419 results about "Heat load" patented technology

The invention discloses a multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method, and relates to the technical field of liquid cooling energy efficiency optimization. The multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method comprises the following steps: S1, collecting and preprocessing two-phase liquid cooling control data; s2, the thermal load level of each area is evaluated periodically, a discrete time linear state space model is constructed, and the thermal load change rate is extracted; s3, the cold distribution amount of each area is quantified periodically, the pump speed and the valve opening target value are calculated, and an adjustment control instruction is generated; and S4, high heat risk and low heat stability areas are recognized, the cooling capacity compensation requirement is evaluated, a cooling capacity enhancement and recovery distribution value is output, and a corresponding control instruction is generated. The problems that an existing two-phase liquid cooling system generally only depends on temperature feedback for adjustment, so that cooling capacity distribution response lags behind, a short-time overheating phenomenon is prone to occurring in a high-heat area, and cooling capacity waste exists in a low-heat area at the same time are solved.

The invention relates to the technical field of data processing, and provides a multi-mode water-cooling heat dissipation system and method.The thermal load urgency degree is generated by obtaining the temperature change rate and the thermal power density of a component in real time, the running state of the component is monitored, and a user scene identifier is output; comparing the temperature difference value of the cooling liquid at the inlet and outlet of the radiator with the cooling liquid flow to estimate the instantaneous heat dissipating capacity; predicting a temperature change track based on the thermal load urgency degree and the user scene identifier, calibrating the temperature change track in combination with the instantaneous heat dissipation amount, determining a candidate mode set and an optimization weight through the user scene identifier, and selecting a target heat dissipation mode; temperature distribution of different areas of the radiator is obtained according to the target heat dissipation mode so as to distribute the target rotating speed of the fan, respond to the local high heat load, control the multi-way valve to dynamically adjust the topological structure of the cooling liquid flowing path and distribute the cooling liquid flow, and deep cooperation of the fan and the cooling liquid is achieved.

The invention relates to the technical field of intelligent building heat load prediction and heat balance regulation and control, and discloses an intelligent building heat balance dynamic regulation and control method and system based on load prediction, and the method comprises the steps: constructing a time-frequency causal double-flow analysis network, mutual enhancement of the time-frequency characteristics and the causal relationship is realized; designing a hierarchical causal discovery algorithm, and mining a multilevel causal structure; developing a causal enhanced time-frequency representation learning method, and fusing time-frequency and causal information; establishing an intervention decision framework based on anti-fact analysis, and evaluating an intervention effect; an abnormal mode self-evolution recognition system is realized, and new abnormal modes are continuously learned; an interpretable abnormity diagnosis mechanism is developed, and a clear diagnosis report is provided; according to the method, the thermal load prediction accuracy is improved, the abnormal early warning capability is enhanced, the intervention efficiency is improved, the system interpretability is enhanced, and the optimization of the energy utilization efficiency is realized.

The invention relates to the technical field of immersion liquid cooling data center cooling, and particularly provides a single-phase immersion liquid cooling server circulation cooling method and system, and the method comprises the steps: comparing the real-time temperature of a cooling liquid with a preset temperature threshold value, transmitting a first program instruction by a control system, and carrying out an immersion liquid cooling cabinet internal circulation mode; or a second program instruction is sent, and a heat exchange circulation mode is carried out; after the heat exchange circulation mode is started, a main liquid outlet pipeline is opened; the cooling liquid flows back to the liquid outlet of the immersed liquid cooling cabinet through the backflow branch pipeline, the real-time temperature of the cooling liquid and the preset temperature threshold value are continuously judged, and switching between the internal circulation mode and the heat exchange circulation mode of the immersed liquid cooling cabinet is achieved according to the comparison result. The system comprises an immersed liquid cooling cabinet, a branch pipeline valve, a main pipeline valve, a main liquid outlet pipeline, a pump, a heat exchanger and a branch pipeline. According to the invention, the topology reconstruction capability of the cooling liquid flow is realized, and local circulation and global circulation are dynamically switched according to the thermal load.

