7753 results about "Heating system" patented technology

The invention discloses a heat supply intelligent decision-making system based on an artificial intelligence large model, and the system comprises a data layer which is used for storing multi-type data of a heat supply system source network load; the knowledge base establishment and large model training layer is used for establishing a heat supply intelligent decision diversified task knowledge base and establishing each task large model; the man-machine enhancement optimization large model layer is used for providing improvement suggestions for a large model, optimizing generated answers, evaluating the answers generated by the large model and performing large model parameter optimization training; the agent decision-making layer is used for integrating each task large model into each agent to carry out environment perception, collaborative interaction and autonomous adaptive decision-making; the analogue simulation layer is used for providing a simulation operation environment, a user interaction interface and scene dynamic parameter simulation; and the agent evaluation optimization layer is used for constructing performance indexes for evaluating the agents, and evaluating, optimizing and adjusting behaviors and decisions of the agents.

A base station is disclosed for an unmanned aerial vehicle (UAV) that includes: an enclosure defining a window that is configured to receive the UAV to allow for entry of the UAV into the base station and exit of the UAV from the base station; a door that is movably connected to the enclosure such that the door is repositionable between a closed position and an open position; a sealing member that extends about the window and which is configured for engagement with the door so as to form a seal therewith in the closed position; and a heating system that is supported by the enclosure and which is configured to heat the door and / or the sealing member to support operation (e.g., opening and closure) of the door in a cold environment, wherein the heating system includes at least one light source and at least one heating element.

The present invention provides a multi-drum washing machine, comprising a first drum, a second drum, and an electric heating system. The electric heating system is capable of generating a hot air flow, and the hot air flow of the electric heating system can be delivered to the first drum and / or the second drum simultaneously or separately. The multi-drum washing machine also comprises a first drum and a heat pump system. The heat pump system is capable of generating a hot air flow, and the hot air flow of the heat pump system can be delivered to the first drum. Compared with the prior art, the multi-drum washing machine described in the present invention uses a common electric heating system for drying the first and second drums, thereby reducing the manufacturing cost and space occupied by the multi-drum washing machine. The first drum uses the heat pump system for drying, avoiding the need for three drums to be dried simultaneously using a single drying system, thereby improving the drying effect of the clothes, shortening the drying time, and enhancing the user experience.

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 relates to the field of electromagnetic induction heating, and discloses a global temperature control method for an electromagnetic induction heating system, which comprises the following steps of: acquiring a two-dimensional temperature distribution diagram of a heating area at a plurality of key moments through a multi-point infrared temperature acquisition and induction heating coil synchronous excitation mechanism; performing multi-dimensional region segmentation on the global temperature scene data set, and identifying a temperature abnormal region and a thermal diffusion path feature based on a boundary thermal gradient change rate and an energy transfer path analysis algorithm; a distributed power regulation and control method and a historical thermal field evolution rule are introduced based on the regional thermal response feature map; thermal field feedback information is collected in real time in the temperature control process, and the current global thermal field state is judged in combination with a thermal distribution stability judgment criterion; and based on the updated control parameter configuration table, in combination with the thermal performance parameters of the material and the geometric information of the workpiece, generating an operation instruction set of the heating process, and executing intelligent regulation and control. The induction heating device has the advantage of improving the induction heating quality.

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 provides a cross-seasonal heat storage coupling solar ground source heat pump combined heating system. The cross-seasonal heat storage coupling solar ground source heat pump combined heating system mainly comprises a solar heat collector, a valve, a heat storage water tank, a heating tail end, a buried phase change heat storage water tank, a buried pipe and a heat pump. Solar radiation heat in non-heating seasons is collected and stored through cross-seasonal heat storage, and the stored heat is extracted in the heating period in winter and is used for heating a building together with a solar ground source heat pump combined heating system. The system has the beneficial effects that the system efficiency is improved, and the use of thermal insulation materials is reduced, so that the cost is reduced; the buried pipe absorbs and inhibits heat diffusion of the heat storage water tank, heat balance of a soil temperature field around the buried phase change heat storage water tank is guaranteed, and efficient operation of a heat pump unit is maintained. The invention has six control methods: solar energy-soil heat storage, solar energy direct heating, solar energy coupling buried phase change heat storage water tank heating, buried phase change heat storage water tank direct heating, solar energy ground source heat pump combined heating and ground source heat pump independent heating.

