354 results about "Cooling load" patented technology

The invention relates to the technical field of air conditioner cooling load prediction, and discloses an air conditioner cooling load prediction method and system. The method comprises the following steps: acquiring temperature, humidity and CO2 data through an environment sensor network and carrying out standardization processing; establishing a time slice matrix based on the environmental parameters and the personnel density, and obtaining user behavior characteristics; constructing a wall heat storage model by combining the environmental parameters, and calculating instantaneous heat flow; establishing a spatial downscaling model according to the heat flow and meteorological data; time sequence features are extracted based on local weather and heat flow data, and the cooling load is predicted; and a time-phased refrigeration control instruction is generated accordingly. By establishing a data processing and analysis process, factors such as building microclimate characteristics, user behavior modes and wall dynamic thermal characteristics are comprehensively considered, and more accurate prediction of the cooling load of the air conditioner is realized.

According to the non-intrusive identification method suitable for the operation load of the central air conditioner, the central air conditioners of different brands are selected, electrical waveforms of the complete operation process of the central air conditioners are recorded, time-frequency domain characteristic analysis is carried out, common characteristics are extracted, and total daily electrical load data and daily air temperature data of a user in the past one year are selected; analyzing the incidence relation between the daily electrical load curve and the daily temperature curve of the user, obtaining a heating load area, a basic load area and a cooling load area, performing linear data fitting with the temperature to obtain a load curve, and subtracting the basic load curve from the cooling load curve to obtain a load curve; further judging whether the central air conditioner is in a starting / stopping state or not, and obtaining the electric quantity of the central air conditioner equipment in the operation period through steady-state power integration by adopting sub-item electric quantity estimation in the starting event and stopping event period of the user air conditioner; and running state tracking and electricity consumption estimation of the central air conditioner are realized.

The invention belongs to the technical field of intelligent building energy management, and particularly discloses and provides a dual-mode cold storage air conditioning system based on human flow dynamic cooperative regulation, which comprises the following steps: acquiring dynamic human flow thermodynamic diagram data in real time, and accurately calculating a multi-region linkage cold load demand value based on a density change correlation coefficient and a heat conduction model; the real-time sensing of the flow density fluctuation and the dynamic refrigeration demand matching are realized; the phase change state of the coolant of the cold storage tank and the power consumption data of the refrigerating unit are collected in real time, the cold storage release proportion, the refrigerating power increment and other key parameters are dynamically adjusted, and the energy utilization rate is increased; the inherent defect of non-uniform cold and heat in zone control is overcome by calculating inter-zone cooling capacity migration parameters; temperature feedback data and a people flow thermodynamic diagram are collected in real time, the proportionality coefficient of the fan rotating speed and the air valve opening degree is dynamically adjusted, a closed-loop learning mechanism is formed, and the matching precision of a temperature control strategy and people flow distribution is continuously optimized.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses a second-level regulation and control method and system for a refrigeration system of a data center, and the method comprises the steps: firstly carrying out the validity verification, type recognition and scale evaluation of a data request, outputting a request type and a task scale, distributing a hardware number, predicting the pre-refrigeration capacity through a pre-refrigeration model, and regulating and controlling the equipment combination of the refrigeration system to complete the pre-refrigeration; a load-energy consumption prediction model is utilized to predict a load curve of each component of a server and convert the load curve into a thermal power consumption curve, a cold load conversion model is utilized to predict a cold load of a data center, a real-time cold quantity difference value is combined to dynamically supplement and adjust a refrigeration system, and multi-stage verification and correction are performed according to temperature, cold quantity and energy efficiency data of a measuring point to generate an optimal control strategy. Finally, the optimal strategy is fed back to the pre-refrigeration model, the load-energy consumption prediction model and the cold load conversion model, and iterative optimization of the models is achieved. The response speed, the cooling capacity distribution precision and the overall energy efficiency of the refrigeration system are improved through task perception preposed pre-cooling, component-level precise regulation and control and closed-loop feedback optimization.

