2331 results about "Heat distribution" patented technology

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention discloses an optical fiber data storage management system and method based on big data, and relates to the technical field of storage management. According to the optical fiber data storage management system and method based on big data, the spatial-temporal characteristics of the historical access records are extracted through the LSTM network, the data heat value is calculated in combination with the time decay function, and the thermal distribution diagram of the data blocks is generated. And based on the thermal distribution map, constructing a storage resource allocation model by using a deep reinforcement learning algorithm, and dynamically adjusting the optical path priority and copy distribution. A data prefetching strategy is optimized through the spatio-temporal joint index and the vector similarity index, and a high-frequency data cache index distribution diagram is generated. And comprehensively considering the data access delay, the index hit rate and the copy migration frequency, obtaining an optical fiber storage efficiency index, and adjusting a storage strategy according to the index. According to the method, the dynamic scheduling and utilization efficiency of data storage resources is effectively improved, the data access delay is reduced, the copy management is optimized, and the overall performance of a storage system is remarkably improved.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention relates to the technical field of chip testing, in particular to a chip testing method and a chip testing system. The method comprises the following steps: acquiring a thermal imaging image of a working state of a chip; global color temperature offset optimization is carried out on the thermal imaging image of the working state of the chip, and spatial heat distribution mining is carried out, so that a chip heat distribution diagram is constructed; performing multi-moment heat distribution tracking processing on the chip heat distribution diagram, performing dynamic heat distribution evolution fitting, and constructing a dynamic heat distribution trend evolution diagram; acquiring a high-definition image of a to-be-tested chip; performing chip assembly visual monitoring on the high-definition image of the to-be-tested chip, performing heat distribution trend rendering based on the dynamic heat distribution trend evolution graph, and constructing a chip assembly heat distribution trend rendering model. According to the invention, through limit working condition test analysis, the evaluation efficiency and accuracy of the chip performance are improved.

The invention discloses an energy management and safety protection cooperation method for a liquid cooling industrial and commercial energy storage system, and particularly relates to the technical field of energy storage system management. A battery electrochemical model, a heat distribution diagram, temperature gradient data and electrical parameters are used as input, and a battery temperature change trend curve is output; a liquid cooling control strategy is set according to the prediction result; fusing the temperature gradient abnormal parameters, the temperature trend risk and the multi-modal environment data abnormal parameters, starting a fire risk assessment model, predicting the fire probability and position, calculating a fire risk coefficient, generating a fire risk report and setting safety protection measures; a multi-objective optimization mathematical model is constructed based on the energy efficiency ratio, the full life cycle income and the battery health degree, energy storage operation data and power grid requirements are combined, a Pareto optimal solution set is generated by adopting a non-dominated sorting genetic algorithm, and a charging and discharging strategy and liquid cooling parameters are optimized; the liquid cooling pipeline layout is optimized through reinforcement learning, and the problem that the battery temperature cannot be effectively managed is solved.

The invention relates to the field of electromagnetic induction heating, and discloses a global temperature control method for an electromagnetic induction heating system, which comprises the following steps of: acquiring a two-dimensional temperature distribution diagram of a heating area at a plurality of key moments through a multi-point infrared temperature acquisition and induction heating coil synchronous excitation mechanism; performing multi-dimensional region segmentation on the global temperature scene data set, and identifying a temperature abnormal region and a thermal diffusion path feature based on a boundary thermal gradient change rate and an energy transfer path analysis algorithm; a distributed power regulation and control method and a historical thermal field evolution rule are introduced based on the regional thermal response feature map; thermal field feedback information is collected in real time in the temperature control process, and the current global thermal field state is judged in combination with a thermal distribution stability judgment criterion; and based on the updated control parameter configuration table, in combination with the thermal performance parameters of the material and the geometric information of the workpiece, generating an operation instruction set of the heating process, and executing intelligent regulation and control. The induction heating device has the advantage of improving the induction heating quality.

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention discloses an intelligent short-pulse laser processing cutting path optimization method and system. The intelligent short-pulse laser processing cutting path optimization method comprises the steps that S1, coordinate data of all cutting points in a laser processing area are obtained and processed; s2, simulating the heat distribution condition in the laser processing process according to the coordinate data, and outputting a heat distribution diagram; s3, based on the heat distribution diagram, calculating a moving path of the laser head through a path planning algorithm, and optimizing the path to minimize the total moving distance and reduce the influence of a heat accumulation area; s4, the optimized path is adjusted according to specific machining requirements, and parameters are dynamically adjusted through a real-time feedback mechanism; and S5, outputting the optimal cutting path, and applying the optimal cutting path to actual machining equipment to improve the machining efficiency and quality. According to the method, the cutting point coordinate data of the laser processing area are obtained and processed, the heat distribution condition is simulated, path planning is conducted by adopting the ant colony algorithm in combination with the heat distribution diagram, the idle stroke time is minimized, and the influence of a heat gathering area is reduced.

