876 results about "Temperature gradient" patented technology

The invention discloses a pepper freezing temperature gradient dynamic adjusting method and system, particularly relates to the technical field of industrial freezing temperature control, and is used for solving the problem of temperature gradient misalignment caused by the fact that a static model does not sense dynamic changes of material thermodynamic parameters in real time in an existing freezing control method. Temperature distribution and gas flow rate data in the freezing process are collected in real time, a high-confidence-coefficient data subset of a low-flow-rate area is screened based on spatial correlation, the dynamic heat conduction coefficient of the material is identified online through a recursive algorithm, and the dynamic heat conduction coefficient of the material is identified through a double-threshold-value judgment mechanism of the energy release rate and the phase lag angle. Performing segmentation fitting to generate a stable parameter sequence; further dynamically adjusting a preset temperature gradient model through a nonlinear correction strategy to generate a self-adaptive gradient control curve, and finally driving heat exchange rate adjustment to realize closed-loop tracking of the actual temperature gradient on a target curve; the freezing uniformity and energy efficiency of the high-moisture material are remarkably improved, and the control precision caused by thermal response mismatch is improved.

The invention discloses a server heat dissipation device and a heat dissipation control method.The surface of a server mainboard is divided into a core heat dissipation area, a transition heat dissipation area and an edge heat dissipation area, and the method comprises the steps that real-time temperature data of all the areas are synchronously collected through a temperature detection module; constructing a three-dimensional temperature field distribution map containing temperature gradient and heat flow direction; then thermodynamic parameter calculation is carried out, temperature time sequence change characteristics of all the partitions are extracted, and a thermal diffusion path in a future set time period is predicted by fusing the temperature time sequence change characteristics and the thermodynamic parameters; a control instruction set is generated according to the predicted thermal diffusion path, the actual heat dissipation response is monitored through an acoustic sensor in the execution process, monitoring data and the predicted thermal diffusion path are dynamically compared, and control parameters of the control instruction set are updated till all the partitions reach the target temperature threshold value; and the sudden temperature change is pre-judged, the hot spot elimination response time is shortened, and the collaborative breakthrough of the heat dissipation efficiency and the energy consumption control of the high-density server cluster is realized.

The invention relates to the technical field of communication machine room safety protection, and discloses a communication machine room intelligent fire monitoring and fire extinguishing system based on edge computing, which comprises a fire parameter acquisition module, a temperature characteristic analysis module, a smoke dynamic detection module, a multi-source fusion judgment module, a regulation and control parameter generation module and the like. The fire parameter acquisition module acquires parameters such as temperature gradient, smoke concentration and equipment current, judges fire risk and triggers cooperative monitoring; the temperature characteristic analysis module extracts temperature distribution characteristics and quantitatively evaluates hot spot abnormity; the smoke dynamic detection module analyzes the smoke diffusion trend; the multi-source fusion judgment module is combined with multiple parameters to generate a fire extinguishing or optimizing signal; and the regulation and control parameter generation module is matched with the strategy to generate injection and ventilation parameters. The system also corrects a risk index through an equipment coupling analysis module, and forms closed-loop control by using a dynamic feedback execution module. According to the system, multi-source data fusion monitoring and intelligent fire extinguishing are realized, and the fire prevention and control accuracy of the communication machine room is improved.

The invention discloses a temperature control management method for an electric oven, and relates to the technical field of intelligent household appliance control, and the method comprises the steps: carrying out the offset correction of a pre-stored mode mapping table based on a calibration matrix, loading a target temperature interval and a three-dimensional temperature field partition parameter according to a cooking mode selected by a user, and triggering a dynamic learning mark; according to the dynamic learning mark, the furnace chamber temperature and the environment temperature change rate are collected in real time, the power compensation amount is calculated through a thermal inertia compensation model, and a partition power control matrix is generated in combination with the three-dimensional temperature field partition parameters; and distributing the power compensation amount and the partition power control matrix to the heating pipe according to a preset topology, dynamically adjusting power output, and implementing temperature early warning and no-load identification based on an acoustic emission spectrum entropy value. The temperature gradient change rate is calculated through the Fourier heat conduction law, the three-dimensional temperature field partition parameters are dynamically generated, and accurate control over the temperature field in the complex three-dimensional space is ensured so as to cope with environment changes and user demand changes.

