2726 results about "Thermal load" patented technology

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

The invention relates to the technical field of motor control, in particular to a permanent magnet motor current control system based on predictive compensation, which comprises a thermal field identification module, a response offset identification module, a correction track generation module, an amplitude limiting interval decision module and a compensation instruction output module. According to the method, a dynamic prediction correction mechanism is established based on thermal field characteristics and a response error trend, real-time adjustment of a current track of the permanent magnet motor is realized, a three-dimensional space thermal load curved surface is constructed by means of a stator temperature rise curve, a permanent magnet temperature change rate and driving torque fluctuation, and transient influence of thermal working condition change on magnetic performance of the motor is reflected. A multi-dimensional prediction compensation path is integrally constructed through interaction among multiple thermal field parameters, electromagnetic response variables and time sequence feature points, the response precision and control stability of the permanent magnet motor under the dynamic working condition are improved, the magnetic performance attenuation influence caused by thermal drift is reduced, and meanwhile the tracking consistency under current track disturbance is improved.

The invention relates to the technical field of air conditioner chilled water control, in particular to an air conditioner chilled water control method and system based on deep learning, and the method comprises the following steps: collecting states of an air conditioner water chilling unit, including water pump frequency and valve opening feedback, and pipe network water supply temperature, return water temperature, flow and differential pressure operation readings; according to the method, the state parameters of the water chilling unit are dynamically collected, the system operation state feature set is constructed, real-time fusion and standardization processing of multi-source heterogeneous data are achieved, the problem of time sequence dislocation caused by sensor sampling frequency differences is solved, the heat load change key input quantity is recognized based on the feature set, and the system operation state feature set is established. And the performance attenuation trend of the water pump is accurately pre-judged in combination with prediction calculation, and extra energy consumption caused by lagging adjustment is reduced. A layered optimization framework with total energy consumption minimization as a target is introduced, water supply temperature, pressure difference setting and unit operation combination are decoupled into independent optimization sub-problems, and invalid work of a water pump is reduced on the premise that the tail end heat requirement is met.

The invention provides a power plant heat tracing system intelligent optimization method based on environment temperature fluctuation, and relates to the technical field of temperature control of a power plant heat tracing system, and the method comprises the following steps: S1, data collection and preprocessing: deploying a sensor array, collecting an operation data signal # imgabs0 # of a power plant heat tracing pipeline, carrying out the preprocessing of the operation data signal # imgabs1 #, and storing the preprocessed operation data signal # imgabs0 #; outputting a standardized feature vector; s2, model establishment and parameter optimization: constructing a physical-data dual-drive heat transfer model, and inputting standardized feature vector optimization initial parameters; compared with the prior art, the method has the following beneficial effects: firstly, control parameters can be automatically adjusted according to factors such as thermal load change, external environment temperature fluctuation and equipment aging; and secondly, the temperature trend in the future short time can be predicted by constructing a heat transfer dynamic model, the control strategy is adjusted in advance, the temperature is prevented from being out of control, heat dissipation can be increased in advance or heat source input can be adjusted in advance, and safe and efficient operation of the system is effectively guaranteed.

The invention discloses an efficient cross-season energy storage energy pile, and relates to the technical field of new energy and energy conservation. The problems that in an existing energy storage system, the thermal load prediction error is large, underground thermal diffusion attenuation is caused, the heat exchange capacity is lowered, and the geological adaptability is insufficient are solved. According to the scheme, an LA mixed time sequence prediction model is adopted to optimize load prediction, a distributed optical fiber temperature measurement array and a finite element inversion algorithm are combined to accurately reconstruct a stratum temperature field, and the geological type is identified based on a support vector machine classifier to realize self-adaptive regulation and control of heat exchange parameters; meanwhile, the heat pump power, the circulating pump frequency and the heat charging and discharging rate of the phase change material are optimized through a depth deterministic strategy gradient algorithm and a gradient heat release strategy; the heat exchange efficiency, the long-term stability and the complex environment adaptability of the cross-season energy storage system are remarkably improved, and the energy-saving effect of a building energy supply system is improved.