The invention discloses a sewage waste heat recovery heating control method and system based on a digital twinning heat pump. The method comprises the steps that a digital twinning platform of a sewage waste heat recovery heating system is constructed; based on the operation parameters of the entity equipment of the sewage waste heat recovery heating system, establishing a digital twinborn model of the sewage waste heat recovery heating system; deploying the established digital twinborn model of the sewage waste heat recovery heating system in the established digital twinborn platform of the sewage waste heat recovery heating system; based on the physical equipment operation parameters of the sewage waste heat recovery heating system and the virtual data of the digital twinborn model, an optimization target model of the sewage waste heat recovery heating system is constructed, and an optimal control strategy is obtained; dynamic optimization control is carried out on the heating system entity equipment according to the optimal control strategy; the prediction precision is improved through real-time data calibration, model prediction control is fused, second-level response and multivariable cooperative regulation and control are achieved, and the problems that a traditional sewage waste heat recovery heating system is low in energy efficiency, slow in response and high in maintenance cost are effectively solved.

The invention relates to the technical field of new energy, in particular to a fan blade composite deicing system and method.The fan blade composite deicing system comprises a sensing layer, a decision-making layer, an execution layer and a feedback layer, and the decision-making layer comprises a graph neural network ice type recognition module, a CFD-icing coupling simulation module, a health management module and an energy consumption scheduling algorithm unit; the execution layer comprises a control unit and an execution unit, the control unit comprises a dynamic partition heating controller and a self-adaptive vibration controller, and the execution unit comprises an electric heating system and a vibration deicing system; compared with the prior art that a single deicing mode is usually adopted, and the adaptability to different ice type physical characteristics is poor, the scheme adopts a differential execution strategy, and partition electric heating control is carried out on different chord length areas of the blade based on the ice type recognition result; and high-frequency piezoelectric standing wave vibration is adopted for glaze ice, low-frequency eccentric wheel vibration is adopted for glaze ice, and the beneficial effects that the deicing efficiency is remarkably improved, invalid energy consumption is reduced, and damage to blades is reduced are achieved.

The invention relates to the field of crude oil heating, in particular to a full-automatic electric heating system and device based on the integrated heat storage technology, and the system comprises a temperature scale module, a heating module and a heat storage module. The heating module is provided with all temperature control nodes of the oil gathering pipeline, target temperature difference compensation, pipe section heat loss compensation and a flow velocity correction factor are integrated, and adaptive electric heating power of all the temperature control nodes of the oil gathering pipeline is generated through a reverse cascade feedback control algorithm. The heat storage module continuously collects temperature field characteristics, operation condition characteristics and medium thermal attributes of the oil gathering pipeline, judges whether heat storage operation is triggered or not through multi-dimensional coupling evaluation, distributes heat storage media through a priority algorithm, drives a valve to switch and execute adaptive heat storage operation, and achieves precise temperature field regulation and control and energy efficiency optimization of the oil gathering pipeline. And stable crude oil transportation is guaranteed.

The invention discloses a digital twinning-based thermal heat supply system defense cooperative protection method and system, particularly relates to the technical field of industrial control system safety, and is used for solving the problems of hidden attack missing detection and response delay caused by splitting of energy flow and data flow in an existing protection mechanism. Physical-network behavior collaborative verification is realized by constructing a digital twinborn model, and precise protection is realized by quantifying coupling failure characteristics of energy flow and instruction flow. The method specifically comprises the following steps: collecting production data, industrial control logs and network traffic; constructing a digital twin model to simulate expected behaviors of equipment, communication and physical states; comparing and detecting abnormal data in real time; extracting a thermodynamic potential gradient characteristic quantity and a path integral characteristic quantity from the abnormal data; calculating a covariant difference value of the two characteristic quantities, and judging an external attack when the covariant difference value exceeds a threshold value; when an attack is triggered, a micro-isolation gateway is deployed, dynamic access control is implemented based on an equipment fingerprint, a user behavior sequence and an environment fingerprint, and an attack chain is blocked.