The invention provides a hydrogen energy storage equipment leakage fault early warning method and system based on deep learning, and the method comprises the steps: collecting the multi-source sensing data of hydrogen energy storage equipment in real time, constructing a multi-dimensional feature matrix, building the nonlinear mapping of pressure fluctuation and gas concentration through a bidirectional interaction network, and carrying out the early warning of the leakage fault of the hydrogen energy storage equipment. And performing spatial modeling on the temperature gradient and the vibration spectrum to generate a fusion feature tensor. And then, inputting the fused feature tensor into a spatio-temporal reasoning model, predicting equipment leakage and energy loss, and outputting a leakage probability cloud picture and an energy loss vector. And an entropy sudden change area is identified based on an energy loss vector, when the high-density leakage area and the entropy sudden change area are overlapped in a continuous detection period, a multi-stage early warning signal is triggered, a leakage source thermodynamic diagram and a pressure balance parameter set are generated, a self-adjusting system is activated to dynamically correct the valve opening degree, network weight is updated, and a closed-loop control link is formed. The early warning accuracy and the response speed of the leakage fault of the hydrogen energy storage equipment are improved.

The invention discloses a temperature measurement precision optimization method based on multiple sensors, and belongs to the technical field of temperature measurement, and the method specifically comprises the steps: deploying a temperature monitoring array which comprises a fixed position basic sensor group and a position adjustable auxiliary sensor group; scanning the target area through an infrared thermal imaging device to generate a thermal field temperature gradient distribution map; identifying a high dynamic change region boundary based on the thermal field temperature gradient distribution map, and extracting geometric feature parameters of the boundary; constructing a heat flow propagation prediction model according to the boundary geometric feature parameters, and calculating a key monitoring node space coordinate set on a heat conduction path; generating a sensor deployment instruction according to the key monitoring node space coordinate set, and dynamically scheduling an auxiliary sensor group to move to a target position; fusing the temperature data streams of the basic sensor group and the auxiliary sensor group, and executing space-time calibration calculation to generate an optimized temperature field distribution diagram; according to the invention, the coverage integrity and the result accuracy of temperature measurement in a dynamic scene are improved.

The invention relates to the technical field of electrical equipment monitoring, in particular to a power distribution equipment state intelligent diagnosis system and method, and the system comprises a current fluctuation detection module, a voltage stability evaluation module, a heat effect diffusion analysis module, a fault point positioning module and an equipment state evaluation module. According to the invention, through extracting current change characteristics of key nodes and judging continuity of fluctuation differences, rapid identification of current anomalies can be realized, a voltage distortion region and a distribution range thereof can be positioned in combination with phase deviation and symmetry change of voltage waveforms, and the current anomalies can be identified based on a temperature gradient path and jump point screening. A hot spot diffusion trend and a thermal disturbance boundary can be described, morphological analysis and coordinate labeling are performed on the basis of multiple fault areas, multi-point concurrent positioning is realized, association between thermal disturbance and connection defects is identified in combination with temperature difference and current path change characteristics, fault diffusion trends and state disturbance blocks can be accurately presented, and the accuracy of fault diagnosis is improved. And the power distribution state identification and intervention efficiency is improved.

The invention relates to the technical field of intelligent control, in particular to a composite material forming process intelligent control system which comprises a micro-scale deformation detection module, a stress offset correction module, a temperature gradient regulation and control module, an interlayer curing balance module and a forming intelligent control execution module. According to the method, a deformation rate sudden change area is accurately positioned through micro-scale detection, and stress concentration is found in advance, so that stress adjustment is more targeted, the local stress overload influence is reduced, real-time pressure feedback is combined with target pressure offset analysis to optimize a loading mode, uniform stress in the curing process is ensured, and the defect risk is reduced; high-precision temperature gradient monitoring and thermal diffusion rate analysis improve temperature control precision and improve interlayer temperature equilibrium, curing rate change rate calculation and heat conduction path optimization dynamically adjust a heat dissipation channel and promote uniform curing, stress-temperature distribution equilibrium degree analysis is combined with a deformation trend to optimize heating and pressure parameters, and uniform curing is achieved. And the forming stability and the quality consistency are improved.

