3067 results about "Temperature.ambient" patented technology

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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of intelligent temperature control, and discloses an intelligent temperature control system and a control method thereof. The temperature sensing module is used for collecting real-time data of the environment of the target area, including temperature, humidity, air flow rate and external environment temperature; the data processing module is used for receiving, processing and analyzing the collected real-time data; and the temperature prediction module is used for receiving the processed and analyzed real-time data and predicting a future temperature change trend based on historical data. By adopting the technical scheme of combining the temperature prediction module and the data processing module, the temperature change trend of the target area is accurately predicted by analyzing historical data and real-time data. Therefore, the system can judge temperature fluctuation in advance and make response adjustment, and compared with a mode that a traditional temperature control system can only adjust according to a set value, the method can more flexibly cope with environmental changes, and the accuracy and stability of temperature control are remarkably improved.

The invention relates to the technical field of air conditioner energy-saving control, in particular to an evaporation cooling air conditioner energy-saving optimization control method based on deep reinforcement learning, and the method comprises the steps: calculating a heat load rising index according to the change trend and change amplitude of the environment temperature in the local time period of each moment in the operation process of an evaporation cooling air conditioner; obtaining an energy consumption peak value synchronization index according to the similarity degree of peak value fluctuation in the power of the fan, the water pump and the evaporator in the local time period of each moment; calculating the environmental complexity according to the difference between the thermal load rising index and the energy consumption peak value synchronization index at each moment; setting a reward function in a deep reinforcement learning algorithm by integrating the environment temperature and the environment complexity at each moment; and according to the environment temperature and the power of the water pump and the evaporator at the current moment, the deep reinforcement learning algorithm is used for conducting optimization control on the fan power of the evaporative cooling air conditioner. According to the method, the precision of air conditioner energy-saving control by using a deep reinforcement learning algorithm can be improved.

The invention discloses a fan control method and device, a product, a server and a medium, and relates to the field of computer hardware control. Under the condition that the temperature value of the server component is larger than or equal to the new current temperature threshold value, the target rotating speed value of the fan is output through the proportional-integral-differential control model, then the fan is controlled to rotate according to the target rotating speed value, and cooling of the server component through the fan is achieved; secondly, when the temperature threshold value is determined, the historical temperature values of the components under the multiple environment temperatures are considered, and the temperature threshold value is updated, that is, the fan is controlled by depending on the dynamic threshold value. The threshold value is adjusted in advance according to the environment temperature, so that frequent triggering of starting of the fan due to transient fluctuation of the environment temperature is avoided, and the stability of the server is ensured; and thirdly, based on an environment-load combined compensation mechanism, the rotating speed of the fan is dynamically adjusted to offset the influence of the environment temperature, the influence of the environment temperature and load factors on fan control is avoided as much as possible, and the fan control precision is improved.

The invention discloses a temperature compensation method and system, and the method comprises the steps: receiving a valve block temperature signal collected by a temperature sensor integrated with a pressure sensor in a closed gas supply unit, and an electronic element temperature and environment temperature signal collected by a temperature sensor on a control unit PCB, generating an initial temperature-working condition data set by combining the power of the air pump and the working duration of the electromagnetic valve; performing thermal resistance network modeling on the initial temperature-working condition data set, constructing a multi-node thermal characteristic matrix, and decomposing the matrix into a plurality of sub-matrixes; performing real-time operation on the sub-matrix by using a dynamic heat balance algorithm to generate a temperature estimation result of the key component; and generating a temperature compensation control instruction according to the temperature estimation result of the key component. According to the embodiment of the invention, the temperature of the key component can be accurately estimated, dynamic compensation is realized, and the control precision and stability of the closed gas supply unit are improved.