The invention discloses an air pressure-boiling point self-adaptive regulation and control system and method of an energy storage immersed phase change liquid cooling system, and relates to the technical field of energy storage systems. The method comprises the following steps: calculating a thermal load of a system in real time, performing multi-objective optimization grading, and dynamically determining an optimal boiling point set value under a current working condition; resolving and obtaining a target air pressure set value by using the air pressure-boiling point relation mapping library; an improved composite control algorithm integrating feedforward control, fuzzy adaptive PID, integral saturation resistance and change rate limitation is adopted to drive an air pressure regulation execution mechanism, and regulation and control of the air pressure in the system are achieved. The system and the method have the advantages of high control precision, high response speed, good stability, high adaptability and the like.

The invention relates to the technical field of liquid cooling heat dissipation, in particular to a liquid cooling distribution device, liquid cooling heat dissipation equipment and a liquid cooling control method. The liquid cooling distribution device comprises a main liquid supply collecting pipe, a flow adjusting assembly, a multi-channel liquid distribution assembly, a plurality of cooling medium conveying pipelines and a central control unit, a proportioning valve of the flow adjusting assembly is connected into the main liquid supply collecting pipe in parallel, and a liquid distributor of the multi-channel liquid distribution assembly is correspondingly connected with the proportioning valve. A parallel branch of the liquid separator is connected with the liquid outlet node through the reversing valve, and the two ends of the cooling medium conveying pipeline are connected with the liquid outlet node and target heat dissipation equipment respectively; the central control unit achieves accurate distribution and dynamic adjustment of the flow of the cooling liquid by controlling the proportion adjusting valve and the reversing valve, the problem of uneven flow distribution caused by pipeline resistance difference is solved, the flow can be adjusted according to real-time heat loads, and system energy consumption is optimized.

The invention provides an LED lamp adaptive speed regulation heat dissipation method and system based on real-time junction temperature monitoring. The method comprises the following steps: S100, acquiring a PWM signal or an analog signal of an LED driving command signal in real time; s200, on the basis of the change of the driving instruction signal, feedforward prediction is carried out by using a dynamic thermal inertia mapping model, and a feedforward fan control quantity is generated; s300, LED junction temperature is monitored in real time through a forward voltage method, and feedback fan control quantity is generated through a feedback controller according to the deviation between the real-time junction temperature and the target junction temperature; and S400, synthesizing the feedforward control quantity and the feedback control quantity to generate a fan PWM control signal, outputting the fan PWM control signal to a fan driving circuit, and adjusting the rotating speed of the cooling fan. According to the method, driving signals and junction temperature information are processed in parallel, and feedforward and feedback control is combined, so that self-adaptive accurate regulation and control are realized, thermal load change is quickly responded, system lag is effectively inhibited, and LED heat dissipation performance is optimized.

The invention relates to the technical field of heat pump system control, in particular to a heat pump system control method and system based on compressor rotating speed feedback. The method comprises the steps that a refrigerant side parameter cluster, an air side parameter cluster and a heat load side parameter cluster of the heat pump system in a preset sliding window are obtained, and a first refrigerating capacity, a second refrigerating capacity and a third refrigerating capacity are determined through the different parameter clusters so as to determine fault source positioning indexes corresponding to the different parameter clusters; comparing the fault source positioning index with a preset fault threshold value, and determining a fault sensor cluster from a refrigerant side parameter cluster, an air side parameter cluster and a heat load side parameter cluster; and controlling the rotating speed of a compressor of the heat pump system according to a determination result of the fault sensor cluster. According to the technical scheme, the rotating speed of the compressor of the heat pump system can be effectively controlled.