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

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention relates to the technical field of alloy die-casting, and particularly discloses an alloy die-casting temperature field control method and system capable of reducing hot cracks, temperature gradients of different areas of a die are adjusted in real time according to flowing and solidification characteristics of alloy liquid, so that the surface temperature distribution of the die is matched with the solidification rate of the alloy liquid, and the alloy die-casting temperature field is controlled. Utilizing a machine learning algorithm to predict a risk area where hot cracks are generated on the basis of historical data and real-time monitoring data, and dynamically adjusting the flow velocity of a cooling system, the heating power of a die or die-casting process parameters according to a prediction result; through dynamic temperature gradient control, intelligent prediction and feedback control, a cooling strategy, a stress release mechanism and a real-time monitoring and optimizing process, thermal stress concentration can be effectively reduced, the possibility of generation of thermal cracks is reduced, the yield of castings is improved, the cooling time of the castings can be shortened through the cooling strategy, and therefore the production efficiency is improved, and the production cost is reduced. And reworking and scrapping of castings are reduced.

The invention relates to the technical field of wind power operation and maintenance technologies, and discloses a combined heat dissipation system of a fan, the system comprises a multi-source sensing module, a decision control module, a heat dissipation execution module and a safety early warning module, and by setting multi-mode cooperative control, when fan heat dissipation regulation and control are carried out, the heat dissipation of the fan can be controlled in a multi-mode cooperative mode; a dynamic matching mechanism of temperature field characteristics and heat dissipation modes is established, and air cooling, liquid cooling and phase change strengthening modes are automatically switched in real time according to the temperature rise rate and the heat distribution gradient, so that the system can accurately adapt to heat dissipation requirements under different working conditions, timely heat dissipation of a high-temperature core area is guaranteed, the heat dissipation response accuracy is improved, and the service life of the system is prolonged. By arranging the three-dimensional thermal field balancing system, the hot area distribution difference of the bearing, the winding and the power module is recognized in real time, the cooling liquid flow and the airflow guide angle are dynamically adjusted, the three-dimensional thermal unbalance phenomenon in a traditional heat dissipation mode is eliminated, and it is guaranteed that the temperature gradient of all areas in the motor is always kept within a safety threshold value.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention discloses a converter valve internal temperature monitoring and early warning method and system, and particularly relates to the technical field of converter valve temperature monitoring. Temperature sensors are arranged on a plurality of key heating units, local temperature data are collected in real time, a time sequence is built in combination with cooling water inlet and outlet temperature, a temperature trend prediction model is built based on temperature rise rate and temperature gradient change, local thermal runaway risks are accurately recognized, and a prediction result is compared with a grading threshold value, so that the local thermal runaway risk is determined. Multi-stage early warning signal output is realized, and a corresponding processing strategy is linked; under the extreme conditions that the local temperature is continuously and abnormally increased and the response of the cooling system is lagged, the system can trigger emergency shutdown protection in an override mode, device failure is avoided, the sensing capacity and response speed of local thermal faults are effectively improved, and the operation safety and the intelligent level of the converter valve system are remarkably improved.