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 electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The invention discloses an AR intelligent photovoltaic inspection method and system, and the method comprises the following steps: S1, obtaining a panoramic image and heat distribution data of a photovoltaic module, and generating a data set; s2, multi-modal data preprocessing is carried out, key fault features are extracted, and fault areas are marked; s3, focusing and analyzing the marked area, and identifying a thermal abnormal point; s4, constructing an electrical topological graph, and generating a fault point priority; s5, performing ant colony optimization to adjust the inspection path, and marking the position of a fault point; s6, displaying the path, the fault point and the risk heat map in the AR equipment; and S7, generating an inspection report and performing feedback optimization. According to the invention, through multi-modal data fusion, dynamic path optimization and augmented reality technologies, efficient detection, accurate positioning and visual display of photovoltaic module faults are realized, and the inspection efficiency and the intelligent level are improved.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

The invention relates to the technical field of chip heat management, and discloses a chip heat distribution prediction modeling method and device, and the method comprises the steps: obtaining chip design information, carrying out the three-dimensional grid division through a pre-training transfer learning neural network, and constructing an electricity-heat-force multi-physics field coupling model; the method comprises the following steps: monitoring operation load data in real time, carrying out thermal simulation and generating a temperature distribution characteristic matrix, collecting data through a temperature sensor array, simulating thermal diffusion by using particle filtering and Bayesian inference, compressing and reconstructing the temperature distribution characteristic matrix by using a variational auto-encoder, generating a digital twinborn body, and optimizing a heat dissipation strategy by using deep reinforcement learning. The temperature control strategy is generated, the chip heat management precision is improved, the heat dissipation strategy is optimized, the performance and stability are improved, and the problem of insufficient prediction of temperature distribution in a traditional heat management scheme in the prior art is solved.

The invention discloses a temperature monitoring method for a photovoltaic grid-connected cabinet, and particularly relates to the technical field of photovoltaic grid-connected cabinets. The method comprises the following steps: arranging multiple types of temperature sensors in a grid-connected cabinet, collecting real-time temperature data, constructing a heat distribution model, comparing a current heat map with a historical steady-state heat field template, and identifying heat field offset and an atypical temperature rise trend; when the system detects that early warning is not triggered by an existing measuring point in a heat abnormal area, redundancy alarm logic is automatically started, temperature abnormal early warning is generated, and the speculated abnormal heat source position is recorded; according to the method, the sensing capability of the thermal anomaly in the blind area of the sensor is effectively improved, and the safety and the intelligent level of the system are enhanced.

The invention provides a welding defect identification method, system and equipment for an automobile part and a medium, and relates to the technical field of automobile welding.The method comprises the steps that a structured light vision sensor and a high-frequency ultrasonic sensor are used for obtaining three-dimensional structured light data of a to-be-welded area of the automobile part and sound wave signals generated in the welding process; obtaining characteristic frequency information used for identifying welding internal defects; the method comprises the following steps: capturing a heat distribution image of a to-be-welded area of an automobile part through a thermal imaging camera to obtain area feature information used for identifying welding defect thermal anomaly, and obtaining magneto-optical signal feature information used for identifying welding defects; performing defect identification and classification on the fused feature representation by training a deep learning model to obtain a defect identification result; and welding parameters are adjusted through a simulated annealing algorithm, so that the welding defects of the automobile parts are recognized. According to the invention, various welding defects can be identified, so that the accuracy and efficiency of welding quality detection are remarkably improved.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention relates to the technical field of cloud computing and distributed storage, and discloses a distributed metadata management method and storage system based on cloud computing, and the method comprises the following steps: S1, collecting the access frequency of a plurality of metadata objects in a set time period; s2, performing weighting processing on the access frequency, and constructing access heat distribution under multiple time scales; s3, obtaining the available cache capacity of a plurality of cache nodes, and constructing cache resource weight distribution; s4, assessing the access cost and migration cost between each metadata object and each cache node; and S5, generating a scheduling strategy between the metadata object and the cache node based on a preset optimization target, wherein the optimization target is access cost minimization. According to the method, a heat, resource and cost joint modeling mechanism is constructed, and optimal scheduling and feedback adaptive control are combined, so that accurate decision, dynamic adjustment and migration cost controllability of copy deployment are realized, and the performance and stability of distributed metadata management are effectively improved.

