29223 results about "Temperature control" patented technology

The invention discloses an electrical cabinet condensation defense method and system based on environmental parameter monitoring. The method comprises the following steps: acquiring temperature and humidity information of multiple points in an electrical cabinet and outside the cabinet in real time; calculating a dew point temperature and a condensation risk index in the cabinet; predicting the lowest temperature and humidity change rate in the cabinet in the next time period in the control period; based on the minimum temperature and humidity change rate prediction value in the cabinet and the current condensation risk index, whether the electrical cabinet meets the condensation risk trend criterion is judged, if yes, the electrical cabinet enters a condensation risk suppression linkage control mode, and if not, the electrical cabinet enters a condensation risk defense self-adaptive mode; and in the condensation risk suppression linkage control process, when the current condensation risk index of the electrical cabinet is lower than a condensation risk index threshold value, entering a condensation risk defense self-adaptive mode, and otherwise, entering a next control period. Data acquisition is comprehensive and accurate, the dew point temperature can be accurately calculated, the control strategy is intelligent and flexible, equipment can operate according to needs, condensation is effectively prevented, and the energy-saving effect is remarkable.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention relates to the technical field of self-adaptive control, and discloses a self-adaptive temperature control method and system for an automobile heater, and the method comprises the steps: calculating the real-time temperature deviation and temperature change rate of an actuator according to multi-dimensional temperature data; thermal inertia dynamic analysis is carried out on the historical heating power and temperature response relation through a preset thermal inertia compensation function, and a thermal inertia index of the actuator is obtained; the real-time temperature deviation, the temperature change rate and the thermal inertia index are jointly input into a preset fuzzy PID controller, and a preliminary power adjustment instruction of an actuator is output; analyzing the temperature change trend of the actuator in the time period, and performing prospective correction on the initial power adjustment instruction based on the temperature change trend; and controlling an actuator to execute heating operation according to the temperature control instruction, collecting real-time feedback temperature after the heating operation is executed, and performing joint online correction on parameters of the thermal inertia compensation function and a prediction coefficient of the time sequence model according to the real-time feedback temperature. According to the invention, the accuracy of self-adaptive temperature control of the heater can be improved.

The invention discloses a temperature control system and method for a hot runner of an injection molding machine mold, and the method comprises the steps that the temperature control system collects the temperature data of each region of the hot runner in real time through a temperature sensor, and carries out the adjustment according to the data feedback; a built-in self-adaptive algorithm automatically adjusts a temperature control strategy according to different injection molding materials and production requirements, such as temperature upper limit, temperature lower limit and heating and cooling opportunities, and the production efficiency is optimized; when the system recognizes temperature fluctuation, a heating mode or a cooling mode is automatically switched, and the cooling system is dynamically adjusted for temperature balance; a plurality of temperature sensors are arranged in a key area of the mold for regional monitoring, and temperature control output is adjusted in real time; meanwhile, an embedded learning algorithm gradually optimizes a temperature control strategy according to historical production data, adjusts control parameters, and improves the production precision and efficiency. According to the temperature control system and the temperature control method for the mold hot runner of the injection molding machine, the problems of low temperature control precision, poor energy efficiency and slow response in the prior art are solved, and efficient and intelligent temperature control capability is provided.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention discloses a fuel cell hydrogen circulation cooling integrated system, and relates to the technical field of fuel cells, the fuel cell hydrogen circulation cooling integrated system comprises a hydrogen circulation cooling management center, the hydrogen circulation cooling management center is in communication connection with the following modules: a multi-source state sensing module, which is used for collecting multi-dimensional state data of a hydrogen circulation-cooling system in real time, extracting state characteristics, and sending the state characteristics to a control module; and forming a state feature sequence table. The multi-dimensional state data of the hydrogen circulation-cooling system is collected, the dynamic cooperative control strategy of the reinforcement learning decision module is combined, the precision and stability of temperature control are remarkably improved, the Markov decision process model is used for predicting the instantaneous heat production change caused by load sudden change, and the stability of the system is improved. And through combined adjustment of hydrogen circulation and a cooling system, temperature fluctuation is rapidly restrained, compared with traditional PID control, the temperature overshoot is reduced under the dynamic working condition, it is ensured that the galvanic pile works within the optimal temperature range all the time, the service life of the galvanic pile is prolonged, and the energy conversion efficiency is improved.

