3693 results about "Temperature.ambient" patented technology

The invention relates to the field of temperature control, in particular to an automatic control method and system for a constant-temperature test room. The method comprises the following steps: acquiring real-time indoor operation monitoring parameters and external environment multi-dimensional data of a constant-temperature test room; performing indoor operation state analysis on the real-time indoor operation monitoring parameters, and constructing a laboratory dynamic state space model; carrying out multi-time-point environment temperature fluctuation calculation on the external environment multi-dimensional data, and carrying out nonlinear correlation learning to generate indoor state-environment nonlinear correlation characteristics; carrying out dynamic temperature evolution simulation on the laboratory dynamic state space model based on the indoor state-environment nonlinear correlation characteristics, and carrying out dynamic temperature evolution prediction to obtain temperature prediction values of a plurality of regions; constant-temperature deviation compensation calculation is carried out on the temperature prediction values of the multiple areas, then intelligent temperature control compensation is carried out, and an intelligent temperature control strategy is constructed; according to the invention, efficient and accurate constant-temperature automatic control is realized.

The invention relates to a temperature control intelligent optimization method and system for adhesive tape production. The method comprises the following steps that S1, production data in the drying process are collected through a multi-mode sensor; s2, constructing a state prediction model based on GNN and LSTM, inputting the collected production data into the prediction model, and dynamically predicting the state of each temperature control area; s3, modeling each temperature control area into an intelligent agent based on the predicted state between the temperature control areas, and realizing dynamic distribution of heat resources of each heating interval by training a cooperative control strategy between the intelligent agents; s4, a fuzzy control optimization scheme incorporating environment compensation: for short-term changes of environment temperature and humidity, dynamically adjusting temperature zone gradient distribution between drying sections by using fuzzy control, and optimizing energy efficiency; and S5, the temperature control module adjusts the temperature and the wind speed of the temperature control area in real time according to the optimization schemes obtained in the steps S3 and S4. The intelligent level of the temperature control system in the adhesive tape production drying process is comprehensively improved.

The embodiment of the invention relates to the technical field of data processing, in particular to an intelligent monitor temperature drift correction method and system based on a temperature compensation algorithm, and the method comprises the steps: collecting the environment temperature data of an environment where an intelligent monitor is located in real time, and obtaining a preset sensor aging factor in the intelligent monitor; based on the environment temperature data and a preset temperature-drift characteristic model, the temperature drift characteristic of the intelligent monitor is extracted, and a nonlinear compensation curve matched with the temperature drift characteristic is dynamically generated; performing iterative optimization processing on compensation parameters of the nonlinear compensation curve by adopting a self-adaptive compensation parameter optimization algorithm, and performing dynamic correction on the compensation parameters by fusing sensor aging factors in the iterative optimization processing process to obtain an optimized compensation parameter set; and performing real-time correction processing on original measurement data of the intelligent monitor according to the optimized compensation parameter set, and outputting a target measurement result after temperature drift noise suppression.

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 provides an anti-condensation temperature and humidity regulation and control system and method for remote deep learning prediction aided decision making, and the method is characterized in that a field controller is connected with a temperature and humidity collection device, obtains the environment temperature and humidity, calculates the dew point temperature, obtains the surface temperature of a protected part, and starts and stops a heating and / or dehumidification device by comparing the difference between the two values with a preset threshold value; the temperature and humidity acquisition device acquires field environment temperature and humidity data for the field controller to calculate the dew point temperature, and the temperature and humidity acquisition device is also used for acquiring the surface temperature of the protected part; the heating device is used for raising environment temperature; the dehumidification device is used for reducing the environment humidity; the communication module is used for data transmission between the field controller and the remote prediction terminal, sending field data and receiving prediction information; the remote prediction terminal obtains field data through the communication module; an anti-condensation system structure combining local autonomous control and remote prediction assistance is established, and redundancy fault-tolerant capability is achieved; even if the remote prediction function fails, the field control can still operate independently and reliably.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The invention discloses a refrigeration equipment control method and system for a communication machine room, and relates to the field of heat dissipation control, and the method comprises the steps: obtaining machine room indoor temperature and equipment heating data of the communication machine room, and determining a preset refrigeration index according to the machine room indoor temperature and the equipment heating data; the operation parameters of the air conditioning equipment are adjusted to refrigeration operation parameters; when the temperature difference value between the indoor temperature of the machine room and the outdoor environment temperature is higher than a preset starting threshold value, starting parameters of the natural cold energy exchange unit are determined; the natural cold energy exchange unit is controlled to operate according to the starting parameters, and operating parameters of the air conditioning equipment are adjusted to be optimized operating parameters; determining a temperature change trend curve of the outdoor environment temperature based on the weather forecast information, and determining an optimization adjustment node according to the temperature change trend curve; and when the adjusting nodes are optimized, the starting parameters of the natural cold energy exchange unit and the operation parameters of the air conditioning equipment are adjusted. By implementing the application, the energy use of the refrigeration equipment in the communication machine room can be saved.

