49 results about "Power usage effectiveness" patented technology

The invention relates to the technical field of intelligent power grids, in particular to a virtual power plant multi-level collaborative optimization control method based on digital twinning. According to the invention, data of the power generation end, the power storage end, the power transmission end and the load end are collected and processed in real time, a digital twin model is established, and accurate modeling and real-time monitoring can be carried out on each device of the virtual power plant. Through real-time optimization scheduling and cooperative control, energy loss can be minimized, supply and demand can be balanced, the energy use efficiency is improved to the maximum extent, and the cost is reduced; according to the method, the genetic algorithm and the particle swarm optimization algorithm are combined, scheduling optimization is carried out based on a multi-level collaborative optimization model, a virtual power plant can rapidly respond according to changes of real-time environment and load requirements, fossil energy reduction and clean energy increase are preferentially considered in the power generation equipment scheduling process, and greenhouse gas emission reduction is facilitated.

The invention relates to the technical field of machine room energy consumption management, and discloses a machine room energy-saving control method and system based on cloud computing, and the method comprises the steps: collecting equipment operation data in real time through a multi-dimensional sensor network, building a power consumption prediction model and an energy distribution optimization model, and extracting key features through principal component analysis; and constructing a multiple regression model to determine an energy efficiency coefficient, and predicting a future power demand by adopting a long-short-term memory neural network. And an energy distribution scheme is optimized based on a genetic algorithm, and accurate power control and dynamic balance are realized. According to the invention, a self-adaptive energy consumption management decision mechanism is also established, the energy consumption trend is analyzed through a sliding window, an emergency adjustment program is started, state estimation is corrected by using a Kalman filtering algorithm, and parameters of a prediction model are dynamically adjusted. According to the method, intelligent management of energy utilization of the data center can be realized, the total energy consumption cost is effectively reduced, the energy utilization efficiency is improved, and technical support is provided for energy conservation and emission reduction of a large machine room.

The invention discloses a building energy-saving optimization system, method and equipment based on photovoltaic power generation, and relates to the field of energy-saving system management.The building energy-saving optimization system comprises a data acquisition module, a data uploading module, a data processing module and a data response module. The data flow of each edge node is asynchronously uploaded to the data processing module at the maximum uploading rate, the photovoltaic equipment data is disassembled and segmented, the system splits a data processing task into a plurality of sub-tasks, and the sub-tasks are processed by adopting a multi-channel task scheduling architecture. The load state of the channel is monitored in real time, task allocation is adjusted in time, the energy use efficiency is effectively improved, building energy consumption is reduced, and meanwhile the stability of the system is guaranteed.

The invention relates to the technical field of energy management, in particular to a smart park comprehensive energy optimization management and control system and method, and the method comprises the following steps: collecting the energy data of electric power, heating power and natural gas in real time, and employing the algorithm based on a long-short-term memory network, support vector regression and random forest regression; modeling is carried out on demand characteristics of electric power, thermal power and natural gas, and future demands are predicted; the method comprises the steps of establishing an optimization objective function, optimizing park energy configuration by adopting a particle swarm optimization algorithm based on the established optimization objective function, determining an optimal energy scheduling scheme, and finally applying the optimized energy scheduling scheme to a park energy management system to realize dynamic scheduling and real-time control of energy. According to the invention, the energy waste of the park can be effectively reduced, the energy use efficiency is improved, and intelligent and refined management of energy is realized.

The invention is suitable for the technical field of energy consumption control, and provides an energy consumption prediction and energy-saving control method adopting a deep neural network. According to the method, virtual training data is generated by constructing a machine room digital twinborn model including a three-dimensional geometric model and a CFD simulation model, and the electric energy use efficiency and the overheating condition are predicted in combination with a deep neural network optimized by an attention mechanism. And the operation strategy system generates an optimal control suggestion by predicting the energy-saving effect and the safety risk after the air conditioner is turned off or the temperature is increased. The visual interface integrates real-time parameters, historical data and three-dimensional airflow organization dynamic display, and assists an administrator in decision making. The method breaks through the limitation of real data, improves the prediction precision, reduces the PUE on the premise of ensuring that the operation number of air conditioners is greater than or equal to 3, and achieves the efficient and energy-saving management of a machine room. Experiments show that the PUE can be reduced by about 4.3%, and the cabinet overheating probability can be reduced by 60%.