The invention discloses an equipment temperature adjusting method fusing a long short-term memory network, and particularly relates to the technical field of temperature control, which comprises the following steps: determining the deployment position of a sensor through thermal simulation, collecting multi-source heterogeneous data, dynamically adjusting the sampling frequency in combination with the temperature and the load current change rate, and adjusting the temperature of the sensor; after data preprocessing, an attention mechanism enhanced LSTM prediction model is constructed, a time sequence sample data set is divided according to equipment thermal response characteristics, training is carried out, and an optimal model is obtained through early stop mechanism optimization; predictive feedback double-closed-loop regulation and control is achieved based on the optimal model, outer loop PI control is combined with an integral separation mechanism to generate a basic control quantity, an inner loop outputs a correction quantity through fuzzification, reasoning and defuzzification, an actuator is driven after superposition, and safety linkage is triggered synchronously; constructing an incremental data buffer pool to screen effective samples, and adaptively updating model parameters by adopting a layered fine tuning strategy; the temperature regulation and control precision and the long-term self-adaptive capability are obviously improved, and the over-temperature risk of equipment is reduced.

The invention relates to the technical field of chip temperature control, and discloses a chip temperature regulation and control system and method, and the method comprises the steps: building a thermal field dynamic model, integrating real-time power consumption data and environment heat dissipation parameters, and generating a thermal resistance adjustment coefficient and a power consumption correction coefficient; establishing a temperature prediction network to perform multi-source temperature field prediction; a dynamic regulation and control strategy is set according to a prediction result, and heat dissipation control parameters are optimized to achieve thermal field balance control; and executing feedback calibration and model iteration. The system comprises a thermal field dynamic modeling module, a multi-source temperature field prediction module, a dynamic regulation and control strategy execution module, a feedback calibration module and an iteration module. The method can accurately predict the heat distribution of the chip, realizes real-time and intelligent temperature regulation and control, improves the performance and reliability of the chip, reduces the energy consumption, and has good adaptability and universality.

The invention discloses an agricultural product cold chain carriage temperature control method based on space equalization, and relates to the technical field of intelligent cold chain logistics, and the method comprises the steps: collecting carriage three-dimensional point cloud data and agricultural product electronic tag information, building a carriage loading three-dimensional model, carrying out the space grid division, and generating a partition topological feature vector; collecting temperature, humidity and carbon dioxide concentration data, calculating average temperature and average gas concentration observation values, and generating a breathing heat estimation value in combination with the partition topological feature vector; inputting the breathing heat estimation value into a dynamic prediction model, generating a subarea cold load curve and a priority weight by combining the subarea temperature deviation, and outputting a subarea cold load sequence; and according to the partition cold load sequence, generating a local execution proposal, constructing a global optimization problem taking the space temperature balance degree as an optimization target, and generating a global control plan. According to the method, global temperature equalization and energy consumption collaborative optimization are realized through network reasoning and dynamic optimization of the meta-reinforcement learning strategy.

The invention provides a performance optimization and regulation method for a high-temperature sintering furnace, and belongs to the technical field of intelligent regulation, and the performance optimization and regulation method comprises the following steps: arranging sensors in each heating area of the high-temperature sintering furnace, and monitoring the heating areas in real time; constructing a heating area operation state database, comparing the collected real-time data with a preset sintering process parameter range, and analyzing to obtain a performance analysis set of each heating area; establishing a three-dimensional heat conduction model, simulating heat flow distribution of each heating area under different heating powers and temperature settings, determining a thermal coupling effect of each heating area in different stages, and determining a heat transfer coefficient between adjacent heating areas; and an initial strategy of the high-temperature sintering furnace is determined, the initial strategy is optimized based on the auxiliary parameters of all the heating areas, and all the heating areas of the high-temperature sintering furnace are regulated and controlled according to the optimization strategy. The problems of inaccurate temperature control, single parameter analysis, insufficient thermal coupling effect processing and the like in the traditional regulation and control technology are effectively solved.

The invention relates to the technical field of electronic packaging, in particular to a welding process applied to a double-mesa device. The technology comprises the following steps that firstly, a heat conduction model of a double-table-board device is built, a temperature field prediction system is built in combination with measured data, secondly, an asymmetric double-pulse-current welding strategy is adopted, differential pulse waveforms are loaded to an upper table board and a lower table board respectively, independent temperature control over all welding areas is achieved, a temperature difference closed-loop feedback mechanism is introduced in the process, and the temperature field prediction system is obtained. Pulse parameters are dynamically adjusted according to real-time temperature deviation, a stress prediction and weld penetration monitoring module is used for assistance, the welding spot forming quality and heat stress balanced release are ensured, a staged annealing type cooling mode is adopted after welding, and the structural stability is improved. The method can be widely applied to MEMS modules, SiC power devices and heterogeneous chip laminated packaging double-mesa structure devices, and has the advantages of being high in welding precision, high in reliability and wide in process adaptability.

