2576results about "Simultaneous control of multiple variables" patented technology

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention belongs to the technical field of cleaning and dirt prevention and removal, and discloses a control system and a control method of an ultrasonic cleaning device, stable energy coupling and dead-corner-free cleaning are realized through double-closed-loop driving and dynamic modulation in an ultrasonic cleaning link, and the uniformity of tank liquid cavitation intensity is remarkably improved. Meanwhile, control strategies are customized in the aspects of a rinsing link, a water cutting and drying link, liquid level control, manipulator control and the like, so that the defects of high energy consumption, incomplete drying, low rinsing efficiency, manipulator conflict and the like in the prior art are overcome while the surface and the interior of the workpiece are thoroughly cleaned; and the cleaning effects of high cleanliness, complete drying and no physical or chemical damage are ensured.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention relates to the technical field of agricultural intelligent equipment, and discloses a multi-mode sensing fusion control system for an aerogel greenhouse heat preservation quilt. An environment multi-mode monitoring module of the system collects internal temperature and humidity distribution, illumination intensity gradient, soil moisture content profile and external meteorological change data of a greenhouse in real time to generate an environment comprehensive monitoring data set; based on the data set, the thermal insulation mode decision module analyzes the difference between the thermal energy loss rate and the thermal insulation demand of different areas in combination with the physical characteristic parameters and historical regulation and control records of the aerogel thermal insulation quilt, matches a dynamic thermal insulation strategy and generates a multi-stage partition regulation and control instruction; the cooperative parameter optimization module calculates a combination scheme of the unfolding angle, the coverage area and the thickness of the heat preservation quilt, and adjusts a partition cooperative control parameter set; the multi-mode execution controller generates a heat preservation quilt dynamic regulation and control map; the anomaly fusion diagnosis module monitors anomalies and generates a set of regulation error correction factors. The system realizes fine, dynamic and intelligent regulation and control of greenhouse heat preservation.

The invention belongs to the technical field of intelligent monitoring and control, and relates to an intelligent monitoring and control system for an agricultural greenhouse. According to the method, multi-environment factor data in a greenhouse is collected, regulation and control actions are encoded into vectors and sampled to construct a sample set, after regulation and control are executed, an environment fluctuation convergence state is detected to construct an action-environment mapping relation, and then candidate actions are generated based on an optimal interval of a crop stage; and selecting an optimal regulation and control combination through a prediction model and real-time state scoring. The system solves the problems of dependence on manpower, slow response, low precision, difficulty in multi-device cooperation, difficulty in dynamic adaptation to crop growth stage changes, low stability and energy efficiency and the like in traditional greenhouse environment regulation and control, and can realize rapid and accurate regulation of multiple factors such as temperature, light, water, gas and the like; and the environmental control automation level, the crop growth quality and the resource utilization efficiency are remarkably improved.

The invention discloses a ground wire multi-machine synchronous traction pay-off scheduling method and system, relates to the technical field of high-voltage transmission line construction, and is used for solving the problem of insufficient multi-machine real-time tension synchronization precision in the existing method. A tension sensor and a speed sensor which are arranged on each traction device collect a tension value, a traction speed value and position coordinate data of a ground wire in real time, and transmit the data to a central scheduling controller; establishing a multi-machine unified time synchronization reference and tension reference model, calibrating a timestamp through a GPS module, and initializing a threshold value based on historical parameters; comparing tension values one by one to calculate a deviation vector sequence; activating a compensation algorithm to execute a speed fine adjustment and tension compensation instruction; iteratively calculating the feedback adjustment value until the deviation is stable; and integrating the synchronous data to the overall paying-off scheduling model to dynamically update the path planning and speed curve. According to the method and the system, the problem of insufficient multi-machine real-time tension synchronization precision in the prior art is effectively solved.

The embodiment of the invention provides a fruit and vegetable ripening acceleration system and a fruit and vegetable ripening acceleration method, which are used for realizing stable and controllable ripening acceleration of fruits and vegetables in a transportation process. The method comprises the steps that when the fruit and vegetable ripening acceleration system is started, a current ripening acceleration mode of fruit and vegetable ripening acceleration operation is determined; wherein the current ripening mode comprises a preset temperature range, a preset ethylene concentration range and an EC fan strategy; acquiring a real-time temperature value, a real-time ethylene concentration value and transportation time for transporting fruits and vegetables in the refrigerated container; and according to the transportation time, the real-time temperature value, the real-time ethylene concentration value, a preset temperature range, a preset ethylene concentration range and an EC fan strategy, a temperature regulation and control system, an ethylene regulation and control system and an EC fan are controlled through a main control system, so that the temperature value of the output end in the refrigerated container meets the preset temperature range. The ethylene concentration value of the output end in the refrigerated container meets a preset ethylene concentration range.