The invention relates to a centralized heating balanced regulation and control method, device and equipment and a medium. The method comprises the steps that firstly, the heat supply area of a user and the heat use state of a neighbor are collected, the basic flow of the jet device is calculated according to an industrial standard formula, and final flow containing resistance characteristic data is obtained through secondary correction by combining a neighbor heat use state correction coefficient and an environment temperature influence factor. Extracting flow and inlet and outlet pressure difference from jet device parameters, adjusting a valve to enable the resistance proportion to reach a preset interval, calculating parameters such as unit area heat consumption by combining real-time flow and supply and return water temperature difference, generating regulation and control basic data through coupling analysis, adjusting the jet device when the regulation and control basic data reach the standard, and generating a dynamic scheme by combining a thermal load prediction model. And finally, capturing the flow pulse signal according to a fixed period to calculate a difference value, generating a valve control signal according to a threshold value, acquiring the water supply and return temperature after execution, and coupling with a dynamic scheme to generate a regulation feedback signal. By adopting the method, accurate regulation and control can be realized through multi-dimensional analysis, and the flow distribution accuracy and the heat supply balance are improved.

The invention discloses a copious cooling rectification real-time monitoring and correction system based on digital twinning, and the system comprises a data processing module which is used for collecting and preprocessing data of a sensor; the graph structure construction module is used for constructing a twin graph structure and generating a topological adjacency matrix; the feature extraction module is used for executing graph embedding operation and extracting virtual twin features; the feature decoupling module is used for carrying out dynamic decoupling on the virtual twin tensor and constructing a feature sequence; the twinning correction module is used for inputting the state mapping vector into a twinning correction network and correcting a virtual twinning tensor; the variational inference module is used for inferring an output confidence interval and generating a reliability mask; and the parameter optimization module is used for adjusting the reflux ratio, the tower top temperature and the reboiling heat load in the credible area to realize real-time monitoring and correction. According to the invention, the intelligent monitoring capability and the self-adaptive control level of the cryogenic distillation process are obviously improved, and the system robustness, the energy-saving property and the industrial applicability are higher.

The invention provides a spindle constant-temperature AI closed-loop heat engine system, and relates to the technical field of numerical control machine tool control and heat management. The invention aims to solve the problems of response lag, uncontrollable thermal stress, single heating strategy and the like of the traditional spindle heat engine method. The system comprises a multi-dimensional thermal state sensing module used for constructing a multi-node discrete temperature field representing the overall thermal distribution of the main shaft; the real-time heat balance feedback module is used for judging the cold / hot starting state of the main shaft according to the initial temperature difference and executing a differentiated feedback control strategy; and an AI prediction forward compensation module that predicts a future cutting thermal load by pre-reading a machining instruction and generates a feedforward thermal compensation instruction. According to the method, the thermal stress is controlled by introducing the radial thermal gradient index as the safety constraint, negative precooling or positive preheating is achieved in combination with the AI predictive capacity, a feedback and feedforward composite control mode is formed, and the dynamic thermal stability and the machining precision of the main shaft under the complex working condition are improved.

The invention relates to an energy-saving control strategy for a medical building air-conditioning ventilation system based on climate change characteristics and load prediction. The energy-saving control strategy comprises the following steps of: acquiring all original data by utilizing various sensors, actuators and the like of a sensing layer; calculating, optimizing and deciding on an intelligent control layer (including building comprehensive cooling and heating load prediction, clean operation department area cooling and heating demand calculation, dynamic water supply temperature optimization, a particle swarm optimization (PSO) algorithm for domain optimization, constraint check and other algorithm modules) on a central management server; and an optimization result is issued to the execution layer and the executed equipment layer, so that energy-saving control is finally realized. The method overcomes the defects that the existing hospital building load prediction technology mostly adopts a macroscopic and rough prediction model, and the global optimization particle swarm optimization algorithm is slow in convergence and long in solving time when coping with a large medical building adopting multiple types of cold and heat source equipment. The device is suitable for large-scale hospital projects in hot-summer and warm-winter areas.

The invention relates to the technical field of ground source heat pump operation control, in particular to an optimal adjustment method of a ground source heat pump system, which comprises the following steps: S1, collecting underground soil temperature and system heat load of the ground source heat pump system; s2, calculating a soil heat balance index based on the underground soil temperature; s3, according to the soil heat balance index and the system heat load, the optimized operation mode of the ground source heat pump system is judged; and S4, according to the optimized operation mode judged in the S3, the operation parameters of the ground source heat pump system are adjusted, and a corresponding execution mechanism is driven. According to the method, the operation mode is judged by introducing the soil heat balance index and combining the system heat load, meanwhile, the flow of the circulating water pump, the frequency of the compressor and the opening degree of the heat exchange valve are subjected to interval adjustment, and unification of energy efficiency optimization and long-term balance of the soil heat environment is achieved.

