The system has the characteristics of efficient management control and high safety.

The invention provides a low-load combustion optimization control system for a unit, and the system comprises a sensing network module which collects key parameters of the unit in a combustion process; the data preprocessing module is used for preprocessing the collected key parameters; the index analysis module is used for analyzing the energy balance deviation degree, chemical balance and combustion uniformity evaluation indexes of the current unit based on the preprocessed key parameters; the control strategy execution module is used for carrying out threshold value judgment on the result of each index and correspondingly executing preset energy balance, chemical balance and combustion uniformity strategies; the feedback regulation module is used for acquiring the executed feedback signal and performing new index analysis and cyclic regulation to form a closed-loop feedback mechanism; energy balance, chemical balance and combustion uniformity in the combustion process are analyzed and controlled, so that combustion stability and combustion efficiency are improved, pollutant emission is reduced, the service life of equipment is prolonged, and meanwhile, the adaptability of a unit to a power grid is enhanced.

The invention relates to the technical field of scientific research instrument management, in particular to an intelligent supervision method and system for a scientific research instrument, and the method comprises the following steps: extracting instrument reservation task information and constructing a timetable, collecting and analyzing control instruction abnormal behaviors to generate risk information, collecting power trend data to recognize an operation state, and carrying out management. And collecting an identity instruction and power information to construct a use record, and analyzing power fluctuation and load degree to generate a maintenance cycle evaluation value. According to the method, dynamic sorting and conflict screening of the reservation requests are realized by combining the task dependency relationship and the priority information, the risk level is evaluated by using the user instruction behavior characteristics, the real-time identification of the operation risk is realized, the accuracy of the operation state identification is enhanced by using the power fluctuation trend analysis, and the user experience is improved. A structured binding relationship of use data is established in combination with the identity and the device number, the information tracing integrity and the data tracing efficiency are improved, and the prejudgment capability of the maintenance period is optimized by using the combined evaluation of the operation load and the power trend.

The invention discloses a pig farm environment control method and system, and mainly relates to the technical field of environment control. Comprising the steps that fans are arranged in a target pig farm in advance, and a fan set and an effective coverage area set are determined; traversing the effective coverage area set to arrange gas sensors, and extracting monitoring data of the arranged gas sensor set according to a preset monitoring frequency after the feeding nodes to obtain a harmful gas concentration monitoring sequence set; performing time sequence trend analysis on the harmful gas concentration monitoring sequence set to obtain a first control influence factor set; determining a second control influence factor set; determining a risk area set; and acquiring a ventilation circulation path of the target pig farm, and regulating and controlling the fan set in combination with the risk area set and the area concentration prediction result set. The pig farm environment control method has the beneficial effects that the technical problems of lack of dynamic analysis and low control reliability of pig farm environment control in the prior art are solved, and the technical effect of improving the control reliability is achieved.

The invention discloses a yellow page number analysis control method and device based on deep learning, and the method is characterized in that the method comprises the steps: obtaining call behavior data of a yellow page number library; inputting the call behavior data into the optimized number judgment model, and obtaining a yellow page number stability result through a gradient boosting tree algorithm; and dynamically adjusting the execution of the maintenance strategy of the yellow page number based on the stability result of the yellow page number. The yellow page number stability can be accurately analyzed, the number library data quality can be improved, unstable numbers can be timely found and classified, more reliable services can be provided for users, the cost can be reduced, and the management efficiency can be improved.

The invention discloses a dynamic coupling modeling method and system for an electric power information physical system, and belongs to the technical field of electric power dispatching automation, and the method comprises the steps: analyzing an AGC control process business data flow, and constructing a workflow engine; performing node classification, generating an information node model and an information branch model of the information system, and constructing a rule engine; constructing an information system network matrix model based on the rule engine, and establishing a preset service strategy based on the information system network matrix model; constructing an information physical coupling model of the AGC control process; and according to the AGC actual service, loading a preset service strategy established by the information system network matrix model based on the synergistic effect of the workflow engine and the rule engine, and updating the information physical coupling model. According to the method, a basic model is provided for cross-level closed-loop simulation verification in the AGC process, the problems of unclear information physical dynamic coupling mechanism, insufficient service logic adaptive mapping and model iteration self-updating are solved, and a guarantee is provided for safe and stable operation of a large power grid.