The invention relates to the technical field of logistics management, in particular to an intelligent supervision control system for operation and maintenance of a cold chain system, which comprises the steps of collecting path environment data to screen an operation and maintenance optimal section, then identifying an energy consumption over-limit risk in the section, and executing main-standby switching on equipment with temperature rise exceeding the standard, meanwhile, power supply sufficiency is judged according to load requirements so as to start a redundant power supply, finally, a priority scheduling object is determined according to resource and task urgency by integrating a power supply state and energy consumption data, and a cold chain operation and maintenance centralized supervision control set is generated. According to the method, path selection is optimized by monitoring path environment fluctuation in real time to filter unstable road sections, energy consumption data and load standards are compared to identify and early warn overload risks, equipment switching and power supply scheduling are automatically executed to avoid operation and maintenance interruption when equipment temperature rise is too high or power supply is insufficient, and finally each link is intelligently scheduled through linkage management and control. The system operates stably and efficiently, the energy use efficiency is improved, and the sudden failure influence is reduced.

The invention relates to the technical field of special valve manufacturing, and discloses a special valve material heat treatment energy-saving process which comprises the following steps: S1, simulating and predicting the structure change of a material in the heat treatment process through a computer; s4, carrying out heat preservation on the material heated to the target temperature; S5, selecting a cooling mode according to the characteristics of the material; S6, improving the energy utilization rate through waste heat recovery equipment; and S7, carrying out heat treatment on the material by an intelligent control system through monitoring the running state of the equipment in real time in the heat treatment process. S8, detecting the quality of the material; and S9, feeding back according to data in the production process. Through the synergistic effect of electromagnetic induction heating, waste heat recovery and an intelligent control system, energy consumption can be greatly reduced, dependence on external energy is reduced, and the energy use efficiency of the whole heat treatment process is optimized.

According to the intelligent stadium energy efficiency management system provided by the invention, activity data in the stadium are accurately captured through the real-time monitoring module, air conditioning and illumination are automatically adjusted by using the environment self-adaptive adjustment module, and optimization of energy consumption is realized. The system adopts an advanced sensor network, a data processing algorithm and a machine learning model, including ARIMA and LSTM, to predict an energy consumption trend and formulate an intelligent scheduling strategy. In addition, the system comprises functions of dynamic data acquisition frequency adjustment and energy consumption monitoring and recording, and a user-friendly interface control and intelligent energy-saving unit. Green energy integration and carbon footprint estimation enhance the environmental protection characteristics of the system. The system not only improves the energy use efficiency, but also supports the goals of sustainability and environmental protection.

Aspects and embodiments disclosed herein include a method of performing an adaptive calculation of power usage efficiency (PUE) of a data center. The method comprises receiving a first input defining quantities and types of equipment in the data center, determining an estimated PUE and a first measure of uncertainty in the estimated PUE from the first input using a range of possible values associated with parameters of one or more items of equipment in the data center, receiving a second input comprising at least one value of one or more parameters of one or more items of equipment in the data center, refining the estimated PUE and determining a second measure of uncertainty in the estimated PUE based on the second input, and presenting the refined PUE and the second measure of uncertainty in the refined PUE to a user.

The invention discloses a network transmission dynamic power distribution method and system for a hydrogen-electricity hybrid unmanned aerial vehicle, and belongs to the technical field of hydrogen-electricity hybrid unmanned aerial vehicles, and the method comprises the steps: carrying out the collection and preprocessing of the state monitoring real-time data of the hydrogen-electricity hybrid unmanned aerial vehicle; analyzing the state monitoring characteristic data of the hydrogen-electricity hybrid unmanned aerial vehicle, and determining a network transmission dynamic power distribution scheme of the hydrogen-electricity hybrid unmanned aerial vehicle; and adjusting and distributing the network transmission dynamic power of the hydrogen-electricity hybrid unmanned aerial vehicle to optimize energy utilization and communication performance. The problems that the network transmission dynamic power of the hydrogen-electricity hybrid unmanned aerial vehicle cannot be effectively distributed in the prior art, the energy use efficiency is low, and the stability and safety of data transmission cannot be ensured are solved. According to the invention, the network transmission dynamic power of the hydrogen-electricity hybrid unmanned aerial vehicle can be effectively distributed, the energy use efficiency of the hydrogen-electricity hybrid unmanned aerial vehicle is improved, the stability and safety of data transmission can be ensured, and the communication quality is improved.