The invention relates to a multi-energy-load scene generation method and device based on a diffusion model. Multi-energy load data and condition information are input into a time-space and cross-dimension decomposition reconstruction network of a classifier-free condition diffusion architecture; projecting the multi-energy load data into time sequence embedding and cross-dimension feature embedding; coding the condition signal and the diffusion step number together to obtain a condition embedding vector; the reconstruction network comprises a time channel and a feature channel, a global context relationship is captured by an encoder, and condition information is fused based on a condition embedding vector through a decoder; wherein an STAR module is arranged behind the decoder of the feature channel; and integrating the output of the time channel and the output of the feature channel, and then carrying out layer-by-layer denoising to generate a multi-energy-load scene. Compared with the prior art, the method has the advantages that autocorrelation, cross correlation and dislocation correlation among cold, hot and electric loads of the building group and renewable energy output are considered, and high-fidelity scene generation under condition control is realized.

The invention discloses a building energy consumption management and control method, belongs to the technical field of building energy-saving control, and solves the problems of response lag and energy waste caused by independent operation of a lighting system and an HVAC system. According to the technical scheme, a building parameter database is pre-stored; environment data and equipment power consumption are collected in real time; calculating a solar radiation thermal gain coefficient and a thermal load influence quantity of illumination on the HVAC; when the dynamic coupling factor exceeds a first threshold value or the variation of the illumination intensity exceeds a second threshold value within a preset time, triggering collaborative optimization; a lighting target power consumption value (the compensated lighting power consumption is not lower than a preset proportion of the basic lighting demand value) is synchronously generated after response, and the HVAC set temperature is corrected based on the thermal gain coefficient; and finally a control instruction is issued. The method is mainly used for real-time energy consumption collaborative optimization of large-scale public buildings.

The invention discloses an air conditioning system control method and system based on reinforcement learning and a twinborn model strategy. The method comprises the steps that S1, a data-driven twinborn model of a central air conditioning system is constructed; s2, performing offline pre-training based on a virtual environment formed by a twin model; s3, performing online integration on a plurality of pre-trained reinforcement learning algorithms and the twin model to form a reinforcement learning strategy integration framework; and S4, the final control action is issued to execution equipment of the central air conditioning system. According to the invention, by constructing the serial data-driven twinborn model, the running state, energy consumption and cooling and heating load supply-demand relationship of the central air-conditioning system are accurately simulated, a high-fidelity virtual environment is provided for various reinforcement learning algorithms, offline pre-training is realized, an initial control strategy is generated, and risks caused by blind exploration in a real system are avoided. Continuous self-adaptive optimization is achieved, and the stability and the energy efficiency level of intelligent control of the air conditioning system are remarkably improved.

The invention relates to a heat supply prediction method and system based on spatial-temporal feature fusion deep learning, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-source heterogeneous data, and constructing an integrated data set; s2, preprocessing the data; s3, constructing a graph structure model of the heat supply system, and constructing a weighted undirected graph; s4, constructing and executing forward calculation of the space-time double-flow deep network; s5, designing a composite loss function including mean square error loss and physical constraint loss, and performing joint optimization training on the space-time double-flow deep network; and S6, performing multi-step heat supply load prediction by using the trained model, outputting a heat supply load curve of each heat exchange station in a specified time period in the future, and integrating a prediction result with a heat supply scheduling system. The method has the advantages that the prediction precision is improved compared with that of a traditional machine learning model by capturing the spatial-temporal characteristics at the same time, and the advantages are more remarkable in extreme weather.