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention discloses a server mainboard regional distributed liquid cooling heat exchange adjusting method, which comprises the following steps of: acquiring a temperature parameter set and a power consumption parameter set of each monitoring region in real time, and generating a thermal load parameter feature vector; identifying a high heat load core area set according to the heat load parameter feature vector; generating a liquid cooling branch flow distribution instruction set based on the position distribution of the high heat load core area set; executing the liquid cooling branch flow distribution instruction set, and obtaining an adjusted temperature feedback parameter set; according to the deviation degree of the temperature feedback parameter set and the target temperature interval, dynamic feedback adjustment is carried out by applying the heat conduction compensation coefficient and the fluid viscosity-temperature compensation coefficient, and an updated liquid cooling branch flow distribution instruction set is generated. The method has the following advantages and effects that by sensing the thermal load space distribution characteristics of all areas of the mainboard in real time, cooling flow distribution is dynamically adapted, real-time compensation of the fluid viscosity-temperature effect is integrated, local hot spots are eliminated, and the heat dissipation effect is improved.

The invention relates to the technical field of mechanical stress and thermal deformation monitoring, in particular to a combustion fine adjustment method based on full-hearth expansion monitoring, which is characterized in that expansion sensor arrays are arranged on each layer of a main burner area of a boiler hearth, and vibration monitoring units are arranged in a layered manner from a main burner to an SOFA air area synchronously; normal deformation, three-dimensional expansion displacement data and vibration spectrum data of the four furnace walls are collected; generating a standardized monitoring data set through wavelet noise reduction and temperature compensation processing, fusing boiler operation parameters, and inputting the boiler operation parameters into the reinforcement learning model to generate a combustor adjustment instruction; water-cooled wall expansion rate change data is collected in real time to optimize model weight and update a control strategy, and a closed-loop feedback mechanism is formed. According to the method, through multi-source sensing data fusion and dynamic modeling, sub-meter analysis of thermal load distribution and quick response of the combustion state are achieved, the boiler operation efficiency is improved, and the service life of equipment is prolonged.

The invention relates to the technical field of energy storage management, in particular to a high-energy-density liquid cooling energy storage system based on thermal management optimization, which comprises a thermal response calculation module, a thermal risk area identification module, a cooling resource allocation module, a thermal decoupling state judgment module and a charging and discharging power limiting module. According to the method, the inlet temperature, the outlet temperature and the cooling liquid flow speed of the high-energy-density energy storage equipment are obtained, the unit thermal load conversion rate is calculated, the unit thermal response deviation degree is determined, the thermal risk area higher than the standard limit is clearly recognized in the energy storage equipment, the corresponding liquid cooling channel is accurately selected, and the cooling liquid pulse flow frequency is optimized; the pertinence and timeliness of the cooling liquid in actual use are effectively improved, the local overheating hidden danger is avoided, the lag response is marked in time by monitoring the cooling liquid pressure difference and the temperature rise change in real time, the decoupling state between the cooling liquid and the thermal load is judged, the charge and discharge power dynamic reduction measure is implemented, and the trend of abnormal and continuous accumulation of the local thermal load is restrained.

The invention provides a lithium battery equalization thermal management method. Real-time temperature data is obtained by constructing a thermal distribution monitoring network, the temperature difference between a center and an edge area is analyzed, a thermal load distribution coefficient is calculated, and a dynamic heat dissipation intensity adjustment model is established; driving the multi-channel cooling system to operate according to the heat dissipation demand priority, and adjusting the flow distribution proportion of a cooling medium; a dynamic nonlinear feedback correction mechanism is adopted, secondary adjustment is carried out for uneven temperature distribution, and an optimal heat dissipation parameter combination is determined in combination with historical data. Uniformity control over temperature distribution of the battery module is achieved, the problem of heat accumulation in a center area is effectively solved, the heat dissipation efficiency under the rapid charging working condition is improved, and the safety performance and the service life of the battery module are guaranteed.