The invention relates to the field of highway construction, in particular to an intelligent asphalt pavement rolling control method and system, and the method comprises the steps: deploying machine vision equipment to collect an image of a just-paved asphalt mixture, recognizing the segregation degree in combination with a convolutional neural network, and generating a segregation distribution thermodynamic diagram with geographic coordinates; the road roller is integrated with a positioning sensor, a temperature sensor, a vibration sensor and a speed sensor, relevant parameters are collected, and the real-time compaction degree is calculated. The thermodynamic diagram and the compaction degree are subjected to space-time matching, optimal operation parameters are calculated through a dynamic coupling model on the basis of data such as the segregation degree and the temperature of a target area, the optimal operation parameters are wirelessly sent to a road roller control system to adjust speed and vibration parameters, and a road roller feeds back the new compaction degree after execution to form closed-loop control. The problem that separation and compaction control are disjointed in the prior art is solved, separation information and the compaction process are deeply fused, and underpressure of a separation area and overpressure of a uniform area are avoided.

The invention relates to the technical field of control systems, and discloses a thick broad-bean sauce production control system and method, which are used for solving the problems of quality fluctuation and pollution risk caused by insufficient precision control in the existing thick broad-bean sauce fermentation process. The method comprises the following steps: acquiring temperature, humidity, pH value and microbial activity indexes in a fermentation process in real time to form a dynamic parameter data set; analyzing the deviation degree of the data set, calculating the matching degree with a standard fermentation curve, and identifying potential unstable factors; generating a preliminary adjustment instruction based on the deviation result, and performing automatic fine adjustment on the temperature and humidity to stabilize the environment; monitoring the activity change of the adjusted microorganisms, feeding back and verifying a control effect through a sensor, and calculating an activity recovery rate; optimizing fermentation model parameters, and iteratively adjusting a control algorithm according to a recovery rate to realize self-adaptive precision improvement; and outputting a final control report, and feeding back optimized parameters to the system to realize closed-loop precision control.

The invention discloses an agricultural environment intelligent control system based on artificial intelligence and a control method thereof, and belongs to the technical field of agricultural intelligent control and artificial intelligence application. According to the method, agricultural sensor data and image data are jointly packaged into a standardized AI input structure by adopting prompt engineering and semantic modeling means; a general large language model or an image recognition model is accessed, and comprehensive evaluation of complex crop states is realized through customizing a Prompt semantic structure; constructing a natural language analysis engine, and converting the control suggestion returned by the AI into a structured command; the Agent module is used for completing control opportunity judgment, control path planning and effect feedback adjustment, and strategic intervention on the whole agricultural control process is achieved.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The invention relates to the technical field of battery testing and environment control, in particular to a lithium oxygen battery testing environment regulation and control system, and aims to solve the problems that in lithium oxygen battery testing, environment parameter regulation and control response is lagged, precision is insufficient, and multi-parameter collaborative decoupling is poor. The system is a combination of a battery test unit, a gas environment regulation and sensing module and an intelligent cooperative control unit. A multivariable decoupling algorithm is embedded in the intelligent cooperative control unit, and dynamic high-precision, wide-range and quick-response cooperative regulation and control of the oxygen concentration, humidity and pressure of the cavity are achieved. The system improves the precision and stability of environment regulation and control, and provides a reliable test environment for lithium oxygen battery research.

The invention provides an autonomous adjustment method and system for a thermal power plant desulfurization wastewater dosing device, and the method comprises the steps: collecting multiple parameters such as flow, pH value, heavy metal ion and suspended matter concentration in real time through on-line monitoring instruments arranged at a water inlet end, a reaction unit and a water outlet end; constructing and continuously updating a dynamic mathematical model based on historical data and a reaction mechanism; a control system integrating intelligent algorithms such as a neural network and fuzzy control is utilized, and optimal dosing parameters are calculated in combination with the model and real-time data; a dosing pump or a valve is driven to execute precise dosing; and finally, closed-loop optimization is formed through effluent quality feedback. According to the invention, automatic, accurate and self-adaptive adjustment of the dosing device is realized, the defects that a traditional mode depends on manpower and response lags behind are overcome, stable and standard discharge of desulfurization wastewater is effectively guaranteed, meanwhile, medicament consumption and operation cost are remarkably reduced, and the automation level and the capability of coping with working condition fluctuation of the system are improved.