The invention discloses a building outer wall leakage detection method and system, and belongs to the technical field of wall leakage detection. The method comprises the following steps: acquiring infrared thermal imaging, laser point cloud and multispectral imaging data, and generating a three-dimensional visual model with temperature information after environment temperature compensation correction, three-dimensional modeling and data fusion; and then extracting temperature characteristic parameters to identify an abnormal region, screening a to-be-verified region in combination with the building construction node coincidence degree and geometric parameters, and finally determining a leakage position through comparison of multispectral characteristic extraction and a preset leakage characteristic library. The system realizes full-process automatic detection by applying the method. According to the invention, temperature signal conduction limitation caused by insulation layer obstruction is broken through, the problem of misjudgment caused by a thermal bridge effect is solved through multi-source data fusion and spectral feature analysis, and the precision and reliability of leakage detection are improved.

The invention relates to the technical field of energy consumption management, and particularly discloses an AI-based clean room dynamic energy consumption management system and method. According to the method, a deep learning algorithm is introduced to carry out deep interaction analysis on clean room indoor air quality parameter time sequence data and environment change driving event data such as the number of indoor personnel, the external environment temperature, the room door opening and closing state and the equipment power, so that intelligent reasoning of the dynamic evolution process of the clean room indoor air quality is realized; according to the method, the air quality parameter prediction result is compared with the preset air quality standard range so as to predict the air quality change in the future short time under the current driving event, and then the short-time air quality parameter prediction result is compared with the preset air quality standard range so as to carry out feedforward adjustment on the HVAC system operation parameters of the clean room according to the difference between the short-time air quality parameter prediction result and the preset air quality standard range. According to the method, environmental changes can be actively predicted, smooth pre-adjustment is conducted in advance, the air quality in the clean room is kept within the preset standard range, meanwhile, unnecessary energy waste is remarkably reduced, and the environmental stability is improved.

The invention discloses a cold station multi-equipment combination energy efficiency optimization method and system, relates to the field of cold station energy efficiency optimization, and aims to solve the problems of low energy efficiency and response lag caused by manual adjustment. The method comprises the following steps: S1, acquiring key operation data (power, state, temperature, flow and frequency) of a water chilling unit, a cooling tower and a water pump and external environment data (temperature and humidity, wind speed and load prediction) in real time; s2, constructing an equipment energy efficiency model based on historical data, and quantifying the change of the equipment efficiency along with the load rate, the temperature difference and the environment temperature; s3, according to the current load and environment prediction, simulating and traversing the energy consumption of the equipment start-stop combination and the water pump frequency combination by applying a Monte Carlo algorithm, and screening a combination strategy with the lowest total energy consumption of the system; s4, converting the optimization strategy into a control instruction, and issuing the control instruction to equipment for execution; and S5, comparing the actual energy consumption with the predicted value, and if the deviation exceeds 5%, correcting model parameters by adopting a least square regression algorithm to realize closed-loop optimization. And the energy efficiency and the response speed of the cold station are obviously improved.

The invention is suitable for the technical field of automobile intelligent control, and provides an air conditioner seat collaborative cooling control method based on a neural network, which comprises the following steps of: constructing a human body-environment comfortable temperature original index model by collecting a large amount of human body and environment temperature distribution data in advance; on the basis, data acquisition and model comparison are carried out on the real-time human body temperature, the environment temperature and the temperature in a passenger compartment before the passenger gets on the bus, so that the heat comfort in the bus is pre-adjusted most reasonably before the passenger gets on the bus, and a method for cooperatively controlling ventilation and heating of an air conditioner and a seat is utilized; the two are combined to improve the thermal comfort of the passenger and the passenger compartment. And meanwhile, real-time sensing data is quickly processed when a passenger feels uncomfortable in temperature during riding and adjustment is carried out, the body temperature of the passenger and the ambient temperature of a passenger compartment are recorded and are input into a model for correction, so that the body comfort requirement of the passenger is met in a more personalized manner, and the comfort of the passenger in the vehicle running process is improved.

