24270 results about "Refrigeration" patented technology

A hand-held refrigeration fan is provided, including a fan head and a hand-held portion. The fan head includes a fan housing, an air duct is formed in the fan housing, and the fan housing is provided with a connecting through hole penetrating through inner and outer sides of the air duct. A conductive member is arranged on an outer surface of the hand-held portion, a refrigeration apparatus is arranged in the hand-held portion, a refrigeration end of the refrigeration apparatus is connected to the conductive member in a heat conduction manner, and a heat dissipation end of the refrigeration apparatus communicates with the air duct after penetrating through the connecting through hole. A conductive sheet is arranged on an outer surface of a hand-held portion, and a refrigeration effect is achieved by the conductive sheet through the operation of a refrigeration apparatus.

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

Disclosed is a screw compressor (100), comprising a screw compressor housing (101), a discharge cavity (113), at least one silencing channel, and at least one adjustment piston, wherein the discharge cavity (113) is defined by at least one part of the screw compressor housing (101); the at least one part of the screw compressor housing (101) defining the discharge cavity (113) forms a wall of the discharge cavity (113); at least one hole is provided in the wall of the discharge cavity (113); the at least one adjustment piston can be inserted into the at least one hole and move therein; the at least one silencing channel is formed by the at least one hole and the at least one adjustment piston, and the at least one silencing channel is in fluid communication with the discharge cavity (113); and the position of the at least one adjustment piston in the at least one hole determines the silencing length of the at least one silencing channel.

The invention relates to the technical field of automatic control, in particular to an efficient magnetic suspension water chilling unit control system which comprises a refrigerant flow monitoring module, a dynamic flow adjusting module, a start-stop delay judging module, a temperature gradient sensing module and a frequency self-adaptive adjusting module. According to the method, the temperature difference curve is established through real-time monitoring data, the refrigerant flow synchronism is analyzed, temperature difference abnormity can be accurately recognized, the refrigerant adjusting requirement is extracted, traditional threshold value judgment lag is avoided, the refrigerant flow is controlled in real time, fluctuation impact is buffered, and the adjusting precision is improved by dynamically adjusting the opening degree of the electronic expansion valve and the rotating speed of the circulating pump; the method has the advantages that starting and stopping conditions are accurately judged, misoperation is reduced, early positioning of thermal imbalance is achieved through temperature difference sensing of an evaporator, a compression cavity and a condenser and establishment of an internal temperature difference distribution diagram, energy efficiency reduction is avoided, the frequency of a frequency converter is adjusted according to temperature difference distribution, the optimal load is matched, frequency adaptivity and system load response are improved, and refrigeration cycle energy efficiency is optimized.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses a control method and device for a fusion scheduling efficient refrigerating machine room system and a storage medium, and relates to the technical field of central air conditioner control. The method comprises the steps that operation data of all edge end devices and sensor data in all areas are obtained; calculating the energy efficiency index of each subsystem based on the operation data, the distribution weight of the subsystem corresponding to each equipment type, the comfort coefficient associated with each region and the sensor data in each region; according to the energy efficiency index, a control strategy of each subsystem is formulated in combination with the cooling capacity prediction data associated with each region; and performing reverse decomposition on the control strategy to obtain control parameters of the edge end equipment in each region, and issuing the control parameters to the edge end equipment in the corresponding region. The technical defect that when the cold source machine room is dispatched, the refrigeration effect of the central air conditioner is poor is overcome, and the overall energy utilization rate and the refrigeration effect are improved.

The shutdown of a compressor (10) installed in a refrigerant circuit (2) in air conditioning or refrigeration system is controlled so as to prevent unpowered reverse rotation of the compressor. Prior to terminating electric power to the compressor drive motor (24), the pressure within the system is relieved and substantially equalized across the compressor, thereby eliminating the possibility of unpowered reverse rotation of the compressor at shutdown. Pressure relief and equalization may be achieved by reducing the operating speed of the compressor to a low forward speed for a period of time prior to deenergizing the compressor drive motor. Pressure equalization may also be achieved by driving the compressor in reverse rotation prior to deenergizing the drive motor.