The invention provides a multimedia file synthesis method and device, electronic equipment and a storage medium, and the method comprises the steps: decoding a first multimedia file to obtain video and audio frame data, constructing audio waveform data, and analyzing statistical parameters and a specific time interval average value of the audio waveform data; determining a control parameter to regulate and control the special effect output of the video frame, synthesizing target video data, combining the target frame and the audio frame to generate a second multimedia file, and dynamically displaying the special effect based on the audio content when the file is played; the video special effect can be controlled through audio data analysis, and the function of dynamically displaying rich visual effects according to the audio content when the multimedia file is played is achieved.

The invention belongs to the technical field of spectrum detection, and relates to a polarization LIBS-LIF system for trace element detection, the system comprises a laser emission unit, a first light path structure, an optical parametric oscillator, a second light path structure, a displacement platform, a polarization acquisition module and an analysis control module; the laser emitting unit is used for emitting a first laser beam to the first light path structure; the first light path structure is used for converting the received first laser beam into circularly polarized light and emitting the circularly polarized light into a sample on the displacement platform; the optical parametric oscillator is used for emitting a second laser beam into the second light path structure; the second light path structure is used for focusing the second laser beam and then emitting the second laser beam into the same position of the circularly polarized light to the sample so as to excite the to-be-detected element in the plasma; the polarization acquisition module is used for acquiring plasma spectral signals, converting the plasma spectral signals into electric signals and transmitting the electric signals to the analysis control module. The plasma excitation efficiency can be improved, the detection limit can be reduced, and the stability of the detection result can be improved.

The invention discloses an intelligent control management method and device for an energy storage system, and particularly relates to the field of energy, and the method comprises the steps: S1, collection region determination, S2, real-time data collection, S3, data preprocessing and transmission, S4, data analysis, S5, control strategy making, and S6, control instruction execution. Multiple indexes are comprehensively calculated through the data analysis module, the wind-solar power generation characteristics and relations are comprehensively considered, the energy storage system can accurately match the wind-solar power generation state, meanwhile, the evaluation decision-making unit is combined with historical data and real-time weather to predict the sum of wind-solar power generation power, dynamically evaluate the energy storage bearing capacity and plan a charging and discharging strategy in advance, and the energy storage efficiency is improved. Energy waste is avoided, the energy utilization efficiency is improved, finally, the energy storage charging and discharging power is rapidly adjusted through a strategy generation unit, the strategy is monitored and optimized in real time, the stability and reliability of the system are enhanced, and the impact on a power grid is reduced.

The invention belongs to the field of coal quality analysis, and discloses a microwave heating type coal quality on-line analysis device and application thereof.The device comprises an on-line sample collecting and preparing module, a microwave heating module, a weight collecting module and an analysis control module, and the microwave heating module comprises a microwave drying cavity, a microwave devolatilization cavity and a microwave ashing cavity; each chamber is connected with a microwave temperature control unit for independent temperature control; the online sampling and sample preparation module is used for collecting coal from a coal conveying line in real time, crushing the coal into standard coal samples, and feeding the standard coal samples into the corresponding treatment chambers for microwave heating treatment; the microwave ashing cavity is further connected with an oxygen supply unit, and the oxygen supply unit is used for providing an oxygen-enriched environment for the microwave ashing cavity; the weight acquisition module is connected with the analysis control module and is used for acquiring the weight of the standardized coal sample before and after heating; and the analysis control module is used for controlling the microwave temperature control unit to adjust the temperature of the corresponding chamber and obtain various indexes of the standardized coal sample. According to the invention, the efficient and full-automatic microwave heating coal quality online sampling analysis effect can be realized.

The invention discloses an electric energy meter intelligent early warning system based on big data analysis, and relates to the technical field of safety early warning for power production, and the system comprises an acquisition monitoring device, an analysis control device, a display device, and an intelligent early warning device. The intelligent early warning device comprises an automatic alarm, the automatic alarm is electrically connected with the analysis control device, and the automatic alarm is used for alarming when the analysis control device analyzes that the meter body temperature, the meter body humidity and the meter voltage of the electric energy meter exceed a preset safety data range and only one item of data exceeds preset data. Namely, the automatic alarm gives an alarm sound to remind an electric power maintenance worker to carry out safety inspection; the system supports on-line real-time monitoring of the electric meter, and the system has a timely early warning function on the fault of the electric meter, so that the system can timely remind a worker, the worker can timely process the fault of the electric meter, and the worker can timely take measures to guarantee safe and normal operation of the electric meter according to the received judgment abnormal early warning.