The invention discloses an optimal scheduling method for a multi-energy complementary virtual power plant, and the method comprises the following steps: building an objective function of an optimal scheduling model of the multi-energy complementary virtual power plant, the objective function comprising two optimization objectives of economic optimization and carbon emission optimization; establishing constraint conditions of the multi-energy complementary virtual power plant optimization scheduling model, wherein the constraint conditions comprise power supply system balance, gas supply system constraint, heat supply system constraint, heat supply network constraint and cold supply system constraint; based on the objective function and the constraint condition, a two-stage robust optimization model of the multi-energy complementary virtual power plant is constructed, and two types of adjustment resources of distributed energy storage and demand response load are introduced into the two-stage robust optimization model; and for the two-stage robust optimization model of the multi-energy complementary virtual power plant, an original problem is decomposed and iterative solution is carried out by using a column and constraint generation algorithm, and an optimal scheduling strategy for the multi-energy complementary virtual power plant is obtained. According to the invention, the power grid can be flexibly regulated and controlled by optimizing the scheduling strategy.

The invention provides an underground heat supply system based on an air energy heat pump as a heat source and a remote monitoring method, and the system comprises a heat source dynamic tuning subsystem which carries out the dynamic parameter optimization processing of environment temperature and energy storage state data, and obtains an air energy heat pump operation instruction; first characteristic adjustment of air energy heat pump output is formed through an air energy heat pump operation instruction, and matching of the temperature of an energy storage water tank and an environment heat source is achieved. The underground hot air accurate adjusting subsystem obtains underground real-time temperature data, the underground real-time temperature data is subjected to spatial thermal field calculation processing to obtain air volume demand parameters, and dynamic balance between the air supply intensity and the underground thermal environment is formed; and the multi-stage cooperative control center subsystem performs three-stage logic verification processing on the signals of the air energy heat pump operation instruction and the fan frequency instruction to obtain an equipment regulation and control instruction set. The underground intelligent heat supply system of the air energy heat pump, which has the environment self-adaption capability, the dynamic balance capability of a thermal field and the remote monitoring characteristic, is constructed.

The invention discloses a greenhouse temperature data monitoring method and system based on data analysis. The method comprises the following steps: S1, collecting and constructing an environment data set by using a multi-point sensor array arranged in a greenhouse; s2, obtaining a preprocessed environment data set; s3, forming an initial partition structure; s4, when it is monitored that the environmental data change exceeds a threshold value, dynamically adjusting partition boundaries and the number of regions to obtain a self-adaptive partition result; s5, performing feature extraction on the environment data of each partition in the self-adaptive partition result to form a feature set for model training; s6, obtaining future temperature prediction results of different partitions in the greenhouse; and S7, generating greenhouse environment regulation and control suggestions in combination with future temperature prediction results of different partitions, and carrying out auxiliary regulation on a ventilation system, a heating system and a shading device. The problems of insufficient precision and poor adaptability in greenhouse temperature monitoring and regulation in the prior art are solved, and efficient technical support is provided for intelligent agriculture development.

The invention provides a battery heating system of a vehicle, a control method and the vehicle. The system comprises a multi-phase inverter, a multi-phase winding, an energy storage device and a controller. Wherein a bus of the multi-phase inverter is correspondingly connected to a bus of a vehicle battery, one end of the multi-phase winding is correspondingly connected to a bridge arm of the multi-phase inverter, a neutral point of the multi-phase winding is connected to one of the buses of the battery, the energy storage device is connected to the battery, and the controller is connected to the multi-phase inverter; the controller is configured to determine a direct current duty ratio signal and an alternating current duty ratio signal according to the real-time heating requirement of the battery, and control the action of a bridge arm of the multi-phase inverter based on the direct current duty ratio signal and the alternating current duty ratio signal. And heating the battery by using the target alternating current corresponding to the real-time heating requirement of the battery formed at the neutral point of the multi-phase winding. The DC duty ratio signal and the AC duty ratio signal jointly control the action of the multi-phase inverter, so that the battery can be heated through the modulatable duty ratio signal.

The invention relates to the technical field of temperature control, in particular to a heating and ventilation heating power constant-temperature variable-flow control method, and provides the following scheme that circulating pump frequency instructions and water temperature response data of multiple historical regulation and control periods are collected, and a dynamic track of the current water temperature in a temperature inertia field is constructed; calculating an attraction potential energy state between the current water temperature and a target constant temperature value in combination with the disturbance residual matrix; and outputting the frequency bandwidth interval of the circulating pump based on the state to realize dynamic adjustment. An adjacency matrix and a Laplacian matrix are constructed by introducing a hydraulic communication relation, spatial convolution and weighted updating are performed on disturbance residues in combination with a topological diffusion core, and accurate propagation and suppression of disturbance are realized. The method can effectively solve the problems of nonlinear response, delay effect and disturbance coupling of the water temperature to the pump frequency, and the temperature stability and the energy utilization efficiency of a heating system are improved.

