3008results about How to "Low running cost" patented technology

The invention relates to the field of temperature control, in particular to an automatic control method and system for a constant-temperature test room. The method comprises the following steps: acquiring real-time indoor operation monitoring parameters and external environment multi-dimensional data of a constant-temperature test room; performing indoor operation state analysis on the real-time indoor operation monitoring parameters, and constructing a laboratory dynamic state space model; carrying out multi-time-point environment temperature fluctuation calculation on the external environment multi-dimensional data, and carrying out nonlinear correlation learning to generate indoor state-environment nonlinear correlation characteristics; carrying out dynamic temperature evolution simulation on the laboratory dynamic state space model based on the indoor state-environment nonlinear correlation characteristics, and carrying out dynamic temperature evolution prediction to obtain temperature prediction values of a plurality of regions; constant-temperature deviation compensation calculation is carried out on the temperature prediction values of the multiple areas, then intelligent temperature control compensation is carried out, and an intelligent temperature control strategy is constructed; according to the invention, efficient and accurate constant-temperature automatic control is realized.

The invention discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, and relates to the technical field of coal mine dust diffusion simulation and control, and the method comprises the following steps: collecting environmental data such as dust concentration, ventilation flow velocity, temperature, humidity and equipment operation state through a monitoring system in a coal mine; and cleaning and formatting processing are carried out in combination with historical working condition data. According to the method, reinforcement learning and virtual extreme scene simulation are utilized, the recognition and emergency response capability of the system to edge working conditions is improved, dust diffusion and explosion risks are effectively reduced, and mine safety is guaranteed. By dynamically adjusting the ventilation and water spraying system, efficient utilization of resources is achieved, energy consumption and operation cost are reduced, and the service life of equipment is prolonged. The system has an adaptive learning capability, can optimize a control strategy according to monitoring data and historical events, can automatically adjust even if the environment changes, improves robustness and flexibility, and reduces manual intervention.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention provides an intelligent dosing method and system, and the method comprises the following steps: building a multi-source water quality data collection and preprocessing unit, and achieving high-quality data acquisition through employing a self-adaptive sampling frequency adjustment and data complementation algorithm; constructing a parameter feature extraction and correlation analysis system, and establishing a parameter coupling network; an intelligent dosing control strategy optimization system is constructed, and accurate control over the dosing process is achieved through deep reinforcement learning and a multi-objective optimization algorithm; a time-frequency domain dual evaluation system and a space-time two-dimensional feature extraction framework are established, and control effect evaluation and system characteristic analysis are achieved. According to the invention, the accuracy and economical efficiency of dosing control are obviously improved, the standard reaching rate of the effluent quality is improved to 99.5% or above, the use efficiency of chemicals is improved by 40-50%, and the operation cost is reduced by 35-45%.

The invention relates to an LLM-enhanced intelligent micro-service migration method and system in a cloud edge environment, and the method comprises the steps: analyzing and judging whether a current exception needs to be subjected to micro-service migration or not according to a historical exception event needing to be migrated through a to-be-migrated micro-service positioning method based on LLM and LSTM, and accurately positioning a to-be-migrated micro-service causing the exception if the micro-service needs to be migrated; a multi-target micro-service migration decision-making method based on dynamic GCN and BO is adopted, and appropriate nodes are intelligently selected for each micro-service to be migrated for migration according to the long-term performance and operation cost of the micro-service. According to the method, the accuracy of judging whether the current exception needs to be migrated or not is remarkably improved, and the to-be-migrated micro-service causing the exception can be accurately positioned. The problems of network congestion, service interruption, performance reduction, unbalanced resource utilization and the like possibly caused by frequent migration are effectively avoided, so that the long-term stability of the system performance after micro-service migration is realized, and the operation cost is remarkably reduced.