The invention relates to the technical field of heat supply system temperature control, and discloses a temperature control method of an air source heat pump heat supply system. The method comprises the following steps: establishing a temperature field model according to temperature-pressure-flow data of the head and tail ends of a building and each region, and obtaining distribution characteristics; analyzing and predicting a thermal load based on the temperature rise and drop rate and the heat storage coefficient; determining a water supply temperature correction coefficient and a valve opening degree through fuzzy control in combination with an outdoor temperature trend; generating a partition start-stop sequence according to the partition start-stop sequence; and finally calculating heat pump operation frequency and water pump rotation speed. According to the method, the partition control strategy based on temperature field modeling is established, the technical means such as unit pressure compensation and partition alternate starting are combined, accurate temperature control over all the heat supply areas is achieved, and the problem of uneven heat supply in the prior art is effectively solved.

The invention relates to the technical field of heat load prediction, and discloses a heat supply system load prediction method and system, and the method comprises the steps: collecting multi-source sensing data in real time, and collecting outdoor meteorological parameters and building structure information; based on building distribution, a pipe network structure and user load characteristics of a heat supply area, a multi-stage heat supply load prediction model is constructed. And constructing a thermal topological graph model of the heat supply area based on the graph neural network. And periodically collecting parameters of the building-level edge prediction model, the heat exchange station-level aggregation prediction model and the thermal topological graph model, performing global aggregation optimization, and updating and optimizing each edge node model. And obtaining an edge prediction result according to the optimized model, and jointly controlling the heat source output power, the main pump rotating speed and the area valve opening according to the edge prediction result and the heat source level scheduling prediction model. According to the method, the depiction capability of the system on the dynamic load change and the space heat conduction path is improved, and the generalization capability of model updating and the real-time responsiveness of edge deployment are ensured.

The invention discloses a dynamic intelligent control method and system for improving the efficiency of a rectifying tower, particularly relates to the technical field of chemical process control, and is used for solving the technical defect that an operation variable is mismatched with a physical state due to the fact that an existing distributed control system cannot recognize a hidden degradation state of equipment. The method comprises the following steps: acquiring temperature gradient distribution of each tower plate of a rectifying tower, thermal load of a reboiler and tower top condensation load parameters in real time, calculating a mass transfer efficiency offset by combining the temperature gradient distribution to generate a scaling trend index, and synchronously calculating a local entropy yield based on the thermal load and the condensation load to generate a thermodynamic irreversibility index. When the double indexes exceed a preset threshold value, it is judged that a recessive degradation state is entered, then a backflow ratio correction coefficient is reversely derived through a dynamic correlation model, and the correction coefficient is input into a distributed control system to dynamically adjust backflow ratio operation parameters; the product purity is maintained, the tower plate scaling process is obviously delayed, and long-term efficient operation of the rectifying tower is guaranteed.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention relates to the technical field of heat supply, and provides an intelligent scheduling method and system for multi-energy cooperative heat supply, and the method comprises the steps: constructing a multi-time scale division mechanism, processing time scales, and dividing the time scales into a long-term time scale, a medium-term time scale and a short-term time scale; aiming at a long-term time scale, constructing an LSTM-Transfer hybrid model to predict renewable energy output and thermal load demand; for the mid-term time scale, dynamic rolling optimization is adopted, and multi-objective optimization is achieved through an NSGA-III algorithm; and for the short-term time scale, constructing a deep reinforcement learning DRL model, and performing dynamic compensation control through the deep reinforcement learning DRL model. According to the method, different processing strategies are used for different time scales, collaborative optimization can be carried out on multi-energy heat supply on multiple time scales, and the overall scheduling effect is improved.

The invention provides a heat supply load prediction and scheduling method based on energy storage peak regulation, and the method comprises the steps: obtaining multi-modal data of a heat supply system, carrying out the preprocessing of the multi-modal data, and constructing a space-time joint feature matrix based on the preprocessed multi-modal data; inputting the spatio-temporal joint feature matrix into a multi-modal depth load prediction model for heat supply load prediction to obtain a heat supply load prediction result; the multi-modal deep load prediction model is obtained by training a deep learning prediction model fused with physical constraints through system historical data; based on the heat supply load prediction result, a distributed energy storage scheduling strategy is generated in combination with a multi-agent reinforcement learning algorithm based on an MADDPG framework, and scheduling operation of the heat supply system is carried out according to the distributed energy storage scheduling strategy; and the energy utilization efficiency and the power grid stability are improved.