The invention relates to an intelligent refrigeration control method based on dynamic computing power distribution, electronic equipment and a computer storage medium. The method comprises the following steps: acquiring a whole-network multi-dimensional data stream, acquiring data in real time through a distributed sensor network, and performing multi-source data fusion and space-time alignment to form a monitoring data set; according to the monitoring data set, a temperature field is generated based on computational fluid dynamics (CFD), and through automatic closing of a cold and hot channel, the air supply amount is adjusted to be matched with a thermal load; predicting a high-load area based on a load prediction model, and dynamically migrating computing power to a low-temperature area or dormant redundant nodes in combination with real-time temperature distribution; a multi-target optimal regulation and control model is constructed, and based on reinforcement learning, the coupling relation among the IT load, the environmental parameters and the refrigeration power consumption is continuously optimized; through energy consumption statistics and analysis, automatic early warning is carried out on abnormal energy consumption and local overheating, and a thermal anomaly area is dynamically displayed through a multi-dimensional chart. According to the invention, deep collaboration of the computing power of the data center and the refrigeration system is realized.

The invention discloses a green data center key cooling system, and relates to the technical field of data center energy saving and heat management. The system comprises a cabinet, a cold and hot channel, a refrigeration device, a waste heat recovery device and a control system. The refrigeration device comprises an indoor refrigeration unit, an outdoor heat dissipation unit and a fluorine pump energy-saving unit, and heat exchange and cooperative operation are achieved through pipelines. The cold and hot channel is provided with a guide plate and an adjustable air valve to optimize airflow distribution; the waste heat recovery device comprises a fluorine-fluorine heat exchanger, a heat pump system and a circulating medium box, and recovery and reutilization of heat energy are achieved. The control system integrates a lightweight edge AI module, constructs a multi-stage temperature sensing and load prediction mechanism, dynamically adjusts the power output of each cooling unit, and supports redundant fault-tolerant switching. The system intelligently switches the operation modes of the fluorine pump and the heat dissipation unit based on the external temperature and the thermal load prediction result, energy consumption is effectively reduced, and the energy efficiency ratio is increased.

This invention discloses a coordinated control method for a heating system based on air source heat pumps and building heat exchange units, comprising: setting up a composite heating system consisting of air source heat pump units, building heat exchange units, heating pipe networks, and buildings, and establishing a digital twin model of the composite heating system; based on the time-period heat load forecast values ​​of each building, peak-valley electricity pricing strategies, and the time-period heating capacity range of the air source heat pump units, with the objective function of minimizing the comprehensive heating cost of each building, solving for the optimal heating mode of the composite heating system for each building in each time period and the optimized load ratio of the air source heat pump units and building heat exchange units under the composite heating mode; establishing a supply and return water temperature control model for the air source heat pump units and a pump and valve control model for the building heat exchange units to obtain the control strategies of the air source heat pump units and building heat exchange units under the corresponding heating modes.

The invention relates to the technical field of power distribution network toughness demand response, in particular to a power distribution network toughness improvement optimization method, system and device based on response prediction and a storage medium. Training an integrated decision tree model based on historical data to predict a response intention value of the cooling and heating load user, and determining a temperature regulation boundary and a response frequency upper limit according to the response intention value; generating an initial scene set through source load uncertainty sampling, extracting a typical scene by adopting a transportation distance scene reduction method, and introducing an overall offset constraint and a single-point extreme value constraint to perform two-dimensional limitation on scene probability distribution; a scene probability combination enabling the load recovery value to be minimum is searched in a probability distribution domain, and a scheduling scheme enabling the key load recovery value to be maximum is solved under the condition that the power flow constraint, the cold and heat power balance constraint and the temperature regulation boundary constraint are met; and carrying out probability weighting on the load recovery values of different scenes to obtain a toughness evaluation value, and carrying out sensitivity analysis.

The invention relates to the technical field of machine room environment regulation and control, in particular to a communication base station intelligent energy-saving window control design method and system, and the method comprises the steps: obtaining and classifying base station source load data, setting energy-saving window parameters and an off-network strategy, monitoring a power supply state, and cooperatively controlling temperature control equipment in combination with electric quantity temperature difference. According to the method, the thermodynamic state is digitalized by calculating the outdoor cold source potential energy gradient and the indoor thermal load cumulative acceleration and combining the enthalpy difference allowance so as to accurately evaluate the natural cold source potential; the optimal ventilation opening degree and the fan rotating speed are matched through multi-dimensional feature clustering, so that the condensation risk is strictly restrained according to dew point approximation deviation while the heat dissipation efficiency is guaranteed, and in addition, a dual-target evaluation function in an off-grid scene is constructed to solve a non-dominated solution set of endurance time and thermal safety. A dynamic protection threshold value is generated; and when the commercial power is interrupted, a control strategy is adaptively adjusted according to the real-time load and the environment temperature, so that the off-network life cycle of the base station is maximized, and core equipment is ensured to always run within a safe temperature boundary.