The invention discloses an optimization control method and system for an ultra-large fan intelligent speed regulator, and relates to the technical field of fan intelligent control, and the method comprises the steps: obtaining the current operation environment parameters of a fan, including but not limited to a wind speed signal, a motor input current, a voltage, a power factor, a rotating speed, an environment temperature and atmospheric pressure; monitoring whether the wind speed fluctuation amplitude exceeds a set threshold or not in a continuous sampling period; judging whether the current system enters a critical hysteresis zone state or not, wherein the critical hysteresis zone state is a working condition when the wind speed data of the sensor does not have stability and fan output has current fluctuation abnormity and a differential pressure rapid change trend; when the system enters a critical hysteresis zone state, the collected wind speed signal is paused as a speed regulation basis, an air volume estimation model based on current, voltage, rotating speed, temperature, humidity and air pressure is constructed, and equivalent air volume is output as a pseudo feedback signal; the problem that the fan speed regulation accuracy and the system stability are reduced in the plateau low-pressure environment is solved.

The invention discloses an excavator heat dissipation control method and system based on environment temperature and operation working conditions. The excavator heat dissipation control method comprises the steps that S1, the operation working conditions of an excavator are divided into five working conditions including the super-heavy load working condition, the heavy load working condition, the medium load working condition, the light load working condition and the super-light load working condition; s2, the external environment temperature is divided into 10 gears; s3, according to the environment temperature and the operation working condition, the heat dissipation requirement is divided into 10 gears; s4, the rotating speed of the cooling fan is adjusted according to the cooling requirement gear, so that the cooling requirement is accurately matched with the rotating speed of the cooling fan; and S5, whether the temperature of the working medium in the excavator is larger than a high-temperature threshold value or not is judged, if yes, heat dissipation is conducted according to the corresponding heat dissipation fan rotating speed obtained through the heat balance test, and if not, heat dissipation is conducted according to the actual heat dissipation requirement gear standard. The problem of heat dissipation redundancy caused by single parameter control is solved, and dynamic matching of the rotating speed of the fan and the composite heat dissipation requirement is achieved. And the energy consumption under the low-load working condition and the cold environment is reduced, and the risk of insufficient heat dissipation under the high-temperature heavy-load working condition is avoided.

The invention discloses an automatic demisting control system for a demisting mirror, belongs to the technical field of smart home and automatic control, and aims to solve the problems that an existing demisting mirror is inconvenient to use and wastes energy due to the fact that the existing demisting mirror depends on manual operation. According to the system, the mirror surface temperature is detected in real time through the mirror surface temperature sensor, environment data are collected in combination with the environment temperature and humidity sensor, a dew point temperature calculation module and a dynamic threshold module are arranged in the control unit, and the current dew point temperature is calculated based on the environment temperature and the relative humidity; and dynamically adjusting a demisting trigger threshold value according to a difference value delta T between the mirror surface temperature and the dew point temperature. The heating wires are distributed on the back face of the mirror body at intervals, heating gears are selected according to the temperature difference change rate and the humidity change rate, and accurate temperature control is achieved. According to the system, through a dynamic threshold value and graded power adjusting technology, the rapid demisting and energy-saving requirements are considered, the system is suitable for automatic mirror surface demisting in humid environments such as bathrooms and swimming pools, the user experience is improved, and the energy consumption is reduced.

The invention relates to the technical field of air conditioners, particularly provides an air conditioner control method and an air conditioner, and aims to solve the problem that in the prior art, when the indoor environment temperature of an air conditioner is close to the set temperature, the indoor environment temperature cannot be accurately controlled. In order to achieve the purpose, the control method of the air conditioner comprises the steps that an indoor environment temperature value is obtained at intervals of a set duration value; determining a first temperature difference value according to the operation mode of the air conditioner, the set temperature value and the indoor environment temperature value; according to the operation mode, the current indoor environment temperature value and the last indoor environment temperature value, a second temperature difference value is determined; and based on the first temperature difference value and the second temperature difference value, the numerical value of at least one of the compressor frequency, the outdoor fan rotating speed and the indoor fan rotating speed is adjusted. The first temperature difference value and the second temperature difference value are comprehensively considered, the frequency of the compressor, the rotating speed of the outdoor fan and the rotating speed of the indoor fan are adjusted, the temperature control accuracy is improved, meanwhile, repeated temperature change is avoided, and energy consumption is reduced.