This invention belongs to the field of power system control technology. It discloses a system and method for the linkage between sub-core load adaptation and main core power replenishment in a dual-core fused power supply. The system includes: calculating the main core's instantaneous power replenishment margin based on the current state of charge of the main core management unit and the real-time available output capacity of the main core converter; sending a low-voltage detection pulse sequence to the sub-cores and simultaneously acquiring the returned damping attenuation waveform and instantaneous leakage current value; determining a dynamic access threshold, comparing the current real-time load power with the dynamic access threshold, and generating a continuous load adaptation tag and a sub-core locking identifier; establishing a sub-core priority power replenishment sequence, and immediately tightening the access conditions of the sub-core priority power replenishment sequence when the instantaneous power replenishment margin is lower than a preset warning value; activating a fast power replenishment channel for sub-cores that meet the preset power replenishment triggering conditions, while simultaneously performing dynamic power backtracking on unlocked sub-cores until the main core's instantaneous power replenishment margin recovers to or above the safety threshold; significantly improving energy efficiency.

The present application relates to a kind of method and device for dynamically adjusting computer room environment energy consumption, belong to adjusting energy consumption technical field, the method includes: collecting computer room environment energy consumption data, set this monitoring energy consumption initial threshold;According to energy consumption data, early warning and generating visual report;If the actual energy consumption in visual report this time is higher than historical average baseline and prediction rise and fall probability trend value is consistent, energy use efficiency value, energy use efficiency prediction value and difference are obtained, according to the size of difference, optimize this monitoring energy consumption initial threshold;When computer room environment energy consumption data is abnormal, locate current submodule, generate data set and mark by correlation relationship mining;The similarity of data set is calculated, set next monitoring energy consumption initial threshold;Determine optimal threshold;Optimal configuration threshold is generated under the computer room environment energy consumption data of optimal configuration threshold is stored, compare the computer room environment energy consumption data generated under optimal threshold and the computer room environment energy consumption data generated under monitoring energy consumption initial threshold.

This invention discloses a server configured with a dual-carbon management mode, comprising: a governance module, a management module, a carbon emission accounting module, a security module, an energy-saving module, and a data analysis module. The governance module is used for dynamically analyzing and evaluating Power Usage Effectiveness (PUE). The management module is used for collecting energy and material flows, performing intelligent analysis, refined management, and system optimization. The carbon emission accounting module is used for performing carbon emission accounting and generating carbon asset management solutions based on the accounting results. The security module is used to improve the underlying IT / OT application infrastructure. The energy-saving module is used to acquire power consumption data of server nodes and generate corresponding energy-saving strategies based on the power consumption data. The data analysis module is used to provide intelligent decision support for the planning, deployment, and operation and maintenance of data center clusters. This addresses carbon emission compliance requirements, ensures improved lifecycles for data center equipment, and meets data center requirements such as fundamentality, continuity, reliability, stability, security, and energy efficiency.

The invention relates to a smart energy digital management system applied to a cloud platform, in particular to the field of energy digital management, and specifically comprises a data acquisition module, a data storage and preprocessing module, a data analysis and optimization module and an energy management decision and scheduling module. According to the system, technologies of Internet of Things, big data analysis, artificial intelligence and the like are combined, comprehensive digital monitoring and intelligent optimization management of enterprise energy are realized through real-time acquisition and processing of various energy data, and the energy management efficiency is improved through integration of advanced data mining, trend prediction and optimization scheduling algorithms. The method helps enterprises to reduce energy consumption, improves energy use efficiency, and optimizes energy management strategies.

The embodiment of the invention provides a temperature control strategy determination method and system, computing equipment and a storage medium, and the temperature control strategy determination method comprises the steps: determining service equipment parameters of service equipment deployed in a target machine room, and temperature control equipment parameters of temperature control equipment; under the condition of determining that the target machine room meets a preset test condition according to the service equipment parameters and the temperature control equipment parameters, performing energy consumption test on the service equipment and the temperature control equipment according to a preset temperature interval to obtain an energy consumption test result corresponding to each temperature test node in the preset temperature interval; and determining a temperature control strategy corresponding to the target machine room according to the energy consumption test result corresponding to each temperature test node. The temperature of the target machine room is subsequently controlled according to the temperature control strategy, the energy consumption of the target machine room is further reduced, and the energy use efficiency of the target machine room is improved.