The invention discloses a dynamic flow control method for a two-phase cold plate liquid cooling system based on multi-modal sensing, and relates to the technical field of equipment cooling. Comprising the following steps: S1, collecting liquid cooling monitoring data in real time, and performing data preprocessing; s2, judging the state risk of the cold plate, carrying out signal cross validation, and taking an abnormal regulation and control measure; s3, cooling liquid flow regulation and control are carried out, and the matching degree of flow regulation and control and chip thermal load is evaluated, so that flow regulation and control correction is carried out; and S4, constructing a big data modeling and simulation feedback platform, evaluating a flow regulation and control self-healing effect, optimizing each parameter and strategy, and carrying out self-learning evolution. The problems that an existing two-phase cold plate liquid cooling system generally only depends on a single temperature signal, lacks a multi-source redundancy check mechanism, has the problems of response lag, weak local reflection capacity, high risk during failure and the like, consequently, the cooling capacity distribution and adjustment precision is insufficient, and the actual requirement of a high-heat-density data center for heat dissipation is difficult to meet are solved.

The invention discloses a thermal load prediction method based on iTransform, and belongs to the technical field of heat supply system thermal load prediction, and the method comprises the steps: collecting historical meteorological data and corresponding thermal load data, and constructing a thermal load data set; constructing an iTransform-based network model, and performing learnable adversarial attack training on the network model by using the thermal load data set to obtain a thermal load prediction model; inputting the meteorological data of the prediction area into the thermal load prediction model, and outputting a corresponding thermal load prediction result; according to the method, thermal load prediction is realized by constructing the iTransform-based network model, prediction errors caused by label correlation are eliminated, and the robustness of the provided model is improved through a learnable adversarial attack training method.

The invention discloses an intelligent heat supply two-network balance control method and system based on fuzzy reinforcement learning, and the method comprises the steps: obtaining real-time operation parameters which comprise the return water temperature, the heat load and the environment temperature; based on the real-time operation parameters, preliminarily generating a preliminary adjusting quantity of the opening degree of the valve; operation data are analyzed in real time, fuzzy rule parameters are adjusted according to feedback of the reinforcement learning module, and the fuzzy rule parameters comprise priority weights and membership functions of return water temperature deviation; and according to the preliminary adjustment effect, a deep learning algorithm is utilized to construct a state-action space equation, an energy consumption design reward function is considered, the priority weight is further optimized and fed back to a dynamic optimization module of a fuzzy rule, and secondary adjustment of the valve opening degree is output to an execution mechanism. The problems that a traditional fuzzy control method cannot adapt to complex working condition changes in the operation of the heat supply two-network, the adjusting strategy lags behind, the adjusting effect is not accurate enough, and the adjusting efficiency and energy consumption are affected are solved.

The invention discloses a heat supply system multi-working-condition scheduling control method based on a large model and multiple agents. The method comprises the steps that a heat supply system digital twinborn model is established; the method comprises the following steps: setting a scheduling control center management agent, predicting a thermal load demand by adopting a large language model and combining a thermal load prediction model, dividing a system into a plurality of operation conditions, and setting a plurality of condition sub-agents corresponding to the operation conditions; the dispatching control center management agent judges whether the working condition sub-agents under the corresponding operation working conditions are triggered to start working or not, and meanwhile heat load demand values, predicted through a large language model, of all time periods in the future and the heat production amount of the new energy heat supply unit are transmitted to the corresponding working condition sub-agents; and the working condition sub-agent adopts a large language model to establish a system optimization scheduling control knowledge graph based on the large language model, obtains a scheduling control strategy of each device on the source network load side of the heat supply system, and feeds back the scheduling control strategy to the digital twinborn model for guiding the actual operation management of the heat supply system.