The invention relates to the technical field of central air conditioners, and discloses a central air conditioner water system energy-saving and consumption-reducing method which comprises the following steps: S1, acquiring operation data of a central air conditioner water system, and preprocessing the operation data; and S2, a cold load prediction model is established based on the operation data, and the model combines wavelet threshold denoising, a convolutional neural network and a long and short term memory network to accurately predict the future cold load demand. According to the energy-saving and consumption-reducing method for the water system of the central air conditioner, through the synergistic effect of a cooling load prediction model and a parameter optimization strategy, the system can dynamically match real-time load requirements, the model is combined with a convolutional neural network to extract local features and capture time correlation of a long-short-term memory network, and high-precision prediction of cooling loads in the next two hours is achieved; and compared with a traditional fixed parameter operation mode, invalid energy consumption of equipment such as a water pump and a fan is reduced, and energy conservation is achieved.

The invention discloses an air conditioner resource allocation method and system based on a cold accumulation prediction algorithm, and relates to the field of air conditioner cold accumulation, and the method comprises the steps: collecting historical load operation data of an air conditioner, analyzing the historical load operation data through employing a grey relational analysis algorithm, and constructing a cold load prediction model in combination with the cold accumulation prediction algorithm; training the cold load prediction model by using a back propagation neural network algorithm, inputting load operation data collected in real time into the trained cold load prediction model, and outputting a cold load prediction result; and in combination with the cooling load prediction result and the electricity price information, an air conditioner resource allocation strategy is formulated, and a model prediction control algorithm is used for optimizing the allocation strategy. Through integration of the load prediction technology and the real-time electricity price information, the cold load demand of the whole day can be accurately predicted, energy waste is reduced, the energy utilization efficiency is improved, the dynamic electricity price market can be responded, and economic maximization is achieved.

The invention relates to a central air conditioner energy consumption optimization control method and system, electronic equipment and a storage medium, and the method comprises the steps that characteristic parameters of load calculation are obtained, and according to the characteristic parameters, the cooling load requirements of a central air conditioner under different working conditions are calculated through a dynamic load calculation model; acquiring real-time operation data of the central air conditioner; establishing a digital simulation model of the central air conditioner based on the cooling load demand and the operation data, and calculating the energy consumption distribution of each device of the central air conditioner under different working conditions through the digital simulation model; with the overall energy efficiency optimization of the central air conditioner as the target, the optimal operation parameter combination of the central air conditioner is obtained through an intelligent optimization algorithm; and synchronously displaying the operation data of the central air conditioner, the energy consumption distribution of each device and the optimal operation parameter combination through a visual interface, and executing an optimized operation strategy according to a user instruction based on the optimal operation parameter combination. According to the method, the accuracy of energy consumption calculation is improved, and the overall energy consumption of the central air conditioner is reduced.

The invention is suitable for the technical field of intelligent control, and discloses a water cooling control method, system and device and a medium. Based on the preprocessed data, constructing a thermal load trend prediction model based on LSTM to obtain a cold load prediction value; based on the cold load prediction value, a PID adjusting signal is generated in combination with the temperature error value; based on the PID adjusting signal, a water pump target frequency instruction is obtained in combination with the cooling load predicted value; based on the water pump target frequency instruction, a valve target opening instruction is obtained in combination with the actually-measured water flow; based on the valve target opening instruction, the fault alarm and emergency instruction is generated, the preset emergency measures are executed, the PID control parameters are updated, the performance evaluation report and the feedback information are obtained, and the response speed, the operation stability and the autonomous decision-making capacity of the water cooling system under the dynamic load and abnormal working conditions are improved.

A composite radiative cooling material includes a first layer including a reflective material, a second layer including a porous material, and a third layer including an emissive material. The optical properties of the respective materials and the arrangement of the respective layers cause the radiative cooling material to exhibit a total solar reflectance greater than 85%, and a thermal emissivity greater than 85% in a wavelength range of 8 to 13 μm. The layers may be in a vertically stacked arrangement, with the third layer capable of directly facing the sky when the composite radiative cooling material is installed for cooling a load, the second layer arranged under the third layer, and the first layer arranged under the second layer. The composite radiative cooling material may be thermally coupled to a cooling load to provide radiative cooling to the cooling load.