The invention is suitable for the technical field of anomaly detection, and particularly relates to a cloud edge multi-mode abnormal event detection system based on a dynamic decision-making mechanism, and the system comprises a data collection module which is used for collecting videos and audios, and obtaining multi-mode data; the data preprocessing module is used for preprocessing to obtain preprocessed data; the key feature extraction module is used for carrying out key frame feature extraction and key frequency band feature extraction; the multi-mode anomaly detection module is used for carrying out anomaly judgment; and the cloud large-model intelligent processing module is used for analyzing the detected abnormal fragments and outputting a generative text result. According to the cloud edge multi-mode abnormal event detection system based on the dynamic decision-making mechanism provided by the invention, more comprehensive event understanding and anomaly detection are provided through combination of video and audio data. And deep features are extracted at the edge end by using the convolutional neural network, so that the detection accuracy is improved, and the false alarm rate is effectively reduced.

The invention relates to a superconductive annular air-core reactor structure optimization method, a medium and a program product, and the optimization method comprises the steps: optimizing the structure parameters of a superconductive reactor through an MATLAB and COMSOL joint simulation technology, and carrying out the optimization of the structure parameters of the superconductive reactor through a multi-objective genetic algorithm, thereby achieving the optimization of the structure parameters of the superconductive reactor. Iterative optimization is conducted on structural parameters such as the coil inner diameter, the surrounding radius, the number of turns of the coils, the number of the coils and the number of parallel branches of the electric reactor, the optimization target comprises the minimum strip use amount and alternating current loss, and in the optimal solution set, according to the strip use amount and the alternating current loss, the strip cost and the refrigeration cost are calculated. And finally, outputting an optimal structure parameter meeting a design index so as to realize the minimization of the total cost. Compared with the prior art, the method can be used for structural optimization of the superconducting reactors with different capacities, structural parameters with the lowest cost under different indexes can be obtained by setting constraint conditions, and economic benefits of reactor design are improved.

The invention belongs to the technical field of distributed energy storage control, and discloses an optimized operation method and system for a multi-network-constructed energy storage system, and the method comprises the steps: carrying out the equivalence of the multi-network-constructed energy storage system, and obtaining an equivalent energy storage system; wherein the equivalent energy storage system comprises an isomorphic network-following converter, an equivalent impedance R and an ideal voltage source which are sequentially connected in series; based on the equivalent energy storage system, obtaining a small signal stability boundary condition by adopting a Routh criterion; and taking the minimum operation cost as an objective function, solving the objective function under operation constraints including small signal stability boundary conditions, and obtaining J and D of each network construction type converter at different moments, so that each network construction type converter operates under the corresponding J and D. The method is suitable for optimized operation configuration of an energy storage system comprising a plurality of network following-constructing converters, and can effectively promote advantage complementation of renewable energy sources and energy storage, thereby improving system stability and operation economy.

The invention discloses an energy storage scheduling method and system based on a service area load and renewable energy power generation prediction, and the method carries out the prediction of the load demand of a service area based on a long short-term memory (LSTM) network, and combines the renewable energy power generation prediction with a power grid electricity price signal to dynamically adjust the charging and discharging strategy of an energy storage unit. By predicting the fluctuation of the load in the service area and the power generation condition of the renewable energy source, the optimal energy storage scheduling scheme can be automatically generated in different time periods, and the method is particularly suitable for scenes with large traffic flow fluctuation and complex power demand in the service area. According to the method, the dependence of the service area on the external power grid is effectively reduced, and the stability and economic benefits of system operation are improved.

The invention relates to the technical field of chip heat dissipation, in particular to a computer chip liquid cooling heat dissipation system and method. The heat dissipation control module is used for outputting a control instruction according to the temperature difference value, constructing a chip temperature prediction model, obtaining a chip temperature prediction value, adjusting a judgment result of a heat dissipation mode according to the chip temperature prediction value, and correcting a safe working temperature value according to the use duration of the chip; and the execution feedback module is used for controlling the cooling liquid adjusting device according to the control quantity, outputting alarm information according to the cooling liquid flow, constructing a preset flow dynamic adjusting model and obtaining preset flow automatic adjusting information according to the real-time environment temperature data and the real-time computer load data. According to the invention, the potential overheating risk can be found in time, and the stability and reliability of the system are improved.