The invention discloses a charging pile ultra-low energy consumption control method, system and device based on AI regulation and control and a medium, belongs to the technical field of charging piles, and aims to solve the technical problem of how to adjust charging power and improve the energy use efficiency of the charging pile. According to the technical scheme, the method comprises the steps that a power grid load historical data table, meteorological data and dates are obtained through a charging operation platform, current power configuration and temperature data are collected through a sensor of a charging pile, then data related to the charging pile are collected, the collected data related to the charging pile are circularly processed, and the data are stored in a storage battery. And obtaining the overall network parameters, predicting the charging power load through the overall network parameters, and then adjusting the charging power of the charging pile in real time.

The invention relates to the technical field of camera equipment control, in particular to a camera power consumption control method and system.According to the camera power consumption control method and system, importance evaluation is conducted on the power consumption influence of an image dynamic area through a graph neural network, so that power consumption of all modules in the image collection process is accurately adjusted, and accurate calculation of the scene change rate is achieved; the power consumption of the camera in different scenes is optimized, the change trend and the power consumption demand of future image acquisition are predicted through the long-short-term memory network, more accurate power consumption control and adaptive adjustment capability are provided for the camera equipment, the effects of prolonging the endurance of the equipment, improving the response efficiency and reducing redundant data processing are achieved, and the method is suitable for large-scale popularization and application. Through combination of future power consumption mode prediction and actual requirements of equipment, the camera can be intelligently switched between a high-performance mode and a low-power consumption mode, the energy utilization efficiency of the equipment is optimized, and higher flexibility and energy utilization efficiency are achieved.

The embodiment of the invention discloses a maximum demand control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the real-time gas reserves and real-time demand power of a target power grid system; under the condition that the real-time gas reserves and the real-time demand power meet a preset control condition, determining a corresponding generation power adjustment amount; and based on the generated power adjustment amount, adjusting the real-time generated power of the gas generator in the target power grid system so as to realize the control process of the maximum demand of the target power grid system. According to the technical scheme, the problem that in the prior art, the maximum demand of a power grid system cannot be effectively controlled is solved, the generated power can be dynamically adjusted according to the real-time gas reserves and the real-time demand power so as to control the maximum demand to be within a certain range, the energy use efficiency can be improved, the power utilization cost can be reduced, and the electric charge expenditure can be reduced.

The invention provides a smart park power management system based on the Internet, and relates to the technical field of power management, and the system comprises a data collection module which is used for collecting the operation data of power related equipment in a park; the data management module is used for processing and storing the acquired operation data; the data analysis module is used for analyzing the operation data through a machine learning algorithm; the intelligent power dispatching module is used for intelligently adjusting power distribution according to the analysis result; according to the invention, through Internet connection, real-time monitoring of power equipment, environmental factors and power consumption of the park is realized, so that through cooperation of real-time monitoring and intelligent scheduling, unnecessary energy waste is avoided, the energy consumption of the park is reduced, the energy use efficiency is improved, the problem of blind scheduling in a traditional method is avoided, and the energy utilization rate of the park is improved. And the power distribution is more accurate and flexible.

The invention belongs to the technical field of power systems, and relates to an operation risk prediction analysis method under power distribution network fault and abnormal working conditions, which comprises a multi-dimensional data acquisition module used for collecting multi-aspect information of a power distribution network, and a dynamic fault diagnosis module used for constructing a three-dimensional fault feature space and performing intelligent diagnosis according to fault features. The operation state prediction module carries out prediction and life evaluation on the change of each part of the equipment according to the fault feature information and sends a starting instruction to the risk quantitative evaluation module, and the risk quantitative evaluation module is used for calculating the possibility of danger occurrence and evaluating the risk of each part of the equipment. The self-adaptive decision-making module is used for automatically formulating a response scheme according to fault assessment information, and the knowledge evolution module is connected with a communication network, and can perform case library construction and automatic learning experience. The system realizes full-closed-loop intelligence of perception, diagnosis, prediction, assessment, decision-making and evolution through modular design; the system can reduce the power failure time and improve the energy use efficiency.