The invention relates to the technical field of gas steel cylinder pressure control, in particular to a gas steel cylinder multi-stage pressure control method based on a self-adaptive threshold value, which can dynamically correct the influence of ambient temperature on a pressure reference value through a constructed pressure-temperature compensation function, eliminate measurement errors caused by temperature drift, and improve the accuracy of pressure control. A pressure fluctuation entropy value is calculated in real time based on a sliding window algorithm, a self-adaptive threshold value adjustment coefficient alpha is generated in combination with historical working condition database matching, a control threshold value is dynamically adjusted along with gas flow velocity fluctuation and equipment aging degree, and the pressure over-limit risk is reduced; according to the method, fuzzy neural network control is introduced in a critical adjustment stage through a multi-stage pressure adjustment strategy, an improved radial basis function dynamic updating mechanism can adapt to a pressure sudden change mode online, the control response speed is increased, and the steady-state error is controlled within + / -1.5%.

The invention relates to the technical field of industrial equipment temperature control, and discloses a temperature control system, which comprises a multi-source state sensing module for acquiring a power load, temperature, humidity and feedback temperature; the feed-forward prediction and analysis module is used for generating a feed-forward signal based on the load and the temperature; the closed-loop feedback and safety guarantee module is used for calculating a feedback signal according to the temperature deviation; the cooperative control decision module combines the feed-forward signal and the feedback signal to generate a fan instruction, and generates a baffle instruction for balancing heat dissipation and regeneration according to the feed-forward signal and humidity; the self-adaptive heat dissipation execution module is used for executing a fan instruction to generate cooling airflow; and the dynamic energy regeneration module executes a baffle instruction to distribute the waste gas flow direction. According to the method, the power load is introduced to serve as a prospective index, correction is conducted in combination with the environment temperature, a predictive heat dissipation control model is constructed, the system can start heat dissipation adjustment before a large amount of heat is generated, and therefore smooth and stable control over the equipment temperature is achieved.

The invention relates to the technical field of air conditioner control, in particular to a self-adaptive energy-saving control method and system for an air conditioning unit, and the method comprises the following steps: monitoring indoor and outdoor environment temperature, humidity and illumination intensity in real time; identifying user behavior modes including rest, motion and leaving states; monitoring the operation states of the air conditioning unit in real time, wherein the operation states comprise compressor operation frequency, fan rotation speed and refrigerant pressure parameters; predicting an environment change trend and a user use demand in a period of time in the future; applying a multi-objective optimization method to generate an optimal control strategy comprehensively considering the energy-saving effect, the user comfort and the equipment stability; and according to the optimal control strategy, the operation parameters of the air conditioning unit are adjusted in real time, the environment change trend and the user use requirements in a period of time in the future can be accurately predicted, intelligent monitoring and prediction are achieved, and the energy-saving effect of the air conditioner is improved.

The invention belongs to the technical field of optimized operation of central air-conditioning systems, and particularly discloses an optimized operation method of an air source heat pump-fan coil central air-conditioning system, which comprises the following steps of: firstly, acquiring influence factors of building load and outdoor meteorological parameters; a central air conditioner load prediction model, an outdoor meteorological parameter prediction model and a data driving model of an air source heat pump and a water pump are constructed, the total energy consumption of system operation, the load demand at the next moment, the outdoor environment temperature and the outdoor relative humidity are obtained through prediction, and then an optimization objective function and constraint conditions are set; and solving the optimization objective function and the constraint condition by using a grid search optimization algorithm to obtain the set temperature of the host at the next moment and the set frequency of the water pump at the next moment, and completing the optimization process. According to the method, the problem of high hysteresis of the central air-conditioning water system is solved, and the host set temperature and the water pump set frequency at the next moment are optimized and solved according to the operation data at the current moment.