The invention relates to the technical field of sewage discharge treatment, and discloses a sewage low-carbon treatment system based on electrochemistry and biochar, which comprises a deep treatment filter tank, an electrochemical nitrogen and phosphorus removal device, a biochar adsorption and filtration module, an intelligent control and operation and maintenance system, a slow-release agent regulation and control module and a low-carbon and resource recycling system, the advanced treatment filter tank is used for filtering sewage impurities and particles, adsorbing and removing organic matters, reducing the content of nitrogen and phosphorus nutrient substances and adapting to the change of water quality and water quantity; the electrochemical nitrogen and phosphorus removal device is used for carrying out electrochemical reaction to remove nitrate, phosphate and fluorine ions. According to the invention, the electrochemical active reaction is combined with charcoal adsorption filtration, so that the technology greatly improves the sewage treatment efficiency, especially shows a higher removal rate when treating complex pollutants, can meet higher water quality standard requirements, and effectively overcomes the difficulty that the traditional treatment technology is difficult to remove multi-element pollutants.

The invention discloses a large model parallel task scheduling method based on space-time two-dimensional segmentation and intelligent sharing, and particularly provides a model segmentation strategy based on space-time two dimensions, and based on significant differences of training and reasoning tasks in calculation properties, targeted optimization strategies are adopted respectively, so that the task execution efficiency is improved; the invention further provides a model placement strategy based on the heterogeneous resources and the interference model, the defects that a traditional method lacks intelligent sharing judgment and cannot be dynamically adjusted are overcome, the flexibility and adaptability of scheduling are improved, and therefore the maximum benefit of the shared resources is guaranteed. The method solves the problems that when an existing heterogeneous intelligent computing cluster scheduling method is used for processing large model training and reasoning tasks, the model placement optimization technology of model segmentation and heterogeneous resource adaptation is not fully utilized, intelligent sharing scheduling of computing resources in a cluster is lacked, system task scheduling is unreasonable, and the scheduling efficiency is high. And the utilization rate of computing resources is low.

The invention provides a micro-grid robust optimization scheduling method considering a segmented multi-interval uncertainty set, and the method comprises the steps: carrying out the segmentation fitting of the source load prediction power and error data of wind, light and loads based on a Gaussian mixture model, and combining with a box-type interval uncertainty set to build a multi-interval uncertainty set of power segmentation segments; the uncertainties are used for describing wind, light and load uncertainties; constructing a microgrid robust optimization scheduling model considering the segmented multi-interval uncertainty set and the carbon emission by taking the minimization of the operating cost and the carbon emission cost of the microgrid as a target; and solving the micro-grid robust optimization scheduling model by adopting a column sum constraint generation algorithm. According to the method, the problems of uncertainty modeling and carbon emission of the micro-grid can be solved, the conservative property of a robust optimization scheduling strategy is reduced, and the economical efficiency of micro-grid scheduling is improved.

The invention discloses an electric power capacity market clearing method and device considering flexibility. The method comprises the following steps: constructing an electric power capacity market clearing model considering flexible adjustment requirements of a system; performing dual change and linearization processing on the clearing model, and solving the model; carrying out capacity market pricing and settlement based on a solving result; wherein the step of constructing the power capacity market clearing model considering the flexible adjustment demand of the system comprises the following steps: determining a capacity demand curve which can adopt a variable resource demand curve; constructing a target function with maximization of social welfare as a target; determining capacity market clearing constraint conditions considering flexibility; according to the invention, the capacity value of the new energy can be priced and metered, and the new energy operation cost is reduced.

The invention relates to the technical field of photovoltaic energy storage cabinets, in particular to a safety type photovoltaic energy storage cabinet. Comprising an outer shell, a plurality of inner shells are fixedly connected in the outer shell, battery packs are detachably and fixedly connected in the inner shells, cooling fans which are symmetrically distributed are arranged outside the outer shell, each inner shell is provided with two cooling row holes which are symmetrically distributed, and the inner shells are slidably connected with two plugging frames which are symmetrically distributed. The two plugging frames on the inner shell are jointly provided with a connecting frame, the inner shell is fixedly connected with a sliding rail, an electric sliding block is slidably connected into the sliding rail, and the electric sliding block of the sliding rail is fixedly connected with the adjacent connecting frame. According to the invention, the temperatures of the plurality of battery packs during charging are respectively detected, and heat dissipation of different degrees is carried out according to different temperatures of the plurality of battery packs, so that the plurality of battery packs are at proper temperatures, cooling resources are allocated as required, energy is saved, and operation cost is reduced.