The invention discloses a hot-pressing control method and system for a flexible circuit board, and particularly relates to the technical field of flexible electronic manufacturing process control, and the method comprises the following steps: calculating a heat conduction equilibrium coefficient through collecting the temperature and heat flux density data of each region in the flexible circuit board, and achieving the real-time evaluation of a heat diffusion characteristic; in combination with an infrared scanning mode, acquiring reflectivity data, analyzing dynamic change of the reflectivity data along with time, and identifying an abnormal region in which coating residues possibly exist; further comparing the position relationship between the thermal diffusion anomaly and the infrared reflection anomaly, screening a space overlapping region as a high-risk point, and executing a preset regulation and control strategy; according to the method, the heat conduction equilibrium coefficient is calculated by collecting heat flow and temperature data, the coating residue abnormal area is recognized by combining infrared reflectivity time sequence characteristics, the high-risk area is screened based on the space overlap ratio for dynamic regulation and control, accurate recognition, differential control and closed-loop optimization of hot pressing defects are achieved, and the hot pressing quality and consistency are improved.

The invention discloses a power distribution station multi-point environment control system and method, and relates to the technical field of power system automation, and the method comprises the following steps: building a time-frequency phase-locked observation baseline based on the scattering relation between an infrared band and water vapor under the condition of sudden humidity increase, inverting aerosol particle size distribution and concentration distribution, and generating a multi-dimensional scattering phase residual field; according to the multi-dimensional scattering phase residual field, a real radiation field is reconstructed by adopting multi-path reverse ray variation solution and combining polarization dual-angle scanning, and a reflection saturation risk spectrum is generated. According to the method, a real radiation field is reconstructed through multi-dimensional observation and inversion, a consistency graph is generated by fusing multi-source data, a misjudgment boundary is accurately identified, a control margin and a switching condition are constructed based on residual playback and time correction, and a steady-state closed-loop control chain is formed in combination with a dynamic regulation and control mechanism. The self-adaptive precise regulation and control of the power distribution station environment and the stability guarantee of the thermal field are realized.

The invention discloses a temperature and humidity cooperative control method for an air source heat pump drying room, and relates to the technical field of energy conservation, environmental protection and intelligent manufacturing. The method comprises the following steps: acquiring gas heat state parameters and material and environment characteristic parameters in an air source heat pump drying room, and constructing a multi-source state vector; calculating a dynamic response coefficient group and a material evaporation influence coefficient based on continuous sampling data, and establishing a cross response model; a control compensation signal is generated and corrected according to the disturbance estimator, and the comprehensive control quantity is input into an execution module of the air source heat pump system; a dynamic response coefficient group and a lag correction parameter are updated through a periodic feedback data set, and dynamic coordination between temperature and humidity variables is achieved; the response stability of temperature and humidity control can be maintained under the condition that the material moisture content, the stacking density and the external environment condition change, system oscillation and energy consumption deviation are reduced, and the energy efficiency level of the air source heat pump drying process and the product drying uniformity are improved.

The invention provides a greenhouse water, fertilizer and pesticide optimization control system and method based on environment-crop-input multi-element coupling, and the method comprises the steps: deploying a sensor network, collecting greenhouse data, processing the greenhouse data, generating a space-time continuous data set, and carrying out the optimization control of the greenhouse water, fertilizer and pesticide on the basis of the generated space-time continuous data set. And quantifying a dynamic causal chain of the environment, the crops and the input, generating a decision rule base, fusing the decision rule base with a real-time simulation result of the three-dimensional digital twinborn model of the root zone, and generating a water, pesticide and fertilizer regulation and control instruction. Space-time continuous acquisition and fusion processing of greenhouse environment-crop-input product data are realized through a multi-modal sensor network, a decision rule base containing a dynamic causal chain is constructed, real-time simulation of a root zone three-dimensional digital twinborn model and Bayesian causal reasoning are combined, an accurate water, fertilizer and pesticide regulation and control instruction is generated, and the accuracy of the water, fertilizer and pesticide regulation and control is improved. The nonlinear causal relationship among multiple elements is effectively quantified, and the decision scientificity is improved.