The invention relates to the technical field of energy system control, and discloses a distributed cooperative fault-tolerant control method for a multi-energy-station cooling and heating system, and the method comprises the steps: enabling each energy station to measure and calculate a residual error based on a physical model and a sensor, forming a normalized health index, and carrying out the neighborhood broadcasting; carrying out robust anomaly judgment by utilizing a neighborhood median and a median absolute deviation, and isolating an abnormal site; cost coefficients are automatically generated for available sites according to equipment maneuverability, and control redistribution of minimum disturbance is solved and implemented through distributed consistency optimization; an exponential weighting updating mechanism of a residual error sequence is adopted to carry out online self-adaption on a noise baseline and a judgment threshold value so as to realize closed-loop self-calibration; when sensor abnormity, equipment errors or individual station faults occur in the multiple energy stations which work cooperatively, distributed rapid identification is achieved, faults are isolated, energy supply is redistributed with minimum disturbance, system control requirements are met, and equipment safety constraints are kept.

Provided is a temperature therapy chamber comprising a housing with an interior for accommodating a user. The interior of the chamber has a heating system configured to increase the temperature within the housing to create a heated area. The housing also has a plunge pool system having a pool with an opening accessible from within the interior and configured to hold water and accommodate the user. A chiller is configured to decrease the temperature of the water in the pool. The user can move between the heated area and the pool without leaving the interior of the housing. The housing can also include a vertical rail system with a mounting plate slidably coupled to at least one rail on a wall of the housing and configured to removably support the heating element. The user can move the heating system between a top and bottom position of the vertical rail system.

The invention discloses a preparation device and method for growing a large-size silicon carbide single crystal. A raw material gas phase in a high-temperature area is transmitted to the surface of a seed crystal in a low-temperature area for crystal growth by using a physical gas phase transmission method; the device comprises a furnace chamber, a movable sectional type heating system, a thermal insulation material, a graphite crucible, porous graphite, a seed crystal unit, a crystal growth induction forming device and an electric field device. The method comprises the following steps: installing a seed crystal unit in a crucible filled with silicon carbide powder, placing the crucible in a heater, and carrying out a crystal growth stage to prepare the silicon carbide single crystal. The whole growth process of the crystal is in a constant temperature state, thermal stress caused by the temperature difference between the growth end and the seed crystal end and the temperature difference between the crystal edge and the crystal center in the top end seed crystal method crystal growth process is avoided, the dislocation defect of the crystal is reduced, and finally the high-quality silicon carbide single crystal is obtained. And the production cost is reduced.

The invention relates to a device and a method for simulating influence of underground water seepage on an existing tunnel and a freezing wall, the device comprises a test box body, a tunnel model, a seepage system, a data acquisition monitoring system, a saline water circulation cooling system and a saline water circulation heating system, the test box body is formed by splicing three box bodies with different structures; a formed space area is used for laying a test soil body and a broken stone hardcore; the tunnel model is arranged in the hollow area in the test box body; the seepage system is used for providing an underground water source for simulating seepage; the data acquisition monitoring system is used for monitoring and recording the soil pressure and displacement of the soil body around the tunnel model and the temperature change of the soil body near the freezing pipe; the saline water circulating cooling system is used for providing cooling capacity required by soil body freezing; and the saline water circulation heating system is used for providing heat required for pulling out the freezing pipe. The device can be used for simulating the influence of groundwater seepage on soil deformation and soil pressure around the existing tunnel, adjusting the seepage direction of groundwater and restoring more real groundwater seepage movement.

The invention relates to the technical field of electric vehicle charging, in particular to a self-adaptive current regulation electric vehicle charging method, which comprises the steps of monitoring battery parameters of an electric vehicle in real time, analyzing the health state of a battery based on the monitored battery parameter data, and predicting the optimal charging current demand of the battery by utilizing a machine learning algorithm. Generating a charging current curve adaptive to the current battery state, dynamically adjusting the charging current according to the predicted charging current curve, starting a cooling or heating system according to the temperature change condition in the charging process, continuously monitoring the voltage, temperature and current change conditions of the battery in the charging process, and starting the cooling or heating system when the battery reaches a preset charging upper limit. And the charging current is gradually reduced, and a charging report is generated after charging is completed. The charging efficiency is improved, the service life of the battery is prolonged, the safety of the charging process is enhanced, detailed charging information is provided for a user, and monitoring and maintenance of the battery are facilitated.