The invention discloses an automobile armrest dynamic damping adjustment system and method based on multi-sensor fusion, particularly relates to the technical field of automobile armrest adjustment, and is used for solving the problem of poor armrest damping self-adaptive adjustment precision. Multi-source data such as vehicle postures, power behaviors, contact behaviors and hand torque are collected, resampling, denoising and phase compensation are carried out, a fusion state vector is constructed, the future state of the vehicle is predicted based on a short-time historical state sequence, state increment is extracted, and a dynamic risk index is formed; a current state and a risk trend are combined to construct an optimization model integrating supporting, smoothing and restraining, controller parameters are dynamically adjusted through a fuzzy reasoning mechanism, self-adaptive adjustment of armrest damping is achieved, and in the damping execution process, cooperative features and dynamic correction strategies of a control instruction and feedback output are analyzed, so that the damping performance is improved. Therefore, the response precision and stability of the dynamic damping of the automobile armrest under the complex working condition are improved.

The invention discloses an automatic circulating water control system based on an intelligent instrument, which relates to the technical field of circulating water treatment and comprises a front-end sensor detection module, a data communication module, a data analysis control module, a water quality regulation module, an execution module and a man-machine interaction module, and the front-end sensor detection module is used for performing water quality online detection on a water sample passing through an outlet pipeline of the circulating water pump through a front-end sensor to obtain online water quality index data of the circulating water. Through real-time monitoring of the front-end sensor and accurate analysis of the data analysis control module, abnormal conditions in the circulating water system can be found in time, corresponding measures can be taken rapidly for adjustment, meanwhile, the execution module can accurately execute control instructions, it is ensured that the system is in a stable running state all the time, and the service life of the system is prolonged. The reliability of the system is greatly improved, and production interruption and equipment damage caused by system faults are reduced.

The invention provides an image sensor for online detection of oil quality, and the image sensor comprises a sensor body which is provided with an oil inlet, an oil outlet and a detection chamber, the detection chamber is arranged between the oil inlet and the oil outlet, and the detection chamber comprises a cavity and a transparent protection plate arranged at the top of the cavity; the magnetic adsorption assembly comprises an annular electromagnet arranged above the transparent protection plate; the image acquisition assembly is arranged on the upper surface of the transparent protection plate and comprises an imaging lens, a CMOS sensor and an annular light source; the processing circuit is used for preprocessing and analyzing the acquired optical image; and the control circuit is used for controlling the coil electrifying preset time so as to adsorb the abrasive particles and control image acquisition. According to the invention, real-time and accurate detection of the abrasive particles in the oil can be realized, and the morphology feature information of the abrasive particles can be obtained.

The invention relates to the technical field of data mining, in particular to a multi-dimensional data mining and analysis system based on flight simulation, which comprises a flight path characteristic analysis module, an action characteristic correlation analysis module, a key action offset prediction module and a training task distribution module. According to the method, the accuracy of track offset dynamic evaluation is improved by decomposing flight track change parameters and extracting track offset characteristic distribution, the capture capability of track change rules is remarkably enhanced, the correlation degree of control torque change and control surface offset is analyzed, and the key correlation between actions and tracks is quantified; the method lays a foundation for prediction of a high-frequency offset action distribution range, combines action characteristic parameter trend analysis, clears an offset direction and amplitude difference, optimizes control surface adjustment and control moment distribution, enables training distribution to meet requirements, realizes comprehensive coverage of flight data from law discovery to task optimization, improves decision precision and training efficiency, and improves the prediction efficiency. And meanwhile, the flight safety is enhanced.

The invention discloses a central air conditioner energy-saving control system and method based on artificial intelligence, relates to the technical field of air conditioner energy-saving control, and solves the technical problems that in the prior art, when a central air conditioner operates, temperature detection evaluation cannot be conducted on the central air conditioner, and it cannot be guaranteed that the temperature detection performance of the central air conditioner meets the requirement. The marking module is used for marking a central air conditioner coverage area as a control area, obtaining a main circulation route and a secondary circulation route of the control area and determining an air supply point; taking the air supply point as a starting point and the main circulation route as a track, dividing the main circulation route into a plurality of sub-positions, and according to the sub-positions, inferring air supply point effects, high effects, energy-saving regulation and control, low effects and frequency conversion control so as to meet control requirements; temperature floating speed deviation information and temperature floating control area information are collected, and temperature detection analysis control is conducted according to information analysis.