The invention provides a lithium battery equalization thermal management method. Real-time temperature data is obtained by constructing a thermal distribution monitoring network, the temperature difference between a center and an edge area is analyzed, a thermal load distribution coefficient is calculated, and a dynamic heat dissipation intensity adjustment model is established; driving the multi-channel cooling system to operate according to the heat dissipation demand priority, and adjusting the flow distribution proportion of a cooling medium; a dynamic nonlinear feedback correction mechanism is adopted, secondary adjustment is carried out for uneven temperature distribution, and an optimal heat dissipation parameter combination is determined in combination with historical data. Uniformity control over temperature distribution of the battery module is achieved, the problem of heat accumulation in a center area is effectively solved, the heat dissipation efficiency under the rapid charging working condition is improved, and the safety performance and the service life of the battery module are guaranteed.

An electronic device comprises a heat source and a heat distribution structure coupled to the heat source to distribute heat generated by the heat source during operation of the electronic device.

The dynamic hot runner temperature control method suitable for the mold comprises the steps that S1, a temperature sensor is installed in a mold hot runner to collect flow characteristics, heat distribution and temperature original data in the material solidification process, and the temperature original data are transmitted to a temperature control system; s2, the required power adjustment amount Pc is calculated through a PID controller in the temperature control system according to the deviation value delta T between the actual temperature Ta and the set target temperature Ts, and the power adjustment amount Pc is sent to a heater for adjustment; s3, constructing a temperature prediction model M, and predicting the temperature change trend of each section of the mold hot runner through the temperature prediction model M; and S4, a multi-objective optimization function Ft is created, an optimal combination solution St is searched through a particle swarm optimization algorithm PSO, and the optimal set value combination of the hot runner temperature is maintained according to an optimization objective. According to the invention, the energy consumption is reduced while uniform temperature distribution is ensured, the temperature control precision and the response speed are obviously improved, and the requirement of human intervention is reduced.

The invention discloses a dam termite cave positioning method based on transient electromagnetism carried by an unmanned aerial vehicle. The method comprises the following steps: constructing an unmanned aerial vehicle transient electromagnetic detection system; preprocessing the received secondary induction electromagnetic field signal, and obtaining a comprehensive electromagnetic signal according to the response difference of the multi-frequency signal; inputting the integrated electromagnetic signals into a pre-trained deep learning neural network model, identifying characteristic signals related to termite acupoints in the integrated electromagnetic signals, and outputting position coordinates of suspected termite acupoints; a dam surface thermal imaging image obtained by a thermal imaging module is combined for secondary verification, and termite cave position information is transmitted to a ground control terminal in real time through a communication module carried by the unmanned aerial vehicle. According to the invention, efficient, accurate and lossless positioning of the termite cave of the dam is realized, underground electromagnetic signals and surface heat distribution information of the dam are rapidly obtained, and accurate termite cave position coordinates are output.

The invention discloses a public transport passenger flow monitoring method and system based on human body detection, and the method comprises the steps: synchronously collecting skeleton key points, heat distribution and contour features of passengers through a vehicle-mounted multi-view camera and an infrared sensor, and generating a passenger space-time feature matrix; outputting a passenger movement track set based on the passenger space-time characteristic matrix; generating a dynamic congestion degree index according to the staying duration and the spatial distribution density of the passengers in the passenger movement track set; calculating an abnormal behavior probability based on the dynamic congestion degree index, and outputting an abnormal event early warning signal; and fusing the dynamic congestion degree index, the abnormal event early warning signal and the real-time passenger flow data, and generating a bus passenger flow monitoring result through space-time correlation analysis. According to the embodiment of the invention, high-precision, low-delay and intelligent passenger flow state monitoring and abnormity early warning can be realized.

The invention relates to the technical field of semiconductor packaging detection, and provides a self-adaptive semiconductor packaging detection method which comprises the following steps: synchronously acquiring packaging surface heat distribution data, three-dimensional deformation data and pin electric signal data through a multi-source sensor; performing dynamic weighted fusion on the multi-modal data based on the packaging process parameters to generate a defect feature matrix; performing dynamic threshold calibration on the defect feature matrix by adopting an adaptive threshold generation algorithm according to clustering center distribution in the historical defect pattern library; performing matching judgment on the dynamic threshold value and the real-time defect feature matrix through a multi-level logic decision model, and outputting a defect type, a position coordinate and a confidence score; and based on the defect type and the confidence score, triggering adaptive compensation control of the packaging process parameters, generating a feedback control instruction, and adjusting a packaging equipment execution mechanism in real time. The packaging quality and reliability can be improved, and the production cost and the defective rate can be reduced.