The invention provides an air source heat pump heating method and system, and relates to the technical field of heat pump system thermal control, and the method comprises the steps: collecting evaporator surface frost layer thickness, environment temperature and humidity parameters in real time, inputting the parameters to a pre-trained neural network algorithm model, and identifying a frosting rate by analyzing a multi-factor nonlinear relation; predicting a future frosting development trend, and outputting frosting thickness increase prediction and severity degree results; and according to the frosting thickness increase prediction and the severity degree result, reverse circulation defrosting is started in advance when the frost layer does not reach the critical thickness, and optimal control over the defrosting process is achieved by adjusting the opening degree of the four-way valve and the rotating speed of the draught fan, so that the system state after defrosting is obtained. The energy efficiency of the system is improved, and the heating comfort is optimized.

The invention relates to the technical field of motor temperature intelligent control, and particularly discloses a motor temperature monitoring and regulation method and system based on intelligent control and a storage medium, and the method comprises the steps: carrying out data collection circulation: judging whether a sampling period is reached, if yes, reading multi-mode sensing data, and outputting a calibrated temperature value T, and if not, outputting a calibrated temperature value T; if yes, vibration noise is eliminated, the environment temperature is compensated, multimode sensing data is added, and then the calibrated temperature value T is output; intelligent analysis is conducted on the calibrated temperature value T obtained through data collection circulation, if the temperature is normal, data analysis and prediction are conducted, and if the temperature is abnormal, the calibrated temperature value T is analyzed, fault self-diagnosis is conducted, abnormal reasons are judged, the system is controlled to conduct self-adjustment, and temperature early warning is conducted. The problems that in the prior art, monitoring is delayed, regulation and control are rigid, and maintenance is passive, so that energy efficiency is wasted, the failure rate is high, and operation and maintenance cost rises are solved; and a real-time temperature monitoring means and an intelligent control system are lacked.

The invention discloses a capacitive proximity sensor and a temperature drift compensation method of the capacitive proximity sensor, and the sensor comprises an oscillation detection module which comprises a sensor electrode and is used for outputting an oscillation signal according to the capacitance change of the sensor electrode and converting the oscillation signal into a first electric signal; the temperature detection module is used for detecting the environment temperature at the current moment and outputting a second electric signal; the digital potentiometer is connected with the oscillation detection module and is used for adjusting the first electric signal output by the oscillation detection module by adjusting the tap position; and the main control module is used for controlling the digital potentiometer to adjust the tap according to the second electric signal and outputting a detection result according to the first electric signal. The integrated temperature detection module is used for detecting the environment temperature, the tap position of the digital potentiometer is adjusted to achieve dynamic adjustment of the first electric signal, real-time compensation of the environment temperature change is achieved, and the detection precision and the environment adaptability of the sensor are remarkably improved.

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 present invention relates to the technical field of control cabinets, and provides an electrical control cabinet for a thermal power plant, comprising an electrical control cabinet, wherein the electrical control cabinet is provided with a plurality of ventilation openings, a heat absorption plate is fixedly mounted on an inner wall of the electrical control cabinet, a heat dissipation structure is mounted on the heat absorption plate, four connecting bases are symmetrically and fixedly mounted on a top side of the electrical control cabinet, a ventilation structure is mounted on the four connecting bases, two mounting frames are symmetrically and fixedly mounted on the top side of the electrical control cabinet, and an anti-snow structure is mounted on the two mounting frames. According to the present invention, heat dissipation is achieved by means of a combination of heat conduction and air cooling, thereby further improving the heat dissipation effect of the electrical control cabinet. When the present invention is used in winter, if the ambient temperature is low, a fan and an electric heating tube can be turned on to heat internal equipment within the electrical control cabinet, so as to prevent any impact on the operation of the equipment due to excessively low temperatures; when the present invention is used outdoors, a protective metal roof can be employed for snow protection. Furthermore, the present invention also includes a snow removal function.

The invention relates to the field of automatic control, in particular to a humidity control method and system for a switch cabinet, and the method comprises the steps: obtaining the temperature values and humidity values of a plurality of time points in the switch cabinet, and calculating the dew point temperature in the switch cabinet based on the temperature value and humidity value of a high-temperature region at the current moment. In consideration of unbalanced temperature distribution in the switch cabinet, whether condensate water is generated in the low-temperature area is mainly considered, and the temperature of the low-temperature area is mainly influenced by the heat transfer effect of the high-temperature area and the environment where the low-temperature area is located. Therefore, the temperature range of the low-temperature region is estimated on the basis of a pre-constructed temperature distribution model, the temperature value of the high-temperature region, and the ambient temperature. And determining whether the temperature of the low-temperature area is lower than the dew point temperature or not through trend analysis and dew point temperature comparison. And if the possibility exists, the water vapor condensation risk is speculated to exist. When water vapor condensation risks exist in the low-temperature area of the switch cabinet, the dehumidification module is controlled to dehumidify the interior of the switch cabinet, and water vapor condensation is avoided.