The invention provides an intelligent data monitoring analysis method and system of a solar seawater desalination system. The method comprises the following steps: calculating condensation efficiency based on annular temperature gradient distribution data and phase change characteristic data according to the phase change characteristic data and environment temperature data of steam in a condensation pipe in a condensation process; the condensation efficiency and the environment temperature data are input into a pre-trained reinforcement learning parameter optimization model, and in combination with physical constraint conditions of the condensation structure, flow adjusting parameters and deflector deflection angle adjusting parameters of all subareas are generated; and the flow adjusting parameters and the deflector deflection angle adjusting parameters of all the partitions are converted into corresponding electric adjusting valve opening degree instructions, and the adjusting valve opening degrees of the cooling water circulation systems corresponding to the different partitions and the angle of the angle-adjustable deflector are adjusted. The cooling water flow and the deflection angle of the guide plate are optimized in real time through reinforcement learning, and efficient and energy-saving self-adaptive regulation and control of the condensation system are achieved through temperature and phase change data monitoring.

The invention relates to the technical field of box transformer substation control, and particularly discloses an intelligent charging box transformer substation overload protection threshold value dynamic calculation method and related equipment, and the method comprises the steps: continuously obtaining the load information, the in-box temperature information and the environment temperature information of an intelligent charging box transformer substation; identifying and acquiring a parameter set of the dynamic thermodynamic model to determine the dynamic thermodynamic model; the identified dynamic thermodynamic model is utilized to predict the temperature in the box at the next moment based on the load information and the environment temperature information; based on the predicted temperature in the box and a preset safe temperature threshold value, a temperature margin is obtained through calculation; adjusting an overload protection threshold according to the temperature margin; according to the method, the self-adaptive adjustment of the overload protection threshold is realized, so that the overload protection strategy can dynamically change according to the actual operation state of the box-type transformer substation, the safe operation of the box-type transformer substation under the working conditions of slow heat dissipation and load fluctuation is ensured, the overload capacity of the box-type transformer substation is fully utilized, and the continuity of the charging service is improved.

The invention relates to the technical field of wind driven generator monitoring, in particular to a fan fault early warning and diagnosis method fusing multi-modal time sequence data, which comprises the following steps: respectively acquiring vibration time sequence data, current time sequence data and temperature time sequence data of a fan, and respectively calculating a first feature sequence, a second feature sequence and a third feature sequence; generating a fault early warning signal based on a three-state polarity synchronous counting mechanism; calculating a dispersion statistical value, and executing chronic fault diagnosis; when the fan is in a low-load steady-state working condition, active lubrication state detection and feedback are executed; when the first feature sequence is calculated, synchronously executing adjoint oil product pollution diagnosis; and the environment temperature is judged through the temperature time sequence data, and low-temperature working condition adaptability compensation is executed under the low-temperature working condition. The main purpose of the invention is to solve the problems in the prior art that early fault signs cannot be effectively captured due to information loss during fusion processing of multi-modal data, and real faults and external interference are difficult to distinguish.

The invention relates to the technical field of energy storage module temperature control, in particular to an energy storage module multi-environment temperature automatic regulation and control unit and a regulation and control method thereof.The energy storage module multi-environment temperature automatic regulation and control unit comprises an external protection frame body installed on the outer side of an energy storage module, and an internal heat exchange flow channel used for adjusting the heating value of the energy storage module is arranged in the external protection frame body. According to the energy storage module multi-environment temperature automatic regulation and control unit and the regulation and control method thereof provided by the invention, by adopting the liftable cover body structure design, the heat exchange mode of the energy storage module can be changed according to the temperature change of the internal environment and the external environment, so that the regulation and control performance of the energy storage module at different environment temperatures is improved, and the applicability is improved; the flow direction of an internal heat exchange runner is changed through lifting adjustment of the upper lifting cover, so that heat loss is switched, and the energy consumption of the energy storage module is reduced; and by adopting an automatic regulation and control mode, the operation accuracy can be improved.