The invention discloses an intelligent welding quality evaluation method and system based on vision, and relates to the technical field of welding quality evaluation.The method comprises the steps that the welding process is monitored in real time through multi-mode imaging equipment, weld joint surface texture, molten pool heat distribution and three-dimensional shape information are synchronously obtained, and a three-dimensional voxel model is constructed; establishing a two-stage multi-channel architecture for executing dynamic resource scheduling, and extracting geometric features and deep semantic features in the three-dimensional voxel model; and on the basis of the defect confidence coefficient vector, in combination with the real-time process parameters and the molten pool state, a dynamic evaluation model of the welding quality index is constructed, and time-phased quality scores are calculated and output through stage weighting. According to the method, the welding seam surface texture, molten pool heat distribution and three-dimensional shape information are synchronously collected through multi-mode imaging equipment, a texture-temperature-space coordinate bound three-dimensional voxel model is constructed, and multi-source heterogeneous data fusion analysis of the welding quality is achieved.

The invention relates to a metal cutting machining path planning system, and aims to optimize the efficiency and precision of a metal machining process through advanced technical means. The system comprises a plurality of functional modules: an image acquisition module captures a multi-angle high-resolution image of a to-be-processed metal workpiece; the three-dimensional reconstruction module reconstructs a three-dimensional model by using the images; the cutting simulation module simulates the three-dimensional model in a virtual environment and records stress and heat distribution conditions in the cutting process in real time; the intelligent segmentation module divides the machining process into a plurality of stages according to the stress and heat data; the comprehensive optimization module is combined with a genetic algorithm and a simulated annealing algorithm to optimize the cutting path, depth and feeding speed of each machining stage; the feedback adjusting module monitors stress and heat changes in actual machining, and when the stress and heat changes exceed threshold values, cutting parameters are automatically adjusted or machining is paused. According to the system, through comprehensive application of image processing, analog simulation and optimization algorithms, the cutting efficiency and the product quality are remarkably improved.

The invention belongs to the field of coal gangue recognition, and particularly relates to an intelligent coal gangue recognition and analysis system based on machine vision and image processing, which analyzes the conformity score of each target object relative to coal gangue by combining the first-order matching degree, the second-order matching degree and the third-order matching degree of each target object relative to the coal gangue so as to recognize whether each target object is the coal gangue or not. Wherein the first-order matching degree comprehensively considers color features, texture features and morphological features in appearance images of the target objects, and the second-order matching degree comprehensively considers a heat distribution effect, a heat boundary effect and a heat conductivity effect fed back in thermal images of the target objects; according to the method, the internal uniformity of X-ray imaging partitions and the uniformity between the partitions of each target object are comprehensively considered through the three-order matching degree, and the target objects are evaluated from different angles through the matching degrees of different orders, so that coal gangue recognition has strong complementarity and accuracy, and the matching degree of the target objects and the coal gangue can be known more deeply and more comprehensively.

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 relates to the technical field of connector layout optimization, and discloses a high-density four-row connector layout intelligent optimization method. The method comprises the steps that design parameters of four rows of connectors on the high-density circuit board are collected, the design parameters comprise connector position coordinates, the number of pins and electrical impedance values, and related environment data comprise temperature distribution, electromagnetic interference intensity and vibration frequency; performing standardized alignment processing on the design parameters and the environment data to form a layout data set with unified dimensions; the layout optimization model is used for processing the layout data set, an initial layout scheme is generated, and the initial layout scheme comprises connector arrangement sequence and position adjustment suggestions; monitoring change data of the working state of the circuit board in real time, wherein the change data comprise current fluctuation and heat distribution updating; and identifying a layout abnormal condition based on the change data, performing iterative correction on the initial layout scheme according to the layout abnormal condition, and outputting a final optimized layout scheme. The method can dynamically adapt to the working state of the equipment and improve the layout rationality.

The invention provides a cooling adjusting method and system for an injection mold, and relates to the technical field of injection molds. The method comprises the following steps: acquiring real-time temperature data in the injection molding process of the mold by utilizing temperature sensors arranged at a plurality of preset measuring points of a mold cavity of the injection mold; based on the real-time temperature data, a mold cavity heat distribution model representing mold cavity temperature distribution information is generated; according to the mold cavity heat distribution model, the mold cavity is divided into a plurality of sub-cooling areas, and each sub-cooling area corresponds to at least one cooling loop; determining the temperature state of each sub-cooling area based on the mold cavity heat distribution model, and generating a flow regulation instruction of the corresponding cooling loop according to the temperature state; and controlling an electric control valve and a variable frequency water pump of each cooling loop according to the flow regulation instruction so as to regulate the flow of the cooling fluid flowing through each sub-cooling area. According to the technical scheme, partition precise adjustment of the cooling process of the injection mold can be achieved.

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.