The present invention discloses a method for dynamically adjusting the number of receiving antennas of a terminal device and a communication terminal device. The method includes: the terminal device obtains current status data, where the status data includes the application scenario of the application programs currently running in the foreground of the terminal device, the interaction frequency with the user in this application scenario, and the current communication signal status; processes the status data through a prediction model to obtain a prediction result, where the prediction result includes the number of antennas adapted to the current state of the terminal device; and adjusts the number of receiving antennas of the terminal device currently in the working state according to the prediction result. The above dynamic adjustment method of the present invention, on the premise of ensuring communication quality, intelligently optimizes the antenna configuration according to the actual application scenario and interaction frequency of the user, thereby significantly reducing the power consumption of the terminal device, improving the energy use efficiency, effectively improving the stability and response speed of communication at the same time, and optimizing the user experience in high-demand application scenarios.

The invention discloses a multi-node energy cooperative scheduling method for a high-speed rail line, and relates to the technical field of high-speed rail transportation, and the method comprises the following steps: real-time monitoring and data collection; constructing an energy consumption model and predicting a load; optimization design of an energy scheduling strategy; scheduling and real-time monitoring are implemented; aiming at the energy management and scheduling problems among multiple nodes in the high-speed rail line, the invention aims to optimize the overall energy use, improve the efficiency and reduce the energy waste and environmental pollution, and compared with the traditional energy scheduling method, the energy scheduling method has the advantages that by comprehensively considering the synergistic effect and time dynamic change among the multiple nodes, the energy scheduling efficiency is improved, and the energy scheduling efficiency is improved. Therefore, efficient and intelligent energy management can be realized under the support of information technology and intelligent analysis, closed-loop management from data collection to evaluation feedback is realized, intelligentization and automation are realized by means of computer technology, finally, the energy utilization efficiency is improved, carbon emission is reduced, and the operation stability and economy of a high-speed rail system are improved.

The application discloses a construction method and system of an asset value model in a digital twin engine, characterized in that the method comprises the following steps: step 1, measuring the importance of business based on a business model of building energy consumption assets, and constructing a first asset value model according to the business importance; step 2, establishing a correlation between the building energy consumption assets and productivity, and constructing a second asset value model according to the productivity loss caused by the building energy consumption assets; and step 3, calculating the energy use efficiency of the building according to optimal decision variables, and predicting the degradation index of the assets through the energy use efficiency, the first asset value model and the second asset value model to generate a maintenance scheme of the assets. The application has a clever concept and excellent effect, and can realize accurate evaluation of asset value by establishing the correlation between the assets and different businesses and different efficiencies, so that the optimal and most accurate asset maintenance scheme is generated.

A computer-implemented method, system, and computer program product for estimating a carbon footprint of an incoming workload to be hosted on a cloud data center. Trained first and second machine learning models are used in combination to estimate the energy consumption for the incoming workload to be hosted on the cloud data center based on the active energy consumption and the idle energy consumption predicted by the trained first and second machine learning models. Upon estimating the energy consumption for the incoming workload, the carbon footprint for the incoming workload is estimated based on the estimated energy consumption for the incoming workload as well as the power usage effectiveness of the incoming workload and the carbon intensity of the incoming workload. In this manner, carbon emissions attributable to workloads to be deployed to a data center (e.g., cloud data center) prior to deployment may be estimated.

The present invention discloses a method and system for allocating dynamic power for network transmission of a hydrogen-electric hybrid UAV, which belongs to the technical field of hydrogen-electric hybrid UAVs, and includes: collecting and preprocessing real-time data of status monitoring of a hydrogen-electric hybrid UAV; analyzing characteristic data of status monitoring of a hydrogen-electric hybrid UAV to determine a scheme for allocating dynamic power for network transmission of a hydrogen-electric hybrid UAV; and adjusting and allocating the dynamic power for network transmission of a hydrogen-electric hybrid UAV to optimize energy utilization and communication performance. The present invention solves the problem that the existing method cannot effectively allocate the dynamic power for network transmission of a hydrogen-electric hybrid UAV, resulting in low energy utilization efficiency and an inability to ensure the stability and security of data transmission. The present invention can effectively allocate the dynamic power for network transmission of a hydrogen-electric hybrid UAV, improve the energy utilization efficiency of a hydrogen-electric hybrid UAV, and ensure the stability and security of data transmission, thereby improving communication quality.