A refrigeration fan with lateral heat dissipation is provided, including a fan head and a hand-held handle. A conductive sheet is laid on an outer surface of the hand-held handle, a cavity is arranged in the hand-held handle, and a cold-heat generation apparatus is mounted in the cavity. The hand-held handle is provided with an air inlet and an air outlet, a refrigeration end of the cold-heat generation apparatus is connected to the conductive sheet in a heat conduction manner, and a heat dissipation end of the cold-heat generation apparatus communicates with the air outlet.

The invention provides a magnetic suspension centrifugal water chilling unit, and belongs to the field of refrigeration equipment. The magnetic suspension centrifugal water chilling unit comprises a magnetic suspension compressor, a condenser and an evaporator, the magnetic suspension compressor is connected with the condenser through an exhaust pipeline, and the condenser is connected with the magnetic suspension compressor and the evaporator through a liquid supply pipeline assembly; the system further comprises a natural process control module, the natural process control module is used for dynamically adjusting the starting sequence of a plurality of magnetic suspension compressors in the water chilling unit by determining the energy efficiency health index of the water chilling unit and calculating the energy efficiency health index, and the natural process control module is electrically connected with the central controller. And a natural process control module is arranged, a multi-dimensional energy efficiency health index model is constructed, and the starting sequence of the compressors is dynamically adjusted by integrating the actual energy efficiency ratio, the evaporation efficiency, the compressor aging coefficient, the pressure fluctuation and the environment humidity correction term.

The invention discloses an energy-saving control method and system based on a large refrigeration house, and the method comprises the steps: S1, obtaining cargo attribute information in real time through a cargo label, and inputting a dynamic load prediction model to generate a cooling capacity demand prediction signal in a future time period; s2, generating a multi-device cooperative control signal based on the cooling capacity demand prediction signal in the future period; s3, a shelf-level cooling capacity demand distribution signal is generated in combination with the cooling capacity demand prediction signal; s4, generating a directional cold airflow path signal matched with goods shelf distribution according to the goods shelf level cold capacity demand distribution signal; and S5, closed-loop feedback adjustment is conducted on the compressor frequency, the refrigerant flow and the air valve opening through an edge calculation module, and a dynamic balance control instruction of cooling capacity supply and space distribution is generated. The intelligent air quality monitoring method and system based on sensing data feedback can solve the problems of excessive refrigeration energy consumption waste caused by inaccurate cold capacity demand prediction of a large refrigeration house and extra energy loss caused by low cooperative efficiency of multiple devices.

The invention discloses a refrigerator refrigerating capacity adjusting method which comprises the following steps that S1, environment data are collected in real time through a plurality of sensors, and future environment changes are accurately predicted through a machine learning algorithm; s2, constructing an environment change prediction model by using a machine learning algorithm; s3, the refrigerator intelligent refrigeration adjusting system analyzes and models use habits of the user, and predicts future refrigeration requirements; s4, according to the prediction data obtained in the step S1 and the step S3, the refrigerant flow and the working mode of the compressor are dynamically adjusted; s5, the refrigerator intelligent refrigeration adjusting system dynamically adjusts a refrigeration strategy according to comprehensive analysis of the real-time temperature data and the prediction result, and the operation mode of the refrigerator is continuously optimized; and S6, regularly storing the use data and the environment data of the user in a local or cloud storage. According to the method, future refrigeration requirements can be accurately predicted, the refrigeration strategy is dynamically adjusted, and the energy efficiency is improved to the maximum extent.

The invention relates to the technical field of communication machine room energy conservation, in particular to a temperature energy-saving control method and system for a communication machine room, and the method comprises the steps: obtaining multi-region temperature and humidity and equipment load data in the machine room, inputting a pre-training model to obtain a cabinet heat dissipation risk level, judging an environment state through combination with an incidence matrix, entering an operation mode if a requirement is satisfied, and entering a control mode if a requirement is satisfied. If not, a local temperature control adjusting mechanism is triggered; the method comprises the following steps: calculating a refrigeration efficiency ratio according to the air volume of ventilation equipment, air conditioner power consumption, real-time electricity price and the like, generating a candidate scheme set by using a dynamic weight multi-target optimization algorithm in combination with factors such as equipment life, and selecting a target scheme through virtual verification; and according to air conditioner waste heat and cabinet heat dissipation and storage heat, a heat energy distribution strategy is dynamically generated by using a deep Q network reinforcement learning algorithm. Therefore, the problems that in the prior art, energy is wasted, cost is high, heat dissipation requirements of all areas are difficult to accurately adapt, the service life and stability of equipment are affected, and fault risks are increased are solved.