The invention belongs to the technical field of liquid cooling system control, and discloses a rotating speed prediction and control method for a charging gun liquid cooling system, and the method comprises the steps: obtaining the performance parameters of key parts, including a radiator, a water pump, a fan, a gun line and a liquid storage tank, in the liquid cooling system; inputting the performance parameters into the one-dimensional simulation model to obtain the highest temperature Tcoolant of the cooling liquid under different working conditions; on the basis of the Tcoolants under different working conditions, the relationship between the different working conditions and the Tcoolants is fitted, so that a rotating speed prediction model is obtained; the environment temperature and the gun line resistor heating power under the target working condition are input into the rotating speed prediction model, under the constraint met by Tcoolant, multiple sets of rotating speed combinations meeting the constraint are obtained through prediction, and the rotating speed combination enabling the total energy consumption of the liquid cooling system to be the lowest is selected from the multiple sets of rotating speed combinations to serve as the optimal rotating speed combination of the target working condition; and during charging, the fan rotating speed and the water pump rotating speed are regulated and controlled in real time based on the optimal rotating speed combination. The system has the advantages of advanced adjustment, quick response, energy conservation, high efficiency and the like.

The invention relates to the technical field of smart home, and discloses an AI-based fan intelligent temperature regulation control system, which comprises a multi-dimensional data acquisition module for acquiring environment temperature, humidity, air quality, illumination intensity and human body state data; the data fusion and preprocessing module is used for carrying out denoising, standardization and time synchronization on the multi-dimensional data and fusing the multi-source data; the AI decision and control module is used for generating a speed regulation and mode control instruction of the fan based on the human body comfort evaluation model and an AI algorithm; and the feedback optimization and execution module is used for executing the control instruction. Through a multi-dimensional data perception and fusion technology, comprehensive monitoring of environment temperature and humidity, air quality and a human body state is realized. The AI intelligent regulation and control algorithm is combined, the fan speed and mode are dynamically adjusted, and the user comfort and energy consumption are balanced; through edge calculation and cloud optimization, real-time response and continuous adaptive optimization of the system are ensured, and the accuracy and stability of the system are improved.

The invention relates to the technical field of smart home, and discloses an electric blanket system capable of adjusting calorific value by self-adaption to environment temperature, comprising a sleep sensing module used for collecting physiological signal data of a user; the state recognition module is used for recognizing the current sleep state of the user; the temperature target generation module is used for determining a corresponding target temperature value according to the current sleep state; the control calculation module is used for calculating a control instruction of each heating area; the multi-area heating execution module is used for performing heating control on the plurality of independent heating areas; and the temperature acquisition and feedback module is used for acquiring the real-time temperature of each heating area and feeding back the temperature to the control calculation module. Through linkage of the AI housekeeper and the external electronic bracelet, adaptive adjustment of the intelligent temperature control system is realized, a temperature control strategy is dynamically optimized according to real-time temperature data, user behaviors and environmental changes, limitation of a traditional static scheme is avoided, personalized temperature adjustment is provided, and flexibility and comfort are improved.

The invention discloses a heat-pump water heater temperature control method based on artificial intelligence, relates to the technical field of heat-pump water heater temperature control, and aims to solve the problem that the COP (coefficient of performance) is reduced due to a low-temperature environment, the exhaust temperature of a compressor and the opening degree of an expansion valve are optimized in real time through a GA (genetic algorithm), and operating parameters of a heat-pump water heater are dynamically adjusted by taking COP maximization as a target; the operation frequency of a compressor and the flow of a water pump are dynamically adjusted through fuzzy control logic in combination with a GA optimization result, the current water temperature and the temperature set by a user, the stability of the water supply temperature is guaranteed, an optimal control strategy is calculated through defuzzification, and the adaptability under different environment conditions is improved; an ANN is adopted to learn and predict a user behavior pattern, an LSTM network is utilized to analyze time sequences of historical water consumption data, target temperature and environment temperature, future water consumption peak and temperature requirements are predicted, a preheating function is started in advance, and insufficient hot water supply in the water consumption peak is avoided.