The invention relates to the technical field of building energy consumption management, in particular to a building heating ventilation air conditioner intelligent control method for energy efficiency optimization, and the method comprises the steps: dividing a building into a plurality of independent temperature control regions, and deploying multifunctional sensor nodes; acquiring real-time environment data, personnel data and weather forecast data; the ultra-short-term load prediction model is maintained to predict the cold / heat load demand of the area in the future 15 minutes, and a cold / heat load demand prediction value is obtained; reporting the cold / heat load demand prediction value to a central processing unit in a standardized data format; the central processing unit optimizes the energy distribution of the whole building through a multi-objective optimization algorithm based on the prediction requirements of all the regions, and obtains a resource scheduling list in the next 15-minute period; and according to the resource scheduling list, a regulation and control instruction is issued to equipment in each independent temperature control area. Therefore, the problems of response lag, extensive control and the like in the prior art are solved.

The invention relates to the technical field of phase change liquid cooling energy storage, in particular to an immersed phase change liquid cooling battery thermal load dynamic modeling and prediction method, and the method comprises the steps: dividing the surface of a battery into n independent calculation regions based on the geometric structure and thermal characteristics of a battery module, static thermal resistance coefficients, dynamic thermal capacity coefficients and thermal diffusion weight coefficients of all the areas are simulated and calibrated through experiments and computational fluid mechanics, and a calibrated parameter matrix is stored in a nonvolatile memory of a BMS; the method comprises the following steps: acquiring battery temperature data at high frequency through a multi-source sensor network arranged in each partition, performing Kalman filtering and derivation calculation through a preprocessing module of a data processing layer to obtain a temperature change rate, calling a pre-stored coefficient by a regionalization modeling module, and calculating an instantaneous thermal load of each partition and a total thermal load of the module; a traditional'uniform temperature body 'hypothesis is broken through, a battery module is divided into a plurality of subareas, and the device is particularly suitable for capturing a non-uniform heat phenomenon caused by non-uniform flow field and local bubble generation under an immersed liquid cooling condition.

Provided herein are systems and methods for managing a temperature of a battery pack. For example, the method may include detecting, by one or more processors, a charge event for the battery pack, and, responsive to detecting the charge event, determining, by the one or more processors, a heat load for the battery pack, based on a current demand corresponding to the charge event of the battery pack, and transmitting, by the one or more processors, a signal to a thermal management system, to modify a condition of the thermal management system for cooling the battery pack, responsive to determining the heat load.

The invention discloses a heat dissipation device and a control method, and relates to the technical field of heat dissipation, the heat dissipation device comprises a main heat dissipation module, an expansion heat dissipation module and a control unit, the main heat dissipation module and the expansion heat dissipation module are flexibly combined and split through a quick connector to form a modular structure, independent maintenance and replacement of each module are facilitated, and the heat dissipation efficiency is improved. And the heat dissipation requirements of different numbers of to-be-cooled pieces can be met. Each heat dissipation module is provided with an independent flow control valve, the first temperature detection mechanism on the corresponding part to be cooled is combined, under coordination of the control unit, the opening degree of the valve can be adjusted in real time according to first temperature data, and dynamic adaptation of the flow of cooling liquid is achieved. Therefore, the technical problems that an existing heat dissipation system is fixed in structure and difficult to adapt to the heat dissipation requirements of different numbers of hardware in the updating process of the server, and the heat dissipation performance is not matched with the actual heat load can be solved, and the technical effects of adapting to the heat dissipation requirements of different numbers of to-be-cooled pieces and achieving dynamic adaptation of the cooling liquid flow are achieved.