The invention provides a power transformer electromagnetic loss control method and device in an intelligent power grid environment, and relates to the field of intelligent power grids, and the method comprises the steps: carrying out the cooling demand prediction of a transformer according to a predicted power load sequence and a space temperature sequence, and outputting a predicted cooling load sequence; and with meeting of the predicted cooling load sequence as a constraint and minimization of energy consumption and oil temperature fluctuation as optimization targets, cooling parameters of the forced oil cooling device are optimized, an optimal cooling parameter sequence is output, and the forced oil cooling device is controlled to dissipate heat of the transformer. The method aims to solve the technical problems that a traditional control method lacks cooling strategy dynamic adjustment capacity, temperature rise abnormity is prone to occurring in a high-load or high-temperature environment, transformer iron core loss and winding loss are remarkably increased, and electromagnetic loss is high, accurate and efficient cooling control can be achieved, the electromagnetic loss is effectively reduced, and the service life of a transformer is prolonged. And the operation efficiency and stability of the transformer are improved.

The invention discloses a wind, light, electricity, heat, cold air and hydrogen optimal scheduling method for an off-grid zero-carbon park, and the method comprises the steps: firstly, building an off-grid zero-carbon park system model, the system comprises energy supply equipment, energy conversion equipment and energy storage equipment, the energy source is renewable energy wind energy and light energy, and the load types comprise an electric load, a heat load and a cold load; establishing a mathematical model and operation constraint conditions according to the system model, and establishing an optimization objective function for the system; describing an optimized scheduling reinforcement learning framework of the off-network zero-carbon park system as a Markov decision process; on the basis of an improved flexible actor-commentator algorithm, advantage learning, transfer learning and an SAC algorithm are combined to solve the model. The advantages of multi-energy complementation are fully utilized, and the economical efficiency and the environmental protection property of the system are improved; and an improved flexible actor-commentator algorithm is adopted, so that the training time is shortened, the algorithm iteration speed is accelerated, and the flexible and efficient scheduling of the integrated energy system is realized.

The invention discloses a new energy building integration energy-saving design analysis method, and relates to the technical field of new energy building integration, and the method comprises the following steps: carrying out the time sequence alignment and unit normalization processing of collected heat flow data, temperature rise data and power output data; according to the heat conduction characteristic of the heat conduction structure and the thermal inertia parameters of the building enclosure structure, a heat flux track and a thermal response profile with a unified thermal resistance reference axis are constructed; and based on the heat flux track and the thermal response profile, extracting a reverse heat flux strength parameter and a heat flux continuous influence duration parameter, and combining the parameters with a temperature control target and a heat conduction structure attribute set in the building to construct a reverse heat conduction interference atlas for judging the heat conduction interference degree of the cold load in the building. The problem of cold load misjudgment caused by the fact that photovoltaic heat is transmitted through a high-heat-conduction structure is solved, and cold and heat conduction accurate recognition and energy consumption dynamic regulation and control are achieved.

The invention discloses a hospital public building temperature refined energy-saving regulation and control method, which comprises the following steps: establishing a refined hierarchical management architecture based on use requirements and energy use purposes of different wards; for a conventional inpatient ward, door and window opening and closing state data and ward heat state data are collected, and air conditioner operation control and door and window opening and closing state adjustment are executed based on a window magnetic control air conditioner operation model and by adopting a multi-step prediction control method; for different types of special wards, regulating and optimizing corresponding thermal equipment by adopting a multi-step prediction control method under an MPC model prediction control framework according to thermal parameters required to be controlled by the current type of ward; for outpatient wards, outpatient air conditioner output operation is optimized by adopting a multi-step prediction control method based on a cooling load prediction model according to the flow density, the temperature and the indoor and outdoor temperature difference; according to the invention, monitoring and accurate prediction of the thermal condition in the ward are realized based on the model prediction control MPC method in combination with the window magnetic switch.