The invention discloses an intelligent centralized control chain distributed multi-energy optimization scheduling device and method, and relates to the technical field of energy management. The method comprises the steps that energy samples are collected, energy characteristic indexes are established, and energy grading is conducted according to the indexes; historical power load data and load influence factor data are collected, and a power load prediction sequence is obtained by using the multi-time scale power load prediction model; collecting power supply and power grid data of the power system, calculating the power supply capability, and obtaining the power supply and demand balance degree according to the power load prediction sequence; and finally, forming a multi-energy optimization scheduling strategy according to the supply and demand balance degree and the energy classification. The device comprises an energy grading module, a load prediction module, a power supply and demand balance degree calculation module, an optimization scheduling module and a real-time monitoring and dynamic adjustment module, and is used for realizing the method. Through grading and optimal scheduling of energy, the energy utilization efficiency is improved, the operation cost is reduced, and the environmental pollution is reduced.

The invention discloses a hydrogen-containing energy storage micro-grid dispatching optimization method in combination with ARIMA-LSTM and DDPG. Comprising the following steps: firstly, acquiring historical renewable energy productivity data, historical power load data, historical electricity price and historical energy storage equipment state of the hydrogen-containing energy storage microgrid; secondly, historical renewable energy productivity and power load data are predicted through an ARIMA-LSTM model, a complete state space is constructed in combination with the renewable energy productivity, the electricity price, the energy storage state and the like of the current time slot and input into an Actor and Critic network of the DDPG, a DDPG network model is trained, and a reward function model in the DDPG network model aims at optimizing the annual operation cost; the constraints comprise equipment operation constraints, renewable energy source output constraints, power imbalance and the like; and finally, a micro-grid scheduling strategy responding to external environment change is obtained, dynamic scheduling optimization of each device is realized, and adaptability and robustness of the system are improved.

The invention belongs to the technical field of waste resource recycling, and particularly relates to a waste resource recycling plastic product manufacturing and forming device which comprises a base, an injection mechanism used for plastic plasticizing and moving is arranged at the top of the base, and a first feeding hopper is arranged at the top of the injection mechanism; a mold closing mechanism connected with a discharging port of the injection mechanism is arranged at the top of the base and used for conducting pressing molding on molten plastic, a treatment box is arranged at the position, located on one side of the injection mechanism, of the top of the base, and a smashing mechanism used for smashing waste resources is arranged in the treatment box; the two driving crushing cutters are driven by the first motor to synchronously and relatively rotate, and then are matched with the fixed crushing cutter to crush waste plastics; according to the waste plastic crushing and cleaning device, multiple functions of crushing, screening, cleaning, drying and the like can be ingeniously integrated, the crushing effect and efficiency of waste plastic are improved, and the powder cleaning effect and the powder quality are also improved.

The invention provides a bidding method for virtual power plants participating in a joint market based on a master-slave non-cooperative game. The bidding method comprises the following steps: constructing a double-layer game framework in which multiple virtual power plants participate in a day-ahead joint market; carrying out Wasserstein distance uncertainty modeling on the wind power, the photovoltaic power and the electric load power; based on the multi-source load power uncertainty set, an upper-layer multi-virtual power plant two-stage distribution robust bidding model is constructed, and the bidding model comprises a first objective function with the minimum sum of pre-scheduling cost for making a bidding strategy in a day-ahead stage and rescheduling cost expectation in an intra-day stage and a corresponding first class of constraint conditions; constructing a lower-layer joint market clearing model; the clearing model comprises a second objective function with the minimum day-ahead electricity-peak regulation-hydrogen combined market energy purchase cost and a corresponding second class of constraint conditions; performing equivalent conversion on the bidding model and the clearing model; and solving the converted bidding model and the converted clearing model to obtain a bidding strategy.