The invention relates to a method for intelligently monitoring the temperature of important parts of a server, which comprises the following steps: acquiring specified data of the server, inputting the specified data into a dynamic threshold model for training, acquiring various items in real time, inputting the trained dynamic threshold model, and adjusting the temperature threshold of each important part in real time according to the load change and environment change of the server; when the real-time temperature of a certain part is greater than or equal to the corresponding temperature threshold value, correcting the temperature threshold value of the associated part in real time, and performing coupling analysis on the temperature data of different parts under the same time dimension and the temperature change curve of different time nodes; when the real-time temperature of a certain part is greater than or equal to the temperature threshold determined by the dynamic threshold model, an early warning signal is sent out; and generating a temperature control strategy based on the early warning signal and executing a corresponding action by an execution component. The server temperature condition is comprehensively mastered through a dynamic threshold value model, correlation analysis and coupling analysis, an optimized control strategy is generated according to the early warning level, and rapid and accurate regulation and control of the server temperature are achieved.

The invention discloses a multistage fire-fighting and intelligent heat management combined system and method for an energy storage system, a battery cluster is provided with a single fire extinguishing nozzle, a composite detector monitors the state of the battery cluster in real time, and when any battery cluster meets a spraying condition, the fire extinguishing nozzle is triggered to spray a fire extinguishing material; the internal state of the energy storage cabin is monitored in real time through a composite detector, and when the interior of the energy storage cabin meets the spraying condition, a spraying nozzle is automatically started to continuously spray for preset spraying time; according to the centralized liquid cooling unit, a dual-redundancy compressor is combined with an ethylene glycol aqueous solution circulating system to conduct dynamic flow regulation and control, the opening degree of an electromagnetic valve of the dual-redundancy compressor is dynamically adjusted through a composite detector, and the internal temperature of an energy storage cabin is controlled; dynamically distributing flow according to the energy storage time period by adopting an intelligent temperature control algorithm; after the multi-stage fire-fighting module acts, the centralized liquid cooling unit is started for cooling; when the temperature detected by the composite detector exceeds a high-temperature threshold value, the fan is started for air replacement, and the multi-stage fire-fighting module is triggered to act.

The invention discloses a temperature control and noise reduction method for an analog optical module, and relates to the technical field of data processing. The method comprises the following steps: collecting and preprocessing an original temperature signal, environment temperature time sequence data and an optical wavelength original detection value of an optical module; improving the LSTM model, constructing a junction temperature dynamic prediction model, and outputting prediction data; a pre-trained BP neural network is utilized to calculate the temperature compensation amount according to the real-time wavelength offset, and a PPO reinforcement learning algorithm is adopted to optimize with the temperature stability, the wavelength offset and the TEC power consumption as multiple targets to obtain an optimal PID parameter mapping table; and finally, predicting, compensating and optimizing parameters are fused, and control output for driving the TEC is generated through a multi-mode intelligent decision. The problems of insufficient control precision and response lag are effectively solved, cooperative high-precision control over the junction temperature and the emission wavelength of the laser is achieved, transmission noise is remarkably reduced, and meanwhile system energy consumption and the self-adaptive capacity are considered.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

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 temperature control, in particular to a mass concrete water cooling water temperature control method and system. The method comprises the following steps: collecting concrete key parameters through a multi-source sensor, constructing a microenvironment model based on information geometry, and generating a thermosensitive characteristic spectrum; forming a distributed thermodynamic model by utilizing a mutual information routing algorithm and heat-fluid-solid coupling analysis, and calculating an optimal cooling strategy; water flow path and flow optimization is realized in combination with fractional order particle swarm optimization; a pump set and a valve are adjusted through fuzzy self-adaptive control; and a potential instability region is identified through the spectrum energy gradient, and risk early warning is triggered. The precision and reliability of mass concrete cooling temperature control are remarkably improved, and intelligent, efficient and safe temperature control adjustment in the whole process is achieved.