The invention discloses a self-adaptive regulation and control method for an air conditioning system based on information matching. The self-adaptive regulation and control method mainly comprises the following steps that firstly, a data set used for building cold load prediction is divided into a low-completeness data set and a high-completeness data set through a proposed information completeness judgment method; and for the high-completeness data set, regulating and controlling the air conditioning system by adopting a model prediction control method according to the load prediction model and the air conditioning equipment model. And for a low-completeness data set, a proposed migration imitation learning regulation and control method based on deep reinforcement learning is adopted. According to the method, the influence of data quality on the model prediction precision and the regulation and control effect is considered, the influence of fluctuation of historical data quality on the regulation and control effect can be overcome, the relative advantages of the model prediction control method and the reinforcement learning method are integrated, compared with a single regulation and control method, the energy-saving and cost-saving effects can be improved, and good robustness is achieved.

The invention discloses an efficient refrigerating machine room performance optimization method and device, and relates to the technical field of intelligent energy-saving optimizing.The efficient refrigerating machine room performance optimization method comprises the steps that operation data of refrigerating equipment are collected through various sensors and preprocessed; based on the preprocessed operation data, constructing an MT-LSTM model to predict a future cold load demand, and generating a cold load demand prediction value; on the basis of the cold load demand predicted value, generating an optimal scheduling strategy through an NSGA-II optimization algorithm; and according to the optimal scheduling strategy, equipment starting and closing and load proportion distribution are executed through IoT, operation data are monitored in real time, and the operation state of the refrigeration equipment is dynamically adjusted and optimized. According to the method, the future cold load demand is predicted by constructing the MT-LSTM model, and accurate demand prediction is realized.

The invention relates to a data center equipment type selection method based on electrical and heating ventilation load linkage, and belongs to the technical field of data center infrastructure design. The model selection method comprises the steps of setting basic data, obtaining the number of machine rooms and the number of IT devices in a single machine room, obtaining the total power of the IT devices, setting an energy efficiency index set value, obtaining the project power consumption scale, obtaining the total cooling load, selecting models of air conditioners at the tail ends of the machine rooms and substations, selecting models of water chilling units, selecting models of heat exchangers, selecting models of cooling water pumps and chilled water pumps and selecting models of cooling towers. And obtaining the total power of the air conditioner, obtaining an energy efficiency index accounting value, and carrying out PUE verification. According to the method, through closed-loop iteration of electrical load-heating and ventilation load-PUE, model selection and quantity selection of all key equipment are completed at a time; professional splitting, repeated adjustment and modification and capacity redundancy caused by traditional serial design are avoided, the design period is remarkably shortened, and initial investment is reduced.

A method for controlling HVAC equipment includes using a dynamic thermal model that describes heat transfer characteristics of a building to predict a temperature of the building as a function of control inputs to HVAC equipment that operate to affect the temperature of the building. The method includes obtaining time-varying utility prices and a predicted heating or cooling load for the building and performing a predictive control process using the dynamic thermal model, the time-varying utility prices, and the predicted heating or cooling load to generate the control inputs to the HVAC equipment subject to constraints on the temperature of the building.

The invention relates to the technical field of electrical performance testing, in particular to a method and system for measuring loss of an inductance winding, and the method comprises the following steps: applying a direct current to a measured inductance winding, synchronously monitoring the temperature, and multiplying a pre-measured direct current resistance value by a direct current value to obtain a direct current loss power value. The direct current is applied to the inductance winding, the temperature is monitored synchronously, the direct-current loss power is obtained by combining the direct-current resistance value and the current value, the temperature curve in the initial stage is extracted, the temperature rise rate is calculated, and therefore the winding heat capacity parameter is obtained, loss measurement can be closely associated with thermal response, and the measurement accuracy is improved. Introduction of a heat capacity factor is added in loss calculation, dynamic characteristics of a winding material on a thermodynamic level can be reflected, instantaneous current with load step change is applied after an inductor is cooled, an instantaneous loss power curve is generated by collecting a voltage sequence and a current sequence at the same time, and loss calculation is not limited to average value or steady state measurement any more.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention relates to the technical field of heat energy storage and heating, and provides a refrigeration and heating integrated system based on phase change heat storage and a control method, the refrigeration and heating integrated system comprises a solar heat collection module, a heat storage oil tank, a phase change heat storage module, an absorption refrigeration module, a heating and heat supplementing module and a multi-mode control module; the multi-mode control module is used for controlling the phase change heat storage module to store heat in a heating period in response to the valley electricity time; monitoring the temperature of a heat storage oil tank in response to the non-valley electricity time, switching a heat supply mode of the system based on a comparison result of the temperature of the heat storage oil tank and a set threshold value, and controlling the phase change heat storage module to store heat or driving the absorption type refrigeration module to refrigerate in response to triggering of a user cold load demand in a cold supply period; and in response to the situation that the user cold load demand is not triggered, the heating and heat supplementing module is controlled to supplement heat to the phase change heat storage module. By integrating solar heat collection, phase change heat storage and valley electricity heat compensation strategies, efficient utilization and multi-source cooperative energy supply of clean energy are achieved.