The invention discloses a device, a method and equipment for automatically setting a temperature threshold value of a cable joint, and belongs to the technical field of electric power facilities. The device comprises a cable parameter acquisition module used for acquiring working parameters and physical parameters of a cable; the environment parameter acquisition module is used for acquiring environment parameters of the cable; wherein the environment parameters comprise environment temperature and environment wind speed; the temperature monitoring threshold acquisition module is used for inputting the working parameters, the physical parameters and the environmental parameters into a pre-constructed temperature control model and generating a temperature monitoring threshold of the cable joint position according to an output result of the temperature control model; and the temperature monitoring threshold synchronization module is used for synchronizing the temperature monitoring threshold to the target temperature sensor. According to the technical scheme, intelligent monitoring of the temperature of the cable joint can be achieved through dynamic monitoring of the temperature of the cable joint, the accuracy of abnormal early warning is improved, and invalid inspection of workers caused by wrong information generated by wrong recognition is avoided.

The invention discloses a heat pump environment temperature self-adaptive regulation and control system oriented to peak-valley electricity price difference, and relates to the technical field of environment temperature regulation and control, and the system comprises a disturbance recognition module, a sampling scheduling module, a temperature correction module, a multi-source checking module, a regulation and control inhibition module and a self-optimization feedback module. And the change rate feature, the frequency spectrum mutation feature and the temperature measurement probe self-heating feature of the continuous external environment temperature sequence are extracted, a sampling risk index is generated, and a rapid oscillation mark is formed. Through adaptive sampling, probe self-heating compensation, multi-source consistency checking and feedback optimization mechanisms, dynamic response of the heat pump system to environment changes is achieved, data accuracy and control decision reliability are improved, and stability, energy saving performance and comfort control capacity under complex working conditions are remarkably enhanced.

The invention relates to the technical field of machine tool control, in particular to a machine tool thermal error compensation method and system based on data analysis. A machine tool thermal error compensation system based on data analysis comprises a data acquisition module, a machine tool thermal error analysis data construction module, a machine tool thermal error analysis module and a machine tool thermal error compensation module. The method comprises the following steps: acquiring a shaft temperature data set and vibration signal data of each shaft of the machine tool, current data at a servo motor of the machine tool, ambient temperature data around the machine tool and infrared images at the machine tool, and determining the running state of the machine tool under the assistance of multi-source data; weight distribution adjustment is carried out on the features related to the thermal error based on the operation state of the machine tool, the features strongly related to the thermal error under different working conditions are highlighted, the accuracy of thermal error analysis is improved, and then the thermal error compensation effect of the machine tool is improved.

The embodiment of the invention discloses a control method and device of an air conditioner, the air conditioner and a vehicle, and belongs to the technical field of air conditioning. The control method comprises the steps that the air inlet temperature and the air outlet temperature of an indoor unit are obtained, and the indoor environment temperature is obtained; determining a first temperature difference according to the absolute value of the difference between the air inlet temperature and the indoor environment temperature, and determining a second temperature difference according to the absolute value of the difference between the air outlet temperature and the indoor environment temperature; determining a temperature difference correction value according to the first temperature difference and the second temperature difference; and controlling compressor frequency according to the temperature difference correction value. According to the embodiment of the invention, the frequency of the compressor is accurately regulated and controlled under the condition that an outdoor environment temperature sensing bulb and a compressor exhaust temperature sensing bulb are omitted, the wiring space of the outdoor unit is saved, the cost is saved, and miniaturization design of the outdoor unit is facilitated.

The invention belongs to the technical field of visual intelligent monitoring, and particularly discloses a structure multi-measuring-point displacement visual perception system and method considering temperature self-compensation, and the method comprises the following steps: collecting a target structure multi-target image and environment temperature data; structure multi-point displacement extraction based on a multi-target detection algorithm; temperature-induced error compensation based on time sequence deep learning; and carrying out edge calculation and real-time monitoring and early warning. According to the method, high-precision real-time monitoring of multi-measuring-point displacement of the structure is achieved, the long-term monitoring reliability of visual perception is improved through the temperature self-compensation technology, and the method has great significance in improving the health monitoring level of the structure.