The invention discloses a distribution transformer monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

The invention relates to the field of intelligent temperature control of industrial kilns, and discloses a graphitization furnace temperature uniformity optimization processing system which comprises a temperature monitoring module, a dynamic matrix control module, a material-control coupling module, a lattice feedback module and a multi-objective optimization module. The method comprises the following steps: constructing an equivalent temperature field through multi-source temperature data fusion; predicting a graphitization trend and generating a dynamic matrix control instruction in combination with a multi-physical field model; dynamically adjusting a thermal conductivity matching temperature gradient of the guard plate; online correcting a material constant based on lattice feedback; closed-loop control is formed, and the process stability and energy efficiency of the graphitization furnace are improved. Through multi-source fusion monitoring, multi-physical field coupling modeling, dynamic thermal conductivity regulation and control, lattice feedback online correction and reinforcement learning multi-objective optimization, temperature uniformity control and energy efficiency improvement of the graphitization furnace are realized, and material performance and process economy are guaranteed.

The invention discloses an electronic wire harness intelligent high temperature resistance test method based on dynamic temperature monitoring, and the method comprises the steps: collecting multi-region temperature data through a sensor array, and dynamically adjusting the measurement point density and sampling frequency for a region with a higher temperature change rate; for a local high-temperature area, sampling points are increased, a high-temperature micro-change monitoring mechanism is triggered, and micro-change of the temperature is recorded in real time; according to a temperature gradient change trend, dynamic adjustment parameters of a key area are extracted, and density distribution of measuring points is optimized; meanwhile, optimal utilization of computing resources is also considered, and the density of measuring points in a temperature stable region is properly reduced; through continuous monitoring and algorithm parameter optimization, stable high temperature evaluation is finally realized, and the temperature evaluation error is controlled within 1 DEG C. According to the dynamic self-adaptive temperature monitoring method, the temperature monitoring precision and efficiency during operation of the electronic wire harness are remarkably improved.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

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.

The invention discloses a brittle material laser cutting thermal stress control method and system, and the method comprises the steps: obtaining real-time temperature distribution through infrared thermal imaging, combining finite element analysis and material characteristic dynamic data, constructing a thermal stress and fracture relation model, carrying out the iterative calculation of a thermal stress out-of-control threshold through a lambda algorithm, and determining a regulation boundary condition. According to the boundary condition, laser parameters are optimized, synchronous cooling airflow is introduced, the temperature gradient is dynamically controlled through an airflow pressure adjusting algorithm, historical data are processed through a convolutional neural network, the optimal laser parameter combination is predicted, in the cutting process, the microcrack diffusion state is monitored in real time, and auxiliary technological parameters are finely adjusted when necessary. The problem of fracture caused by out-of-control thermal stress in laser cutting of the brittle material is effectively solved, the cutting quality and efficiency are improved, and a new technical scheme is provided for precision machining of the brittle material.

The invention provides a tunnel fire behavior inversion method based on multi-modal fusion and physical constraint, and aims to solve the problem that the position and power of a fire source are difficult to invert accurately in real time in traditional fire behavior monitoring. According to the method, data, temperature distribution images and physical parameters of distributed temperature sensors in a tunnel are collected, and a spatial-temporal feature fusion network is constructed; designing an information leakage prevention training mechanism, and adopting a modal random discarding and sensor shielding technology to avoid overfitting of the model to a specific input mode; a physical constraint loss function is introduced, and the temperature gradient, the maximum temperature rise and the longitudinal attenuation law are combined to ensure the physical rationality of an inversion result; a coarse-fine two-stage position prediction framework is adopted, and meter-scale precision positioning of the position of a fire source is achieved. According to the invention, the power and position of the fire source can be inversed accurately in real time, and reliable technical support is provided for intelligent monitoring and emergency rescue of tunnel fire.

The invention relates to the field of mold cooling optimization, and discloses an automobile mold cooling optimization system and method based on reinforcement learning, and the method comprises the steps: collecting the real-time temperature and flow data of each cooling loop, carrying out the preprocessing and abnormity filtering of the data, and constructing a multi-dimensional basic cooling state database through combining historical operation records and environmental parameters; performing spatial mapping and statistical analysis on the temperature and flow data in the basic database, identifying a high-temperature region with heat concentration and a part with remarkable temperature gradient, and forming a local heat feature model; establishing an independent control sub-model, and optimizing cooling water flow and temperature regulation parameters of each loop by adopting a simulation and reinforcement learning combined method; a local adjustment strategy and an overall cooling target are fused, and main loop parameters are optimized through a global control algorithm; in the mold cooling process, temperature and flow fluctuation are continuously monitored, and real-time data are input into an optimization system. The method has the advantage that the cooling precision and efficiency are improved.