The invention discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention discloses a new energy automobile fuse life expectancy prediction system based on current sensing, and belongs to the technical field of power transmission and distribution monitoring. In order to solve the problems that life early warning of a dynamic adjustment mechanism is lacked and variable factors in a complex working environment cannot be handled, the system can recognize different load modes by accurately collecting current, temperature and vibration data related to a fuse and combining multi-dimensional factors such as temperature and vibration, and the service life of the fuse is accurately adjusted. The performance of the fuse under various working conditions is comprehensively evaluated, thermal load and loss conditions of the fuse can be estimated by combining a loss model and temperature influence calculation, the accuracy of life prediction is improved, an early warning threshold value is adjusted in real time through a dynamic threshold value adjusting mechanism, and the service life of the fuse can be predicted by dynamically adjusting residual life threshold values at different stages. According to the utility model, the health state of the fuse can be accurately grasped, the maintenance and replacement time can be reasonably adjusted, unnecessary shutdown or delay can be avoided, the fault risk can be effectively reduced, and the safety and reliability of a new energy automobile system can be enhanced.

The invention relates to the technical field of injection molding, and discloses a uniform temperature control injection mold which comprises a mold closing body, a plurality of cooling circulation modules are installed on the surface of the mold closing body, a macroscopic adjusting assembly is arranged in the mold closing body, and the macroscopic adjusting assembly comprises a plurality of heat conduction pipes fixedly connected to the interior of the mold closing body. Temperature difference spiral sheets are mounted in the heat conduction pipes, when the temperature in the middle of the cavity changes, hot air rises, the temperature difference spiral sheets are evenly heated and expand, then the abutting plate is pushed to move, a valve element of the flow valve is driven to rotate through a trigger arm and a swing piece, and the flow of cooling liquid is adjusted in real time; therefore, the macroscopic adjusting assembly can be accurately matched with thermal load differences of injection molding filling, pressure maintaining and cooling stages, the problem of local overheating or insufficient cooling caused by fixed waterway flow of a traditional mold is effectively solved, and the quality stability and performance reliability of automobile parts are improved by dynamically adjusting the flow of cooling liquid.

The invention provides a distributed intelligent cooperative energy-saving control method and device for an industrial air conditioner, electronic equipment and a computer readable storage medium, and is applied to the field of data processing application. The method comprises the following steps: acquiring industrial air conditioner operation related data including environment parameters and equipment state data; an intelligent cooperative control mechanism is constructed based on multi-device cooperation and hierarchical adjustment requirements, and the intelligent cooperative control mechanism comprises a multi-device linkage strategy for dynamically distributing refrigeration resources according to regional cooling and heating loads and a hierarchical adjustment algorithm of a chilled water system and a cooling system, and a control framework for achieving global optimization is formed; processing the environment parameters and the equipment state data to generate a dynamic control strategy; generating target energy-saving control information based on the data analysis result and the dynamic control strategy; and all operation data and control effects generated based on the target energy-saving control information are integrated, a high-energy-consumption link optimization scheme is generated, energy consumption data credible tracing is achieved, and the distributed air conditioning system is aggregated to participate in power grid frequency modulation.

The invention provides a cooling structure of a motor stator, an oil-cooled motor and a vehicle, and belongs to the technical field of automobiles. A guide oil duct and a stepped oil injection duct are arranged on a stator core; the middle of the stator iron core is provided with an oil inlet groove surrounding the stator iron core in the circumferential direction, and a cooling medium enters the flow guide oil channel through the oil inlet groove in the middle of the stator iron core and obliquely injects oil to the stator windings at the two ends of the stator iron core through the stepped oil injection channel. According to the invention, the oil path is directly arranged on the stator core and the stepped oil injection channels are symmetrically arranged, and the cooling medium can be intensively and obliquely sprayed to the surface of the stator winding through the stepped oil injection channels, so that the heat dissipation effect and the heat dissipation efficiency are improved, and the thermal load capacity of the motor and the working efficiency of the motor are further improved; and faults of the motor caused by non-uniform cooling, overhigh local temperature rise and temperature gradient are reduced. The structure is simple, and the overall manufacturing and maintenance cost is reduced; meanwhile, the overall size of the motor is reduced, and overall light weight is facilitated.