The invention discloses an analogue simulation system for operation of a ground source heat pump and a gas boiler in a severe cold area. The analogue simulation system comprises a heat supply correction model building module, a ground source / gas heat supply estimation module, a heat supply dispatching distribution module and a correction model optimization module. According to historical operation data of a ground source heat pump subsystem, heat supply correction models associated with soil geology and environmental climate are constructed based on a single variable principle, a heat supply amount estimated value of the ground source heat pump subsystem is dynamically corrected, heat supply amount of a gas boiler subsystem and heat load required by a building group are estimated, heat supply output of the two subsystems is intelligently distributed, and heat supply efficiency is improved. And the heat supply amount correction model is optimized through the actual heat supply amount deviation. The method can effectively overcome the problem of ground source heat pump heat supply amount deviation caused by soil heat unbalance and climate fluctuation in a severe cold area, further improves the energy efficiency estimation precision of a heat supply system, facilitates optimization of a scheduling strategy, has dynamic compensation and model self-evolution capabilities, and can adapt to complex environmental changes.

The invention discloses an urban carbon emission peak prediction method, and particularly relates to the technical field of carbon emission prediction. A carbon emission data set with high time resolution is constructed, and first K influence factors with the highest contribution degree to a carbon emission peak value are screened based on an adaptive weight attribution mechanism, including extraction of heating system response time and energy scheduling hysteresis characteristics, construction of a carbon emission peak value prediction model, and when a prediction value exceeds a set threshold value, calculation of the carbon emission peak value prediction model is carried out. The method comprises the following steps: automatically adjusting the influence weight of a key factor, calculating a peak confidence interval on a test set, evaluating and optimizing the model performance, finally deploying an optimized model to an urban carbon emission monitoring system, and dynamically updating in combination with a real-time data stream to ensure the accuracy and stability of prediction; according to the method, the accuracy of urban carbon emission peak prediction is remarkably improved, the capability of responding to sudden energy demand increase in extreme weather is enhanced, and scientific support is provided for carbon neutralization policies, energy regulation and control and environment management.

The invention discloses an intelligent electromagnetic induction heating system and method based on edge calculation, and aims to realize efficient and accurate heating control through advanced technical means. According to the system, a multi-source data real-time acquisition module is integrated, and various key parameters in the heating process can be comprehensively obtained. Through the data preprocessing and feature extraction technology, the system can accurately identify the heating state, and a reliable basis is provided for subsequent control. The edge computing node serves as the core of the system, has strong real-time analysis capability, can quickly respond to changes in the heating process, and dynamically adjusts a control strategy. In addition, through an edge-cloud collaborative optimization technology, cloud resources are fully utilized, and the intelligent level of the system is further improved. The problems of feedback lag, high cloud dependence and the like in the prior art are effectively solved, the heating uniformity, the energy efficiency ratio and the abnormity recovery capability are remarkably improved, and a reliable solution is provided for the fields of industrial heating, household induction cookers and the like.

The invention provides a horizontal sliding bearing test bed, which belongs to the field of sliding bearing safety operation and detection and comprises a main transmission system, a driving system, a radial loading system, a heating system and a hydraulic system. A sliding bearing seat in the radial loading system is inversely fixed on a lower loading plate and is detachably sleeved and fixed on a test bearing, and a test bed applies pressure to the lower loading plate through a hydraulic cylinder to apply radial load to a test sliding bearing. A rotary swing hydraulic oil cylinder in the main transmission system provides power to test the horizontal sliding bearing. The hydraulic system is connected with the rotary swing hydraulic oil cylinder and the radial loading oil cylinder and can achieve closed-loop control over torque and radial loading force. The heating system simulates a high-temperature working condition through a heating box, and a test bearing can be set to be tested at a specific high temperature. According to the invention, large load detection can be realized, working condition simulation is accurate, data such as bearing working torque and service life can be detected, and comprehensive and multi-size adaptability is realized.