The invention relates to wide-range multi-component sensor calibration equipment and a wide-range multi-component sensor calibration method. The method comprises the following steps: firstly, checking equipment parts and setting a target temperature range and a compensation model of each temperature interval; then, dynamic temperature control is started, jig deformation is monitored in real time, a transient temperature gradient suppression technology is applied, multi-physical-quantity calibration is executed, and a compensation measurement value is output; then setting multi-environment coupling calibration test conditions to simulate actual working conditions, and monitoring and recording calibration data of each physical quantity; and finally, calculating a calibration error by combining a calibration result and a reference standard value. According to the process, by precisely setting parameters and combining dynamic control, real-time monitoring and multi-physical-quantity calibration, the transient temperature gradient influence is effectively inhibited, and the measurement precision of the sensor in a complex environment is ensured; the temperature control performance is improved through the micro soaking film and the intelligent temperature control strategy, the actual working condition is simulated through multi-environment coupling testing, the accuracy and reliability of the calibration result are remarkably improved through compensation and error calculation, and guarantee is provided for precise application of the sensor.

A temperature control device includes a temperature detector, a difference operation unit, a controller, a saturation processing circuit unit, a rewritable storage unit, and a conversion unit. The difference operation unit operates a digital value corresponding to a difference value between a detected temperature value and a target temperature. The controller calculates a manipulated variable using the digital value operated by the difference operation unit. The saturation processing circuit unit includes a digital circuit to limit an output value of the controller to a pre-set upper limit value. The rewritable storage unit stores the upper limit value read from a storage area of the rewritable storage unit and input into the saturation processing circuit unit. The conversion unit converts the output value of the saturation processing circuit unit into an analog signal to output the converted value as a control command value to the heater.

The invention discloses a PID temperature control system and method for a semiconductor etching machine, and relates to the field of temperature control, and the system comprises a coupling relation construction module, a temperature sensing confidence module, a decoupling compensation module, a coupling relation dynamic updating module, and a transient temperature change control module. According to the invention, by constructing the coupling relation matrix and the inverse matrix thereof, decoupling control of multi-area temperature control is realized, cross interference is effectively suppressed, and the temperature control precision is improved. And a multi-point temperature measurement fusion and anti-interference algorithm is introduced, so that the temperature measurement accuracy and the system robustness are enhanced. A response residual vector is associated with a control vector, and a coupling matrix is dynamically optimized, so that the system has self-learning and model self-adaption capabilities and adapts to working condition changes. And a transient temperature change quick response mechanism is set, so that the control priority can be improved and the PID parameters can be adjusted when the temperature changes suddenly, the response speed and the control stability of the system to abnormal temperature change are remarkably improved, and the thermal uniformity and the process consistency in the etching process are ensured.

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

The invention provides a zone-controlled intelligent charging pile double-cooling heat dissipation system and a control method, and belongs to the technical field of charging pile heat dissipation. Comprising an area division acquisition module used for monitoring the temperature generated by a function module in each thermal control area and the temperature generated by heat dissipation equipment in each thermal control area in real time and constructing a first data set and a second data set, and an early warning analysis module used for constructing an in-pile thermal risk coefficient Rheat and an environment thermal interference coefficient Renv, and the evaluation optimization module is used for constructing a comprehensive temperature control adjustment coefficient Rtotal. According to the system, the heating temperature of the function module is accurately monitored in a zoning manner, heat dissipation is quickly performed on the function module, the temperature change of the function module can be predicted in advance, the power consumption of equipment is reduced, and the heat dissipation efficiency of the equipment is improved.

The invention relates to the technical field of electric vehicle power battery thermal management, and discloses a temperature change-based power battery thermal management system, which comprises a thermal simulation pre-judgment end, a sensor network end, a dynamic liquid cooling control end, a BMS decision end and a fault diagnosis module, aiming at the technical defects of out-of-control temperature difference, response hysteresis and leakage risk caused by static flow channel design of the existing liquid cooling system, breakthrough is realized through a three-dimensional technical architecture: a CFD (Computational Fluid Dynamics) prejudgment layer: automatically calibrating sensor distribution points based on temperature field standard deviation, and improving temperature difference identification precision; according to the dynamic valve control layer, an electromagnetic three-way reversing valve is adopted to execute a directional temperature control strategy, an edge battery cell is preferentially introduced under the low-temperature working condition, and heat dissipation is enhanced under the high-temperature working condition; according to the redundant safety layer, the valve body and the pipeline are integrally formed, the leakage rate is reduced, and flow maintenance under single-point failure is achieved through a double-water-inlet parallel pump source. The method has the effects of improving the convergence speed of temperature difference between modules, reducing the attenuation rate of battery life and reducing the risk probability of thermal runaway.