The invention relates to the field of temperature monitoring, and discloses a high-voltage passive induction temperature monitoring system, which comprises a plurality of temperature sensors, the plurality of temperature sensors are arranged in different cabinets and channels in a machine room; a control unit; the control unit is used for sending a detection instruction to each temperature sensor and receiving a feedback temperature signal; the control unit generates a temperature change curve of each monitoring point according to the received temperature signals; the control unit constructs a three-dimensional temperature distribution model of the whole machine room based on the temperature change curve of each monitoring point, identifies a potential hot spot area based on the three-dimensional temperature distribution model and predicts the temperature change trend of the potential hot spot area; a refrigeration unit; the refrigeration unit is used for adjusting the temperature of the located area; the refrigeration unit is communicated with the control unit; wherein the control unit adjusts the power of each refrigeration unit based on the identified potential hot spot areas.

The invention relates to a food fresh-keeping method, device and equipment and a storage medium, and the method comprises the steps: collecting respiration intensity data and decay gas concentration of target food in a cold chain carriage, carrying out fresh-keeping threshold matching, and generating food fresh-keeping information; real-time environment parameters of the cold chain compartment are collected, a fresh-keeping instruction is generated according to the food fresh-keeping information and the real-time environment parameters, and an air adjusting valve opening instruction and a compressor frequency instruction are obtained; dynamic regulation and control operation is executed based on the air regulating valve opening instruction and the compressor frequency instruction, and putrefying bacterium comprehensive evaluation indexes and compressor power consumption data are obtained in real time; and according to the putrefying bacterium comprehensive evaluation index and the compressor power consumption data, instruction feedback analysis is carried out, strategy construction is carried out, and a fresh-keeping regulation and control strategy is output. The air conditioning and refrigeration strategy can be dynamically adjusted according to the breathing characteristics of different foods and the growth dynamic state of putrefying bacteria.

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 waste heat recovery scheduling method and system for a two-phase cold plate of a data center, and relates to the technical field of refrigeration and heat management. The waste heat recovery scheduling method and system for the two-phase cold plate of the data center comprises the following steps that S1, key thermal parameters and tail end state variables are collected through multiple types of thermal monitoring devices, and thermal state monitoring data are obtained and preprocessed; s2, on the basis of the thermal state monitoring data and in combination with an SLA configuration file, performing priority division on the heat utilization terminals, and constructing a scheduling deviation judgment mechanism to identify thermal state changes; s3, under driving of the regulation and control instruction, accessing multi-source waste heat and distributing the multi-source waste heat to each terminal scene, and evaluating a matching condition of the waste heat and a terminal demand; and S4, based on the change of the heat distribution state and the thermal state monitoring data, identifying an overload risk in combination with a multi-parameter linkage mechanism. The problems that waste heat recovery is insufficient and the energy utilization rate is limited under the light-load and multi-heat-source cooperative working condition are solved.

An intelligent control method of an evaporative cooling air conditioning system based on deep reinforcement learning belongs to the technical field of refrigeration equipment regulation and control, and comprises the following steps: acquiring a space element of a refrigeration area, and converting the space element into a digital model, so as to generate a functional block division strategy of the refrigeration area based on the digital model. Based on a functional block division strategy, acquiring sensor data on each functional block fusion node, and preprocessing the sensor data to obtain a multi-dimensional feature data set; and fusing the multi-dimensional feature data set into a performance index to output a performance evaluation vector of each functional block. And based on the performance evaluation vector, constructing an experience playback pool fusion global state vector so as to carry out offline training on a DDPG algorithm and obtain an intelligent control strategy network. And the real-time performance index of the refrigeration area is input into an intelligent control strategy network to be processed, and a control instruction of the evaporative cooling air conditioning system is output. And controlling the valve opening and the fan frequency of each functional block in response to the control instruction.