The invention discloses an energy-saving self-control and intelligent monitoring system for a central air conditioner, relates to the technical field of energy-saving air conditioners, and aims to solve the technical problem of high energy consumption caused by incapability of precise regulation and control in the prior art. Comprising a touch screen, a display unit and a remote mobile terminal interaction interface. The data acquisition module is used for acquiring indoor and outdoor parameter information in real time, including indoor and outdoor temperature, humidity, air volume, air outlet temperature, pressure and time information; the data processing module is used for calculating the cold load capacity of each area according to the indoor and outdoor parameter information, generating the change rate of the air outlet temperature difference based on the cold load capacity and the air outlet temperature difference, and obtaining a cold load capacity change signal according to the change rate of the air outlet temperature difference of each area; and a data storage module. The method has the advantages that the air outlet cooling load capacity of each area is dynamically adjusted, energy waste is avoided, and the energy-saving effect is achieved.

The embodiment of the invention relates to the technical field of refrigeration control, and discloses a self-adaptive load refrigerating unit energy-saving operation method which comprises the following steps: acquiring a to-be-refrigerated space area, and determining an initial division area of the to-be-refrigerated space area; controlling and generating initial control parameters of each refrigerating unit according to the regional refrigeration demand information so as to perform refrigeration operation on the initially divided regions; matching the activity intensity information of each initial division area with a set condition, and if not, generating refrigeration adjustment information according to a matching result of the activity intensity information of the corresponding initial division area and the set condition; and generating equipment control parameters of each refrigerating unit according to the refrigerating adjustment information and the regional refrigerating requirements so as to perform refrigerating adjustment on the refrigerating units. The refrigerating unit provided by the embodiment of the invention can respond to the cold load change caused by factors such as personnel activities in the space in real time. The operating parameters of the refrigerating unit can be accurately adjusted in real time according to the load change, so that the refrigerating system operates more stably.

The invention discloses a data center dynamic air-conditioning load rapid calculation method, which comprises the following steps of: 1, summarizing data center cold load influence factors as an input condition of a calculation model; 2, constructing a standardized data center reference model based on building energy consumption simulation software; 3, based on the building geometrical characteristics and thermal performance of the target data center, correcting the enclosing structure load of the reference model by adopting an area correction method, and constructing an enclosing structure load rapid calculation model; 4, automatically resetting an internal heat source load calculation result, and constructing an internal heat source load rapid calculation model; 5, correcting the fresh air load of the reference model by adopting a volume correction method based on the geometric characteristics of the target data center building, and constructing a fresh air load rapid calculation model; and step 6, forming a target data center dynamic air-conditioning load rapid calculation method, and obtaining a hourly cooling load prediction result required for covering the whole year or a design stage.

The invention provides an intelligent air conditioner resource energy-saving control method and system, and the method comprises the steps: carrying out the region division of a performance place based on functionality, and obtaining different functional regions which at least comprise an audience core region and a corridor channel region; different operation stages of the corresponding performance place are obtained, and at least comprise a pre-cooling stage, a warm field stage, a peak stage and a scattering stage; on the basis of the obtained multi-terminal data of the corresponding performance place, predicting the regional predicted cooling load of each functional region in each operation stage, and comparing the obtained regional predicted cooling load with the regional actual cooling load corresponding to the same functional region and the operation stage in real time; and responding to a cooling load difference value between the actual cooling load of the region at any moment and the predicted cooling load of the region, and executing dynamic energy-saving temperature control of the corresponding operation stage on the functional region. According to the invention, the resource energy-saving efficiency is at least improved.