The invention relates to the technical field of measurement and monitoring of road engineering variables, in particular to an asphalt pavement paving quality management system, which comprises a data acquisition module configured to acquire real-time meteorological data, mixture temperature parameters and pavement quality monitoring data; the viscosity analysis module is used for determining the dynamic viscosity adjustment range of the asphalt material based on the real-time meteorological data and the temperature parameters of the mixture; the temperature control module is used for regulating and controlling the mixing temperature according to the viscosity change of the asphalt material and generating a target paving temperature curve; the power optimization module is used for matching a dynamic adjustment strategy of the heating power of the screed of the paver based on the target paving temperature curve; by means of the system, meteorological and construction data are monitored in real time, parameters such as asphalt viscosity and screed power are dynamically regulated and controlled, closed-loop optimization is achieved, the paving quality is improved, and intelligent construction is achieved.

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 provides a split type direct-current charging pile liquid cooling heat dissipation system, and belongs to the technical field of direct-current charging piles, a closed liquid cooling circulation system is constructed through equipment such as a liquid cooling circulation pump, a radiator assembly and a temperature sensor array, and active temperature control is realized by adopting a temperature pre-adjusting module as a core. The system firstly collects power and temperature data to establish a power heat effect function to describe a power-temperature relationship, and extracts temperature time sequence characteristics through a heat situation sensing model by adopting an improved time sequence convolutional network to configure a self-adaptive receptive field adjustment mechanism to capture different time scale change modes; and constructing a double-layer optimization framework in combination with a temperature control game model to solve a Nash equilibrium solution, and calculating an optimal cooling adjustment vector including advanced cooling time and cooling power parameters. And a change curve is generated through real-time monitoring and dynamic correction and is converted into a control instruction, the frequency converter is controlled to adjust the rotating speed of the circulating pump and the opening of the flow valve, and advanced adjustment before temperature change is achieved to eliminate response lag.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system for power equipment, which comprises a load lag mapping module, a voltage drop rate identification module, a temperature control abnormity judgment module, a task energy consumption optimization module and a scheduling priority ordering module. According to the method, trend deviation is identified by extracting key sections of load and temperature change, an abnormal propagation path is constructed by combining continuity and directivity of a voltage change rate, an association relationship between a diffusion chain and nodes is determined, and a fault path with a time sequence recursion characteristic is extracted based on load change difference in a temperature rise process. According to the method and the system, the task type, the energy consumption trend and the communication requirement are combined to screen nodes with scheduling value, a scheduling list is dynamically generated, the response priority is synchronously adjusted, and efficient overall planning of remote monitoring node management and self-adaptive optimization of a scheduling strategy are realized.

The invention discloses a liquid cooling charging gun temperature control method for charging an electric vehicle, and relates to the technical field of liquid cooling control, and the method comprises the steps: carrying out the liquid cooling initialization, detecting the integrity of a bionic fractal microchannel and the distribution of phase change microcapsules, calibrating a magnetic field parameter, loading a pre-trained prediction model and a thermal resistance network model, and outputting a ready signal to a charging pile; after the charging pile communicates with a vehicle, according to charging power and environmental parameters, matching an initial flow, a magnetic field intensity threshold value and a piezoelectric vibration frequency, and activating a phase change microcapsule latent heat mode and pulse cooling; and executing pulse cooling and magnetic field fluctuation in a low-flow latent heat mode to remove sediments, recovering a full-flow mode when the temperature falls back, and monitoring an abnormal event to trigger grading protection at the same time. The dynamic heat dissipation performance and working condition adaptability of the liquid cooling charging gun are improved by constructing bionic fractal flow channel-phase change microcapsule-magnetic field regulation and control.

The invention provides a multi-section collaborative dynamic adjustment temperature control system and method, and relates to the technical field of multi-section temperature adjusting.According to the multi-section collaborative dynamic adjustment temperature control system and method, division and data acquisition of a screw rod section, a barrel section and a machine head section are achieved through the thermodynamic state of materials of all sections, and after thermodynamic state data of all the sections are obtained, the temperature of the materials of all the sections is obtained; based on shear heat modeling of a compression section, heat source distribution and axial temperature change of a screw area are obtained, a two-dimensional heat conduction model of a barrel section is established, heat flux density in an axial partition structure is continuously solved, flow coupling calculation is further introduced into a machine head area, outlet temperature fluctuation characteristics are extracted, and a heat flow distribution model is established. And finally, by uniformly fusing the output parameters of each section, a dynamic thermal potential energy function is constructed, and the system can realize overall quantification and uniform expression of the thermal state of each temperature control section. The real-time responsiveness and the global heat balance level of the multi-section temperature control system are effectively improved.