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 relates to the technical field of fault diagnosis, in particular to a fault self-diagnosis system and method for a high and low temperature aging test chamber of a storage chip and computer equipment. The temperature control abnormal index is obtained through the data of the temperature control system sensor, early warning can be performed when the temperature control system has abnormal fluctuation, the performance evaluation basis of the air circulation system is provided by the air system efficiency coefficient, the heating efficiency and the refrigeration coefficient are obtained through the data of the heating and refrigerating system sensor, and the performance of the heating and refrigerating system is evaluated. An electrical stability index is obtained through data of a test system sensor, the operation stability of an electrical system is evaluated, the stability of the electrical system is detected in time, and a fault probability index is obtained through a system entropy increase rate, the electrical stability index, a thermal coupling factor, a wind system efficiency coefficient and a temperature control abnormal index. Therefore, the stability and the reliability of the equipment can be improved, the maintenance cost can be reduced, the use efficiency of the equipment is improved, and the accuracy and the safety of test results are ensured.

The invention discloses a dough humidity and temperature dynamic regulation and control system based on multi-modal sensor fusion, and relates to the technical field of operation parameter regulation and control, and the method comprises the steps: carrying out the non-blind area monitoring of a three-dimensional temperature and humidity field in a fermentation box, and eliminating the local failure risk of a point-type sensor; capturing a space-time coupling relationship of temperature and humidity-carbon dioxide concentration-dough volume through a self-attention mechanism; constructing a differential equation set comprising at least one state variable; establishing a fermentation equipment thermodynamic model, optimizing refrigeration and heating time sequences through reinforcement learning, and designing an energy consumption optimization strategy based on digital twinning; performing cross-device cooperative control; constructing a hierarchical diagnosis tree containing fault features; performing microbial activity enhanced humidity regulation and control, and forming a protective layer on the surface of the dough based on a humidity gradient control algorithm of a water molecule diffusion coefficient. And by arranging the environment regulation and control module, the time compression module and the time sequence optimization module, the product quality, the production efficiency and the energy utilization rate are remarkably improved.

The invention relates to an automatic identification control method and system based on heat dissipation of a liquid cooling plate in a heat exchange system. The method comprises the following steps: acquiring real-time operation data of a target liquid cooling plate; establishing a dynamic thermal resistance network model, and calculating a current thermal resistance value; refrigeration demand parameters are obtained, and a demand weighted thermal resistance value is generated in combination with the current thermal resistance value; thermal resistance deviation is obtained based on the demand weighted thermal resistance value, and the opening degree of a cooling liquid flow valve in the heat exchange system is dynamically adjusted through a fuzzy PID controller; the PID parameters are updated through a preset optimization algorithm, and the fuzzy PID controller is optimized; according to the automatic identification control method and system based on heat dissipation of the liquid cooling plate in the heat exchange system, real-time dynamic adjustment of the flow of the cooling liquid is achieved by establishing the dynamic thermal resistance network model and combining fuzzy PID control, the problems that a traditional method lags in response and is insufficient in control precision are solved, and the control efficiency is improved. The method has the effects of improving the dynamic response speed, improving the control precision and optimizing the energy efficiency.

The invention provides a control method of a refrigeration house, a controller, an air conditioner and the refrigeration house. Under the condition that the target goods are not put in storage or taken out of storage, the first information is received, the quality information and the current temperature information of the target goods are obtained in advance, the first target thermal load data are calculated in combination with the environment parameters (such as the temperature parameters) of the current cold storage, and therefore the first target thermal load data are obtained before the goods are put in storage or taken out of storage. And dynamically adjusting environmental parameters (such as temperature parameters) of the refrigeration house according to a calculation result. The method effectively solves the problem that the temperature control of the refrigeration house, especially the refrigeration house with frequent goods input and output, is not accurate enough in the prior art. By adjusting the operation state of the environment adjusting equipment in advance, for example, the compressor frequency, the fan rotating speed and the control valve opening degree of the refrigeration equipment are optimized, the temperature of the refrigeration house can be accurately reduced before goods are put in storage, or the temperature of the refrigeration house can be properly increased before goods are taken out of storage, and therefore the problem of temperature adjustment lag is avoided.

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

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

The invention discloses an 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.