The invention provides a frying and baking equipment heat preservation automatic control method and system, and relates to the technical field of food processing equipment control, and the method comprises the steps: collecting temperature signals of different positions through a plurality of groups of thermocouples and infrared thermal imaging sensors which are arranged in a frying and baking area, constructing a dynamic three-dimensional temperature field model, and obtaining a dynamic three-dimensional temperature field model; calculating a temperature standard deviation and a maximum temperature gradient in real time according to the dynamic three-dimensional temperature field model; and when the temperature standard deviation is larger than or equal to a preset threshold value, according to the heat distribution difference of the three-dimensional temperature field model, a multi-region differential compensation strategy is generated, the power distribution proportion of each heating unit is dynamically adjusted, and the duty ratio parameter of pulse width modulation is optimized in real time. Through multi-sensor accurate monitoring, dynamic regulation and control and intelligent stop judgment, uniform and stable temperature control, energy conservation and consumption reduction are achieved, the service life of equipment is prolonged, meanwhile, the use safety of a user is guaranteed, and the cooking experience is improved.

The invention relates to the technical field of wire performance detection, and discloses a wire high temperature resistance detection method, device and equipment and a storage medium. The method comprises the following steps: arranging micro thermocouple array sensors on the surface, the bending part and the connecting point of an outer insulating layer of a to-be-measured wire rod to obtain a wire rod temperature monitoring network; placing the to-be-tested wire rod in a constant-temperature box to execute a high-temperature cyclic loading test, and collecting real-time temperature response data of the to-be-tested wire rod; carrying out temperature gradient analysis and heat distribution characteristic calculation to obtain a temperature change characteristic parameter set; executing temperature fluctuation compensation and thermal stress accumulation calculation to obtain thermal stress characteristic parameters; and carrying out aging characteristic analysis on the wire insulating material through the thermal stress attenuation network model to obtain wire predicted life and failure probability distribution of the to-be-tested wire in a high-temperature environment. The method can comprehensively capture the temperature change characteristics of each part of the wire rod, and achieves the precise prediction of the failure probability of the wire rod under different temperature conditions.

The invention relates to the technical field of filling automation control, in particular to a full-automatic essential balm filling control system based on PLC online monitoring, which comprises a filling parameter real-time acquisition module, a dynamic coupling analysis module, a multi-axis cooperative execution module, a foreign matter real-time detection module, an interlocking control module and a filling quality feedback module. Wherein the filling parameter real-time acquisition module is used for acquiring temperature data of a filling head, a liquid viscosity value and the flow velocity of a filling pipeline in real time; and the dynamic coupling analysis module is used for establishing a correlation model of the temperature gradient and the viscosity change rate and generating a dynamic compensation parameter set. According to the method, the nonlinear correlation model of the temperature and the viscosity change rate is constructed, and the dynamic compensation parameter set is generated, so that self-adaptive precise control in the filling process and automatic response linkage of abnormal working conditions are realized, and the stability and the intelligent level of a filling system are remarkably improved.

The invention discloses a thermal balance control method for a liquid cooling charging module, and relates to the technical field of power electronics, and the method comprises the steps: deploying a multi-dimensional distributed temperature sensing array in a key heating region of the liquid cooling charging module; original data are acquired through the temperature acquisition module and transmitted to the temperature field reconstruction module, a three-dimensional dynamic temperature field model is constructed by using a spatial interpolation algorithm, and temperature values, temperature gradients and temperature difference distribution of all regions are output in real time; the thermal balance control module compares the model output data with a preset temperature threshold value and a temperature difference threshold value, and if the threshold value exceeding condition exists, a regulation and control instruction is sent to a partition cooling execution unit; the subarea cooling execution unit comprises three independent branches (corresponding to key areas), and each branch realizes accurate adjustment through an independent electromagnetic flow valve and a variable frequency water pump; and the steps are circulated until the temperatures of all the key areas reach the standard and the temperature difference of the adjacent areas meets the standard, so that the heat balance precision and the operation safety of the liquid cooling charging module are effectively improved.