The invention relates to the technical field of cooling system control, in particular to a ship central cooling water system frequency conversion control simulation analysis system, which comprises a dynamic load prediction module for predicting cooling load requirements of ship main engine and auxiliary engine equipment in different time periods in the future in real time; the load grading control module is used for dividing loads into a plurality of grades, including light load grades, medium load grades and heavy load grades, and generating multi-layer frequency conversion control strategies for cooling water requirements under different load grades; the amplitude control module optimizes the variation amplitude of the rotating speed of the water pump; the load peak value regulation and control module is used for carrying out short-time overclocking control on the water pump when the load fluctuates violently within a short time; and the load distribution simulation module is used for simulating behaviors and responses of the cooling system under different load conditions by performing simulation analysis on distribution of loads of all cooling loops in the ship cooling water system. According to the invention, it is ensured that the equipment obtains sufficient cooling support under a high-load condition, and the load adaptive capacity and the operation efficiency of the cooling system are effectively improved.

The invention relates to the technical field of ground source heat pump control, in particular to an intelligent cooperative control system for a ground source heat pump system and building energy management. The method comprises the steps that a data collecting and preprocessing unit collects multiple types of data in real time and preprocesses the data to form system state vectors; the load and power generation prediction unit predicts future cooling and heating load and photovoltaic power generation power of the building through a machine learning regression model and a time sequence prediction model; the operation optimization unit generates an optimal operation strategy of the ground source heat pump based on a depth deterministic strategy gradient algorithm; and the multi-source energy coordinated control unit realizes multi-device coordinated control through multi-target mixed integer linear programming. Through combination of prediction data and an optimization algorithm, a dynamic matching relation between load fluctuation and energy supply is fully considered, and the problems of soil heat imbalance and unstable comprehensive energy efficiency ratio of the ground source heat pump system in a traditional fixed parameter operation mode are solved.

The invention discloses an industrial circulating water evaporation water consumption water-saving control method based on artificial intelligence. According to the method, an efficient air cooling device is additionally arranged on the basis of an existing open type cooling tower circulating water system, and a dry-wet combined cooling system is formed through integration of pipelines and valves. The system collects the temperature, flow, pressure, energy consumption and environmental parameters of each node in real time, and after data fusion and preprocessing, the cooling load and evaporation water consumption are calculated based on thermodynamic equilibrium and hydraulic equilibrium models. A multi-objective optimization model which takes minimization of evaporation water consumption as a main part and minimization of energy consumption as an auxiliary part is adopted, an artificial intelligence algorithm is used for real-time solving, optimal control parameters such as flow distribution, fan rotating speed and valve opening degree are output, and dynamic adjustment of the cooling system is achieved. The system continuously corrects and optimizes model parameters through a closed-loop feedback and self-learning mechanism, ensures that the water supply temperature reaches the standard, the evaporation water consumption is minimum and the energy consumption is optimal, and remarkably improves the water saving and operation efficiency of the industrial circulating water system.

The invention relates to the technical field of liquid cooling water tower control, in particular to a liquid cooling water tower control method and system.The control method comprises the steps that the water inlet temperature Tin, the water outlet temperature Tout and the environment temperature Tenv of a cooling water tower are collected in real time, and a dynamic adjustment model is established based on the water inlet temperature Tin, the water outlet temperature Tout and the environment temperature Tenv to calculate the target cooling efficiency eta target; and according to the difference value between the eta target and the real-time cooling efficiency eta real, the cooling water flow Q and the fan rotating speed N are dynamically adjusted, and the optimal ratio of Q to N is determined through an energy efficiency optimization algorithm to reduce energy consumption. And when the cooling load is suddenly changed, a feedforward-feedback composite control mode is started. A multi-stage safety control strategy is set, and a thermal runaway prediction model is adopted to prevent risks in advance. The corresponding system comprises a distributed temperature sensing array, a frequency conversion speed regulation module, an edge calculation unit and an emergency cooling execution mechanism. The cooling efficiency can be improved, the energy consumption can be reduced, and the system stability and safety can be enhanced.