The present invention discloses a filler washing tower and a washing method, the filler washing tower is used for washing treatment of VOCs waste gas, and the filler washing tower comprises: a washing liquid pipe which is arranged at the axis position of the washing tower in a penetrating manner, vertically extends and is used for receiving a washing liquid from the outside of the tower; the washing liquid pipe is provided with a rotatable pipe section at the corresponding position of the filler layer; the spraying pipe assembly is composed of a plurality of spraying pipes with different lengths and is communicated with the rotatable pipe section, and a spraying opening of each spraying pipe is formed in the tail end of the pipe; the spraying pipes are arranged at intervals in the circumferential direction of the rotatable pipe section and are tangent to the rotatable pipe section; the spraying pipes with different lengths drive the rotatable pipe section to rotate through counter-acting force when spraying out the washing liquid, and annular partition covering of the washing liquid on the packing layer is formed. Rotation of the spraying pipe is achieved through spraying of washing liquid, and through annular partition covering of spraying, water saving can be achieved while the gas-liquid mass transfer efficiency is guaranteed, and external power consumption is not needed.

The invention belongs to the technical field of intelligent control of air conditioning systems, and discloses a split type air conditioner load management system and method. The system comprises a central controller, a health management module, an adaptive integrated control module AICA and an intelligent scene recognition module. The central controller integrates various sensor information such as temperature and humidity, air quality and user physiological data, data processing and intelligent decision making are carried out through an AICA module, and dynamic optimization management of air conditioner loads is achieved. The health management module automatically adjusts the air conditioner operation mode according to the body temperature, heart rate and other physiological indexes of the user, and a comfortable environment is provided. And the intelligent scene recognition module is used for pre-judging user requirements and adjusting the operation of the air conditioner by learning a user behavior mode. The energy efficiency and the intelligent level of air conditioner operation are improved, the method is suitable for air conditioner load management of various buildings, energy can be effectively saved, and the comfort level of users is improved.

The invention discloses a measuring system and a calculating method for the friction coefficient of a plunger pump flow distribution pair, and relates to the technical field of fluid machinery and intelligent measurement and control. The system comprises the following components: a multi-dimensional data acquisition module, a data processing and preprocessing module, an intelligent calculation model construction module, a friction coefficient prediction and optimization module and a visualization and report generation module. According to the invention, through the multi-dimensional data acquisition module and the intelligent calculation model construction module, accurate measurement and prediction of the friction coefficient of the plunger pump flow distribution pair are realized, the nonlinear relationship among the parameters is fully considered during design of the intelligent calculation model, and training is carried out through a large amount of experimental data, so that the accuracy and the accuracy of the friction coefficient of the plunger pump flow distribution pair are improved. According to the method, the complex influence mechanism of each parameter on the friction coefficient can be automatically learned and identified, the prediction precision of the friction coefficient is improved, and the generalization ability of the model is remarkably enhanced through wide-coverage operation conditions and design variants, so that the prediction result is more reliable and stable.

The invention belongs to the technical field of energy storage power stations, and particularly relates to an energy storage power station electric quantity optimization method based on graph calculation, which comprises the following steps: S1, predicting future power generation capacity and load demand of an energy storage power station based on graph calculation: acquiring historical power generation data and load data of a distributed power station in a grid-connected area of the energy storage power station, and external influence data, constructing a time-space diagram neural network model, and obtaining prediction results of the power generation data and the load data in each hour period of the next 24 hours; s2, calculating the power and load value of each node in the power grid through power flow analysis; and according to the predicted future power generation capacity and the load demand, the electric quantity needing to be provided or consumed by the energy storage power station is calculated. Optimization of energy of the power storage station is achieved, and the cost of the power storage station is reduced.

The invention relates to an optical storage direct flexible park adjustable load orderly energy using method and device and computer equipment. The method comprises the following steps: constructing a micro-grid architecture model of the optical storage direct-current flexible park according to equipment power utilization characteristics and power resource scheduling information of the optical storage direct-current flexible park; constructing an equipment operation model corresponding to each piece of power equipment according to the equipment operation characteristics of each piece of power equipment in the optical storage direct flexible park; according to the micro-grid architecture model and each equipment operation model, constructing an adjustable load ordered energy use objective function of the optical storage direct flexible park and park equipment operation constraint conditions; the park equipment operation constraint condition is used as a limit, an adjustable load ordered energy use target function is solved, and adjustable load ordered energy use adjustment information of the optical storage direct flexible park is obtained; the adjustable load orderly energy utilization adjustment information is used for adjusting the power resource distribution condition of the optical storage direct flexible park. By adopting the method, the satisfaction degree of the user on the regulation and control means of the adjustable load can be improved.