The intelligent control method comprises the steps that a system architecture of the multi-heat-source coupling heat supply system is constructed, and the system architecture comprises a composite heat source module, an intelligent energy storage unit and an edge computing and internet-of-things platform; constructing a heat source energy efficiency dynamic evaluation matrix based on meteorological data and system monitoring data; constructing a multi-objective optimization model of the system, and solving the multi-objective optimization model in combination with the heat source energy efficiency dynamic evaluation matrix and an optimization algorithm to obtain an optimal solution; and dynamically distributing the output of each heat source according to the optimal solution. Controlling the system to operate in combination with multiple operation modes according to the external environment parameters and the actual operation parameters of the system; continuously optimizing related parameters of the system by adopting a reinforcement learning algorithm according to historical operation data and a reward function of the system; according to the scheme, the energy efficiency, flexibility, reliability and adaptability of the heat supply system are improved.

The invention discloses a planning design and optimized operation method for a heat supply system introducing a long-distance heat source, which comprises the following steps: acquiring basic data of the long-distance heat source to be introduced into the heat supply system in a heating season, and analyzing the operation mode change and dynamic characteristic change of a heat supply network of the existing heat supply system after the long-distance heat source is introduced; performing quantitative analysis and comparison on the plurality of heat supply network planning and design schemes in combination with external heat supply industry evolution data in a heat supply season to obtain an optimal heat supply network planning and design scheme; spatial characteristic data and time characteristic data related to the heat supply system are obtained, a space-time model of the heat supply system is established, and heat load prediction of each heat supply network area is carried out in combination with the heat load multi-dimensional influence data; and according to the heat load prediction value of each heat supply network area, combining the multi-dimensional heat supply loss, the heat source output characteristic and the heat supply network heat transmission and distribution characteristic of the long-distance heat source and the traditional heat source, and considering a heat source optimization operation target and a heat supply network regulation and control target, establishing a multi-time scale optimization operation model, and obtaining a corresponding optimization operation strategy.

The invention relates to the technical field of oil nozzles, in particular to an efficient atomization injection system for a direct injection oil injector in a cylinder. According to the technical scheme, an injection execution unit adopts a laser-cladded asymmetric injection hole array, fuel oil is evenly distributed in a combustion chamber through aperture gradient distribution and logarithmic spiral arrangement, and the oil-gas mixing effect is enhanced. The dynamic pressure adjusting unit is integrated with an MEMS film sensor array and a fuzzy neural network controller, a sensor can accurately monitor fuel oil pressure in real time, and the controller is based on a 7-12-3 type BP network and can rapidly and accurately adjust fuel injection pressure according to the working condition of an engine through mass data training. The fuel oil pretreatment unit comprises an ultrasonic cavitation device and a graded heating system, the ultrasonic cavitation device reduces the viscosity of fuel oil, and the graded heating system accurately controls the temperature by means of a PTC heater, a semiconductor laser and a vortex generator. The three units work cooperatively, the fuel oil atomization and combustion efficiency is greatly improved, pollutant emission is reduced, and the engine performance is remarkably improved.

Examples of the present disclosure relate to systems and methods for providing hot water for a hot water heater and heating capacity for a climate control system by utilizing a Stirling heat pump system. The Stirling heat pump may be partially integrated into an indoor unit of the climate control system and a hot water heater. An additional indoor heat exchanger may receive heating fluid from the Stirling heat pump system to provide heating capacity in addition to another indoor heat exchanger of the climate control system. Further, the heating fluid circuit of the Stirling heat pump system may be partially integrated into a hot water tank for heating water. A controller may direct the flow of heating fluid from the Stirling heat pump to the hot water tank and / or the climate control system based on a request for heating.

The invention discloses a multi-mode heat supply energy consumption evaluation method and system, and relates to the technical field of heat supply energy consumption evaluation and dynamic thermal scheduling control, and the method comprises the steps: arranging a multi-source sensor in a high-temperature flue gas path to collect thermal parameters, and building a time sequence mapping model between a unit load and flue gas thermal characteristics; and according to the thermal power data output by the time sequence mapping model and the heat exchange characteristics of all the heat supply modes, a heat energy availability map of the target time period is generated, and heat demand adaptation data is constructed. And selecting a matching path according to energy consumption and switching constraints based on the heat energy availability map and the heat demand adaptive data, and executing mode dynamic switching control. According to the method, a complete technical closed loop from data perception to state evaluation to action execution is formed. In the multi-mode heat supply system, system fusion of three-layer logic of heat load prediction, map expression and dynamic scheduling is realized, and the intelligence, self-adaption and energy efficiency optimization capabilities of the heat supply process are remarkably improved.