The invention discloses a low-carbon flexible coal-fired unit power control system and method, and relates to the technical field of power systems, and the system comprises a cascade waste heat recovery module, a boiler deep regulation and control module, a thermal coordination module, an environmental protection and emission reduction module, an intelligent operation scheduling module and an energy efficiency monitoring module. The gradient waste heat recovery module carries out gradient recovery on flue gas heat energy according to temperature zone grades; the boiler depth regulation and control module is used for regulating combustion air supply and heat absorption paths to realize low-load stable combustion; the thermal coordination module dynamically distributes thermal loads between the multiple heat sources and the thermal units; the environmental protection and emission reduction module jointly controls desulfurization, denitration and wastewater resourceful treatment; the intelligent operation scheduling module generates a load regulation and control instruction and coordinates multi-module response; the energy efficiency monitoring module collects and analyzes energy consumption and emission indexes and assists strategy optimization. The energy efficiency utilization and emission control capability of the unit under the deep peak regulation condition is improved, and flexible, efficient and low-carbon operation is achieved.

The invention relates to the technical field of intelligent control, in particular to an intelligent radiation constant-temperature and constant-temperature system and a remote monitoring method, and the system comprises a radiation tail end control module, a hydraulic balance adjusting module, a fresh air humidity regulation and control module, a cold and heat source matching module and a noise and vibration suppression module. According to the method, through fine collection and analysis of temperature data of differentiated areas and optimization of a heat exchange strategy, precise temperature control is achieved, the response capacity is improved, the fluid flowing state is dynamically adjusted, hydraulic balance is optimized, reasonable distribution of cold water and hot water is ensured, heat loss is reduced, energy efficiency is improved, and air quality monitoring and the humidity change trend are combined; the dehumidification and humidification capacity of the fresh air system is optimized, the humidity adjusting precision is improved, cold and heat source output is dynamically matched with environment heat loads, energy consumption optimization is achieved, hydraulic control and vibration suppression are optimized based on flow speed and vibration monitoring, the mute effect and comfort are improved, multi-dimensional data collaboration is achieved, and accurate adjustment, independent control and efficient energy consumption management are achieved.

The invention relates to the technical field of intelligent regulation and control of a heat supply system, in particular to a heat supply system hydraulic unbalance real-time correction method and system based on federated learning, and the method comprises the steps: constructing a temperature prediction model based on an LSTM neural network, deploying federated nodes at each user side, collecting multi-dimensional heat supply data in real time, and carrying out the preprocessing; local model parameters are encrypted and uploaded to a central server through a federated learning framework, global model parameters are generated based on verification set error dynamic weighted aggregation, and nodes are reversely updated; the updated model is used for predicting the heat load in the next one hour, the valve opening degree is dynamically adjusted in combination with a hydraulic equilibrium algorithm, and whole-network flow and heat demand matching is achieved. According to the method, data privacy is guaranteed through federated learning and a block chain technology, edge calculation and LSTM time sequence prediction are combined, the problems that a traditional method is high in data transmission delay, high in privacy risk and poor in model generalization are solved, and the real-time response capability and the energy utilization efficiency of a heat supply system are remarkably improved.

The invention discloses an intelligent control system and method for a multi-unit parallel water-cooling refrigerator, and belongs to the technical field of refrigeration equipment. The control system comprises a data acquisition module, a load prediction module, an energy efficiency optimization module and a cooperative control module. The data acquisition module is used for acquiring the running state and environmental parameters of the refrigerator in real time; the load prediction module is used for predicting the refrigerator thermal load based on the operation state and the environmental parameters; the energy efficiency optimization module is used for optimizing operation parameters of each unit according to the predicted thermal load of the refrigerator; and the cooperative control module is used for cooperatively controlling the compressor frequency and the cooling water flow of each unit according to the optimized operation parameters. According to the intelligent control system and method for the multi-unit parallel water-cooling refrigerator, precise temperature control and energy efficiency optimization of the multi-unit parallel water-cooling refrigerator can be achieved, load and water flow distribution is uniform, temperature fluctuation is small, system energy consumption is low, and the temperature control and energy saving requirements of a high-quality cold chain can be met.