The invention relates to a combined cooling and heating collaborative optimization method for a central air conditioner, which comprises the following steps of: when cold and heat load fluctuation is detected, according to the response speed difference of a cooling and heating system, asymmetrically controlling the time sequence and pre-starting or hot standby of equipment, so that the time sequence of a cold and heat source is coordinated; potential physical interference nodes or cross paths are identified in the operation process of the cooling and heating system, a hydraulic or energy transmission path is temporarily passivated, and the reverse coupling influence between the cooling and heating system is reduced in combination with hot side delayed water feeding and cold side path adjustment; according to the method, the asymmetric control time sequence and the cold and hot response priority queue are introduced, the control windows can be dynamically distributed according to the cold and hot source response speed difference, the system control conflict problem caused by simultaneous adjustment of the cold and hot sources is avoided, the cold and hot system rush adjustment phenomenon is effectively relieved, and response adjustment is more orderly. By means of the pre-starting device and the hot standby mechanism, the device can be awakened in advance or maintained in the heat balance pre-starting state before load fluctuation.

The invention provides a centralized heating optimal scheduling method and system based on multi-heat-source dynamic matching, and the method comprises the steps: constructing a thermal load prediction model, and fusing various types of data to generate a thermal load demand dynamic curve in the next 24 hours; establishing a dynamic priority ranking model, and generating a heat source priority sequence; generating a multi-heat-source cooperative operation strategy; constructing a multi-heat-source dynamic matching optimization model to obtain an optimal scheduling strategy; heat sources are adjusted in real time through the central control system, and dynamic correction is conducted by combining pipe network feedback data; and periodically updating the thermal load prediction and dynamic priority ranking model. According to the method, a bidirectional LSTM neural network is fused with data to construct a high-precision thermal load prediction model, and a curve is generated in combination with a feature pyramid network; meanwhile, related data are periodically combined, the weight of the LSTM neural network is updated through an incremental learning algorithm, the weight of the priority ranking model is adjusted through a gradient descent method, the adaptability of the prediction model is continuously optimized, and the prediction precision is improved.

The invention discloses an air conditioning method and system for equal-state air supply, a medium and equipment, and belongs to the technical field of air conditioning equipment. The method comprises the steps that on the basis of the indoor design temperature, the indoor design relative humidity, the indoor heat load and the indoor humidity load, the heat-humidity ratio parameter is calculated, and the target temperature value of an equal-state air supply point is determined on an enthalpy humidity diagram; real-time temperature data of the air supply outlet of the fresh air handling unit and the air supply outlet of the fan coil are obtained and compared with the target temperature value, and a corresponding fresh air temperature deviation value and a corresponding return air temperature deviation value are generated; and according to the fresh air temperature deviation value and the return air temperature deviation value, the opening degrees of adjusting valves of the fresh air handling unit and the fan coil are adjusted, so that the air supply temperature of the fresh air handling unit and the air supply temperature of the fan coil are both maintained at the target temperature value. By implementing the method, the technical problem that in the prior art, accurate temperature and humidity control and refrigeration system energy efficiency improvement of a traditional air conditioning system in the high-temperature and high-humidity area high-humidity environment are difficult to achieve at the same time can be solved.

The invention discloses a cabinet temperature control method and device for a distributed control system, and the method comprises the steps: reconstructing a three-dimensional temperature distribution model in a cabinet through the combination of real-time temperature data and a spatial interpolation technology, so as to obtain the current temperature distribution data; and based on a neural network, in combination with the historical thermal load data, the operation state data of the system, the external environment data and the temperature distribution data, determining thermal load prediction data and temperature distribution prediction data in a future preset time period. And based on the real-time temperature data, the thermal load prediction data and the temperature distribution prediction data, a multi-objective optimization algorithm is adopted to optimize the control instruction of the temperature adjustment equipment in each partition in the cabinet. According to the optimization result of the control instruction, the temperature adjusting device of each partition in the cabinet is controlled, partition temperature control of the cabinet is realized, the thermal load and the temperature are predicted by using a spatial interpolation technology in combination with a neural network, partition control instruction optimization is carried out on the cabinet, and the precision and the response speed of the temperature control method are improved.