The invention discloses a reconfigurable casting process module hierarchical control method, which comprises the following steps of: firstly, acquiring a multi-source sensor data stream generated in a casting task execution process, the data comprising key process parameters such as temperature, pressure, flow velocity and the like; based on this data stream, an initial condition feature set for characterizing the dynamic condition is determined. And then, carrying out segmented working condition optimization processing on the feature set by utilizing a hierarchical decoupling regulation model, and obtaining an optimized working condition feature set through system coupling dissociation and random disturbance stripping. And finally, performing feature reconstruction processing on the optimization feature set, and generating an optimization control instruction accurately matched with the current casting process to adjust the pouring speed, the cooling rate or the pressure parameter in real time, thereby realizing closed-loop optimization control of the casting process. According to the method, the control precision and the process stability under complex working conditions are effectively improved.

The invention discloses an indoor environment automatic adjustment method based on self-supervised learning, and the method comprises the steps: collecting the multi-modal data of an indoor environment and a user behavior, completing the synchronization and standardization processing, and constructing a causal graph model which comprises an environment variable, a user behavior variable and an adjustment response variable; adjusting causal representation vectors are generated through reverse causal modeling, building structure parameters, user preferences and climate regionalization information are fused, and a unified cross-modal embedding space is constructed; and further training an adjustment strategy migration model based on a federal migration learning mechanism, and generating a personalized control instruction in combination with local terminal data. The method has the advantages of being high in causal reasoning ability, accurate in cross-modal fusion expression, high in control strategy adaptability and the like, and is suitable for application scenes such as intelligent buildings, energy-saving air conditioning systems and environment sensing type home furnishing.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention provides a hollow fiber membrane cleaning pressure self-adaptive adjustment control method and system, and relates to the technical field of control, and the method comprises the following steps: in a set temperature range, washing a water inlet side and a water production side through a constant flow, and determining a target transmembrane pressure difference interval; according to the transmembrane pressure difference deviation and the change rate, the rotating speed of a water inlet pump and the opening degree of a concentrated water discharge valve are cooperatively regulated and controlled according to a valve-first and pump-second sequence; when the transmembrane pressure difference reaches the upper limit and pollution is reversible, an impact cleaning sequence is generated, the opening of a concentrated water valve is increased, and the rotating speed of a water inlet pump is increased to form shear scouring. According to the invention, the self-adaptive adjustment of the cleaning pressure in the membrane pollution process is realized, and the membrane cleaning efficiency is improved.

The invention provides an intelligent shelter equipment operation state monitoring method and system based on machine learning, and the method comprises the steps: firstly obtaining a synchronous operation real-time signal set which comprises the real-time parameters of all operation parts, the interaction between equipment and the comprehensive influence of a shelter environment, and carrying out the mutual feedback correlation processing of the synchronous operation real-time signal set to extract a mutual feedback correlation feature set; inputting the set into an operation state deduction model to generate a short-term operation state evolution sequence, identifying a potential abnormal evolution trend and determining an abnormal association influence range based on the short-term operation state evolution sequence, and generating collaborative regulation and control demand information; then, inputting the collaborative regulation demand information and the mutual feedback correlation feature set into a collaborative regulation model to generate a multi-device collaborative operation regulation instruction, after the multi-device collaborative operation regulation instruction is sent, collecting an execution feedback signal, updating a feature extraction rule, and adjusting model parameters; the comprehensive, dynamic and accurate monitoring and regulation of the operation state of the square cabin equipment are realized, and the stability and the reliability of the equipment operation are improved.

The invention relates to the technical field of facility agriculture environment control, and discloses an anoectochilus formosanus growth environment temperature and humidity non-electrical variable adjustment method, which comprises the following steps: acquiring dry-bulb temperature and relative humidity, calculating real-time dew point temperature, and generating a dew point approximation index; when the dry-bulb temperature is higher than a set value to trigger cooling, monitoring the relationship between the index and a security defense threshold value; when the index is lower than a threshold value, the humidification starting permission is locked, and ventilation is driven; when the index is higher than a threshold value, locking is relieved, and humidification output is limited according to the dry-bulb temperature drop rate, so that the dew point rise rate is lower than the dry-bulb temperature drop rate. By establishing the dew point defense boundary and asynchronous decoupling control logic, the problem of transient condensation caused by response time constant mismatch in a multivariable coupling system is solved; the risk of microscopic condensation of leaf surfaces is eliminated, and high-precision lossless regulation and control of the anoectochilus formosanus growth environment are realized.