The invention discloses a temperature effect monitoring method and system for a metal roof, particularly relates to the technical field of building structure health monitoring, and is used for solving the problems of decoupling of temperature monitoring data and structural mechanical response, failure of a static early warning threshold and the like in the prior art. Acquiring three-dimensional temperature field data through a distributed temperature sensing network, and extracting a temperature gradient characteristic value from the three-dimensional temperature field data; a piezoelectric film sensor is used for collecting constraint point micro-vibration signals, and dynamic constraint reaction force spectrum characteristic values are extracted through signal transformation and manifold learning; establishing a temperature gradient and mechanical characteristic coupling model to generate a temperature stress evaluation value; dynamically adjusting an early warning threshold range according to the environmental parameters; when the evaluation value exceeds a threshold value, a structure safety early warning signal is output; multi-physical field coupling analysis of temperature field distribution and structural response is achieved, an environment-adaptive dynamic early warning mechanism is constructed, and the accuracy and reliability of metal roof temperature effect monitoring are remarkably improved.

The invention relates to the technical field of building environment temperature control, and discloses a temperature control equipment intelligent adjusting method and system based on machine learning, and the method comprises the steps: building a space thermal field sensing network, obtaining multi-dimensional temperature field data, and generating a temperature gradient distribution matrix; and the positions of cold and hot spots are calibrated by combining infrared temperature measurement data, an airflow path is predicted, and air supply parameters are optimized. Meanwhile, building heat capacity characteristics are analyzed, and a thermal response dynamic model is constructed. And the air supply direction is adjusted for the local supercooling area. And based on the temperature change rate and the air supply parameters, the equipment start-stop time is optimized. Through real-time temperature sampling, continuous updating of temperature gradient distribution, cyclic execution of cold and hot spot calibration, airflow prediction and air supply optimization, accurate control of space temperature is realized. According to the invention, the accuracy and energy efficiency of building space temperature adjustment can be obviously improved, and an innovative solution is provided for an intelligent temperature control system.

The invention relates to an error compensation method of a birefringence self-calibration laser level meter, in particular to the field of laser level meters, and aims to improve the precision of the laser level meter by combining a birefringence effect, physical modeling and neural network optimization. Firstly, temperature data are collected in real time, Kalman filtering is used for noise reduction, and accurate data are provided for physical modeling; then, through physical constraints such as a heat conduction equation and a Jones matrix, a coupling relation between the temperature gradient and optical parameter changes is established; then, dynamically optimizing the refractive index correction by using a physical information neural network to avoid an overfitting problem; and finally, calculating optical path difference compensation by using a hardware accelerator, correcting a system error in real time, and feeding back and adjusting a network weight. According to the method, the precision and the stability of the laser level meter in an environment with relatively large temperature change are effectively improved.

The invention provides an in-tunnel inrush water pressure monitoring method and system based on dual-acquisition multi-modal data, and belongs to the technical field of inrush water pressure monitoring. Comprising the steps of collecting video data by adopting a high-definition camera; sequentially performing water flow area intelligent segmentation, optical flow calculation and ConvLSTM time sequence prediction on the video data to predict the water flow velocity; an infrared camera is adopted to collect temperature data; self-adaptive temperature gradient calculation and temperature anomaly detection are sequentially carried out on the temperature data to predict the water flow temperature; aligning and fusing the predicted data of the water flow velocity and the water flow temperature, and constructing a water pressure calculation model to obtain a water pressure predicted value; and determining an early warning level according to the water pressure predicted value so as to adjust the monitoring mode of the inrush water pressure in the tunnel. According to the method, the infrared technology and the vision field technology are combined, accurate prediction of the inrush water pressure in the tunnel can be achieved, and a corresponding early warning monitoring mode is provided according to the prediction result.