The invention relates to the technical field of energy storage systems, in particular to an energy storage system charging and discharging strategy optimization method based on big data analysis. The technical problems are that the energy utilization rate and the charging and discharging efficiency of the energy storage system are low due to the fact that a traditional charging and discharging strategy of the energy storage system is difficult to adapt to a dynamically changing power grid environment and user requirements, and the normal operation and the service life of the system are affected due to the fact that key components of the energy storage system are prone to faults due to lack of effective fault prediction and monitoring means. According to the technical scheme, the energy storage system charging and discharging strategy optimization method based on big data analysis comprises the following steps: establishing a time sequence prediction model and a particle swarm optimization model based on a data analysis result; according to the method, the future state and demand of the energy storage system can be accurately predicted through big data analysis and a machine learning algorithm, so that the optimal charging and discharging strategy is formulated; the energy utilization rate of the energy storage system is improved, the charging and discharging efficiency is remarkably improved, and the operation cost is reduced.

The invention discloses a building integrated energy system optimization scheduling method giving consideration to the comfort level of a light storage soft environment, and relates to the field of building energy system optimization scheduling, and the method comprises the steps: building a photovoltaic output uncertainty model based on Beta distribution according to typical daily solar radiation intensity data; secondly, constructing an energy storage model; thirdly, a comfort level quantitative model considering temperature regulation and control of the indoor air conditioner and the electric water heater is constructed, and the influence on the environmental comfort level is reflected laterally; and finally, according to the various formed models, constructing a building integrated energy system optimization scheduling model considering the light storage soft comfort degree, and performing solving based on a mixed integer linear programming method. The environment comfort level of the building integrated energy system can be quantified, the flexibility of the building integrated energy system is improved by introducing photovoltaic and energy storage equipment, flexible scheduling of load resources is facilitated, peak load shifting is better achieved, the operation cost of the building integrated energy system is reduced, and the operation efficiency of the building integrated energy system is improved.

The invention relates to a high-precision variable air volume valve and an air volume control method thereof. The variable air volume valve is composed of a valve body, a front valve block set, a rear valve block, two actuators and a controller, linear adjustment of the air volume can be achieved, the air volume adjustment precision is improved, and adjustment of high and low air volume intervals is supported. The controller collects pressure difference data of an inlet and an outlet through the pressure difference sensor, and the real-time air volume is calculated through a built-in Kolmogorov-Arnold (KAN) neural network model. By comparing the actual air volume with the target air volume in real time, the controller adjusts the actuator to change the opening degree of the front valve plate set and the opening degree of the rear valve plate set, and accurate air volume control is achieved. According to the technical scheme, the accuracy and the adjusting range of air volume measurement are improved, additional resistance in the air pipe is reduced, and the performance and the operation efficiency of an air conditioning system are remarkably optimized.

The invention discloses a treatment device and process based on zero discharge of high-concentration coating wastewater, wastewater passes through an adjusting tank and then enters a pretreatment module through a pipeline, the coating wastewater passes through a Fenton tank body, a coagulation tank body and a primary sedimentation tank of the pretreatment module in sequence through the pipeline to be treated, and the coating wastewater is continuously conveyed to a biochemical treatment module; the biochemical treatment module is used for carrying out anaerobic environment treatment through an anaerobic granular sludge bulking bed and then carrying out aerobic environment treatment through an aerobic aeration tank; and introducing the wastewater into a secondary sedimentation tank to separate the formed sludge from water, entering a biochemical tank, carrying out ultrafiltration and reverse osmosis treatment through a deep treatment module, recycling the treated wastewater to a production line, and recycling the generated condensate water and RO fresh water. The adopted wastewater treatment device is combined and optimized by various advanced technologies, so that the treatment efficiency and the water quality are improved, efficient recovery and reutilization of resources are also realized, and the system has good economic benefits and environmental benefits and is suitable for the requirements of modern coating wastewater treatment.