The invention relates to the technical field of automobile cup holder temperature control methods, and discloses an automobile cup holder temperature regulation control method based on a microcontroller. According to the method, real-time temperature data and environment parameters of a target container are collected, and an initial temperature parameter set containing a container surface temperature distribution curve and an environment heat exchange coefficient sequence is generated; then, establishing a first adjusting model containing a mapping relation between surface temperature change and refrigeration power according to the container surface temperature distribution curve, and establishing a second adjusting model containing environment heat interference and a heating power compensation rule according to the environment heat exchange coefficient sequence; during cup stand temperature control, based on real-time temperature difference fluctuation and container material heat conduction characteristic data, a target model is activated to generate a dynamic power instruction, finally, the instruction is input into a semiconductor temperature control unit of the cup stand, and the current gradient of a refrigeration sheet or the voltage distribution of a heating film is adjusted. The temperature of the automobile cup holder can be accurately and intelligently adjusted, and the user experience and the comfort level of an automobile cabin are improved.

The invention discloses a battery prefabricated cabin intelligent refrigeration control method and system based on data driving, and relates to the technical field of battery energy storage system thermal management, and the method comprises the steps: collecting the operation multi-source data of a battery prefabricated cabin, and constructing the current thermal environment state expression; constructing a battery cabin short-term internal heat change trend prediction model based on the operation multi-source data of the battery prefabricated cabin; and according to the current thermal environment state expression and the thermal trend prediction result of the battery compartment short-term internal thermal change trend prediction model, an optimal control strategy for an air cooling, liquid cooling or composite refrigeration mode is formulated. According to the method, a closed-loop thermal management system driven by data is formed by constructing a thermal state expression module, a thermal trend prediction module and an intelligent refrigeration decision module, accurate recognition and active regulation and control of the thermal risk of the battery prefabrication cabin are achieved, the temperature control precision and the response efficiency are improved, energy consumption and over-control risks are effectively reduced, and the method is suitable for popularization and application. And the system has the advantages of remarkable engineering practical value and system intellectualization.

The invention discloses a low-temperature minimal quantity lubrication cooling method in aluminum alloy precision machining, and relates to the field of aluminum alloy precision machining. Machining parameters such as the cutting speed and the feeding amount are determined according to the aluminum alloy material, the machining requirement and the tool performance, and finite element simulation optimization is carried out; high-purity vegetable oil-based lubricating oil is selected, a nano anti-wear agent and an antioxidant are added, and cooling liquid is prepared by mixing compressed air through vortex tube refrigeration; spraying to a processing area through a special multi-channel nozzle, and dynamically adjusting the flow; multiple sensors are used for monitoring the temperature, the tool abrasion and the surface quality, and adjustment is conducted when threshold values are exceeded; and the cooling liquid is recycled after being subjected to centrifugation and membrane filtration treatment, and the metal filing is classified, recycled and reused. The cooling and lubricating effects are good, cutting heat and tool abrasion are reduced, the machining precision is improved, and the tool service life is prolonged; environment-friendly lubricating oil is recycled, so that pollution and cost are reduced; real-time monitoring and adjustment guarantee stable machining quality, machining efficiency is improved, and high-end manufacturing requirements are met.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention relates to an intelligent energy-saving method for a large building refrigerating system based on a large language model, and belongs to the field of intelligence of large building refrigerating systems. A reinforcement learning framework is constructed, a multi-dimensional state space formed by building thermal loads, equipment operation states and thermal environment parameters is defined, and a dynamic reward mechanism is designed by taking improvement of energy efficiency, guarantee of thermal safety constraints and maintenance of thermal comfort as optimization objectives; developing a word embedding model special for the refrigeration field and a cross-modal decision conversion model, and realizing bidirectional analysis of a natural language instruction and a physical control parameter through feature coding and embedding mapping; and performing low-rank adaptive fine tuning on the pre-trained large language model based on the reinforcement learning experience set, and constructing a closed-loop verification system. Generalized migration and multi-target collaborative decision-making of equipment control strategies are achieved, the self-adaptive regulation and control capacity of a refrigeration system under dynamic loads is improved, and a universal intelligent solution for energy efficiency optimization, thermal safety and comfort is provided for a large building.