The invention discloses a demand management control method and system based on real-time rolling prediction and decision analysis, and relates to the field of energy scheduling, comprising load prediction of a demand gateway line and power demand trend analysis of a controlled object. Determining a power demand control strategy by using a load prediction result and a power demand trend analysis result and through gate demand periodical rolling prediction and optimization calculation; constructing a sensitivity matrix between the power variation between the demand gateway and the load line and the power variation of the load line; using the sensitivity matrix to provide a demand out-of-limit value of a demand gateway line and support construction of a demand out-of-limit analysis control strategy; and performing power calculation by combining an equipment energy efficiency model and line load characteristics, and dynamically adjusting an energy distribution strategy of each piece of equipment. According to the invention, accurate prediction and dynamic adjustment of energy demands can be realized, energy distribution can be optimized, and operation cost can be reduced.

The invention relates to the technical field of marketing data analysis, and discloses a marketing analysis control method and system. The method comprises the steps of collecting multi-dimensional interaction data of a target user group; performing time sequence alignment processing on the multi-dimensional interaction data, eliminating asynchronous interference caused by acquisition time offset, and generating a synchronization behavior matrix; potential mode mining is carried out based on the synchronization behavior matrix, behavior clusters with relevance are recognized, and each behavior cluster corresponds to one user decision mode; constructing a dynamic weight model according to the user decision-making mode, calculating the contribution degree weight of the data of each dimension in the current marketing scene, and updating the model by adopting an incremental learning mechanism in combination with real-time feedback data; establishing a marketing strategy mapping table, and selecting an adaptive strategy group according to a current optimal weight combination output by the model; when the strategy is executed, a responsivity index is monitored synchronously, if the responsivity index is lower than a dynamic threshold value, re-matching is triggered, incremental data is fed back to a potential mode mining link, and a closed-loop optimization mechanism is formed.

The invention relates to the technical field of charging pile monitoring, and discloses a charging pile spontaneous combustion prevention system based on electric spark monitoring, and the system comprises a charging line monitoring module which is used for monitoring the state of a charging line, and obtaining and storing the monitoring data of the charging line; the analysis control module is used for analyzing the charging line monitoring data, judging whether the charging line needs to be replaced or not according to an analysis result, monitoring the state of the charging line through the charging line monitoring module, and obtaining and storing the charging line monitoring data; charging line monitoring data are analyzed through the analysis control module, the actual line loss coefficient and the real-time abnormal index are obtained, whether the charging line needs to be replaced or not is judged according to the actual line loss coefficient and the real-time abnormal index, and a worker can directly overhaul or replace the charging line according to a result; manual judgment after detection of the charging wire is not needed, so that the processing time is shortened, and the charging pile can be recovered to be used as soon as possible; fire disasters caused by electric sparks due to damage of the charging wire are prevented.

The invention relates to the technical field of environment monitoring and automatic control, and specifically provides a dust monitoring analysis control system based on a port bulk cargo storage yard electronic greenhouse. The system comprises an environment multi-dimensional sensing module, a dust field dynamic reconstruction module, a dust suppression demand prediction module and a precise execution control module, and through multi-dimensional data acquisition, dynamic three-dimensional dust field modeling, future dust suppression demand prediction and space and time sequence optimization regulation and control of spraying resources, the dust suppression demand prediction and the precise execution control module are optimized. Accurate monitoring and prediction of dust concentration and on-demand throwing of dust suppression resources are achieved, and control timeliness, foresight and operation energy efficiency are effectively improved.

The invention relates to the technical field of intelligent agricultural intelligent irrigation control, and discloses an intelligent irrigation analysis control system based on a crop growth period. The system comprises a crop image sequence, soil humidity profile data and a regional meteorological sequence. And the data analysis module is used for performing multi-stage growth state interpretation on the image sequence, generating a crop structure state profile in each growth period, performing vertical moisture exchange identification on the soil humidity data, and generating a root zone moisture dynamic map. And the habitat mapping and stress evaluation module synchronously executes habitat adaptability mapping and moisture stress risk mapping. And the irrigation decision module corrects the habitat mapping result according to the stress evaluation result and generates an irrigation decision profile. And the control instruction generation module generates a partition valve control instruction in combination with the decision profile and the alternate irrigation rule. According to the invention, deep fusion analysis of the crop growth state, the root zone moisture and the meteorological environment is realized, and the irrigation decision accuracy and the water resource utilization efficiency are improved.