The invention relates to the technical field of concrete curing, in particular to a concrete curing environment control system with intelligent temperature control and humidity adjustment functions. Comprising a data acquisition and storage unit; the regulation and control decision-making unit is used for calculating a deviation value between the current environment and a target maintenance condition through an improved sectional type PID (Proportion Integration Differentiation) regulation algorithm; a temperature and humidity execution unit; and a man-machine interaction unit. According to the invention, through the distributed air temperature and humidity sensor array and the embedded temperature sensor in the concrete, the air temperature and humidity of the curing space and the internal temperature data of the concrete are collected, and the initial setting stage, the final setting stage and the hardening stage are judged based on the internal temperature change rate and the internal surface temperature difference; the PID parameter adjustment interval corresponding to the concrete mark is called in combination with the stage, smooth parameter switching is achieved, in the concrete curing scene, the heat release rate and strength development requirements of different hydration stages can be accurately met, and the situation that a regulation and control strategy is disjointed with the actual concrete curing requirement is avoided.

The invention discloses a temperature monitoring control method and system for a communication machine room or a data center machine room, and relates to the field of machine room temperature and humidity monitoring, and the method comprises the steps: S1, selecting a proper temperature and humidity sensor; s2, drawing three-dimensional models of the communication machine room and the data center machine room; s3, determining layout point locations of the temperature and humidity sensors; s4, formulating temperature and humidity control measures; s5, continuous temperature and humidity data acquisition is carried out; s6, collecting and processing the data; s7, setting a temperature threshold value and an early warning mechanism; and S8, carrying out an analog simulation experiment. According to the method, a three-dimensional model analysis process specially aiming at the machine room is arranged, monitoring sensors corresponding to key positions can be specifically configured according to the actual three-dimensional model condition, some existing heat dissipation structures can be optimized according to the three-dimensional model, and in the actual application process, the heat dissipation efficiency can be improved. And a better temperature monitoring effect and a better temperature control effect are achieved.

The invention discloses a landscaping environment automatic monitoring and feedback adjusting method, and belongs to landscaping and ecological environment management. Comprising the following steps: acquiring omnibearing perception data of a garden environment and area position information of each perception area; constructing a hierarchical spatio-temporal state prediction model, generating a prediction result of garden multi-dimensional spatio-temporal change, and performing development trend analysis; dynamically correcting the monitoring data, and updating local prediction parameters of the space-time modeling module; determining the priority of environmental adjustment of each region and generating a resource allocation scheme, and establishing an equipment collaborative scheduling strategy and an abnormal condition plan; controlling the execution adjustment module to sequentially move to each target area needing to be adjusted; configuring a corresponding adjustment strategy based on the environmental parameter deviation and the plant growth state of the region; accurate adjustment of moisture, nutrients and environmental factors of the target area is carried out by utilizing multi-device cooperative control; and judging whether the adjusting effect reaches a preset environment standard or not, and monitoring mutual influence among the areas.

The invention discloses a smelting wastewater trace arsenic real-time monitoring method based on an electrochemical sensing array, and relates to the technical field of electrochemical detection.The method comprises the steps that bypass sample flow is established through a peristaltic pump and pre-filtered, and stable sample flow is obtained; the sample flow is divided into a first passage and a second passage, and a form distinguishing sample flow is obtained after oxidation setting and medium equalization; performing short-time adsorption equilibrium to obtain an interface state parameter set, executing multi-segment coded deposition control based on the parameter set, and collecting a dissolution current curve; a dissolution signal set is obtained by applying single-frequency perturbation signals, characteristic peak parameters are extracted and subjected to correlation calculation with a multi-section response matrix, the total arsenic concentration and the pentavalent arsenic concentration are calculated through a weighted inversion algorithm, and a confidence interval is obtained; and calculating the concentration of the trivalent arsenic and determining an uncertainty range according to the two. According to the method, the quality judgment result is linked with the dynamic adjustment of the sampling flow, the potential amplitude and the deposition duration, so that the self-adaptive optimization control of the detection process is realized.

The invention relates to the technical field of fluid control, and discloses a split type mass flow controller calibration method, system and equipment. According to the method, a control unit, a valve unit, a detection unit and a fluid passage unit are connected through detachable interfaces through a split type modular design, and the problem that a traditional integrated structure is difficult to calibrate and maintain is solved. A flow mapping relation model is established by adopting temperature difference and pressure data, and dynamic correction is performed in combination with an environment temperature compensation coefficient, so that the detection precision is remarkably improved. Through cooperative control of a flow closed loop and a valve element displacement closed loop, precise adjustment of the opening degree of the valve is achieved, and the stability and the dynamic response performance of flow control are effectively improved. The system has the advantages of being flexible in structure, high in calibration precision and high in environmental adaptability, and is suitable for the high-precision flow control field of semiconductors, biological medicine and the like.