The invention discloses an electric vehicle charging station load prediction and schedulable resource aggregation method and system based on a CNN-TSMIX model, and relates to the field of electric vehicle charging station energy management. Load data of a plurality of charging stations are collected, and data preprocessing is carried out; training a global model by using the preprocessed data; training a local model suitable for each charging station by finely adjusting global model parameters; predicting the power of each time point under each charging station and the number of various automobiles at each time point by using the established local model in a rolling updating mode; and establishing a consumer psychological model, evaluating the flexible resource adjustment potential of each charging station by using the obtained predicted value, and aggregating the flexible resource adjustment potential into a virtual power plant according to a rule. According to the invention, high-precision prediction of the load of the charging station is realized. Moreover, the energy use efficiency of the charging station is optimized, and a virtual power plant is formed to provide services for a power grid on the premise that the charging experience of an electric vehicle user is not affected.

The invention discloses a method and a system for evaluating the adjustable and controllable resource capacity of a distribution transformer district based on a multilayer clustering strategy, and relates to the technical field of power demand side management. The method comprises the following steps: carrying out modeling on adjustable resources of a distribution transformer area by adopting an equivalent thermal parameter model, implementing multi-layer clustering, carrying out multiple control on an adjustable resource cluster formed after the multi-layer clustering, judging whether three-phase loads of the distribution transformer area are balanced or not, and if so, executing a three-phase load balance control scheme, if not, executing a three-phase load imbalance control scheme; and according to the result of the control scheme, constructing an adjustable resource capacity evaluation model in combination with the user comfort, the user intention and the user controllability, and evaluating the adjustable capacity of the adjustable resource. Through the method and the system provided by the invention, the regulation capability and the stability of the power grid are improved, the balance between the user demand and the power grid efficiency is met, the energy use efficiency is improved to the maximum extent, and the reduction of the energy consumption is facilitated.

The present invention relates to the field of smart grid technology, and specifically to a multi-level collaborative optimization control method for virtual power plants based on digital twins. The present invention establishes a digital twin model through real-time data collection and processing of the power generation end, the power storage end, the power transmission end, and the load end, and can accurately model and monitor the various equipment of the virtual power plant in real time. Through real-time optimization scheduling and collaborative control, it is possible not only to minimize energy loss, but also to balance supply and demand, maximize energy efficiency, and reduce costs; combining genetic algorithms and particle swarm optimization (PSO) algorithms, scheduling optimization is performed based on a multi-level collaborative optimization model, so that the virtual power plant can respond quickly according to changes in the real-time environment and load demand. In the process of power generation equipment scheduling, priority is given to reducing fossil energy and increasing clean energy, which helps to reduce greenhouse gas emissions.

The invention discloses a distributed energy management method for optimizing energy distribution and fine regulation and control, and the method comprises the steps: collecting real-time load data and energy utilization equipment operation state data, and obtaining a real-time data report; based on the real-time data report and the prediction demand data, integrating the user side energy storage device to obtain energy storage device state data; generating a charging and discharging control strategy based on the real-time report and the state data of the energy storage device; executing the charging and discharging control strategy, and updating the real-time data report and the state data of the energy storage device; and based on the updated real-time data report and the state data of the energy storage device, generating a monitoring report and a graphical interface, and monitoring and managing the distributed energy system. According to the invention, by monitoring the energy demand of a high-power customer in real time and combining the user-side energy storage device and a data analysis and prediction algorithm, dynamic balance of power supply and demand is realized. According to the invention, the energy utilization efficiency is improved, and the stability of the system during high-power output is ensured.

The invention provides a post-curing energy-saving system and a post-curing energy-saving method for a wind power blade. The post-curing energy-saving system for the wind power blade comprises a wireless temperature sensor and a state monitoring device, and the wireless temperature sensor and the state monitoring device are used for collecting multi-point temperature data Ti (i = 1, 2, 3,..., n) of a wind power blade mold and the state Sh of a mold heating system and transmitting the multi-point temperature data Ti (i = 1, 2, 3,..., n) and the state Sh to a central processing unit. According to the post-curing energy-saving system for the wind power blade and the method of the post-curing energy-saving system, real-time acquisition and transmission of multi-point temperatures of a wind power blade mold are realized by introducing a wireless temperature sensor and a state monitoring device, and high efficiency and reliability of data transmission are ensured by utilizing a LoRa low-power protocol. The central processing unit adopts a process logic control model and a dynamic energy efficiency optimization model, dynamically adjusts the working parameters of the heating and vacuum system according to the real-time temperature and the equipment operation state, and optimizes the energy use efficiency and the temperature distribution uniformity in the curing process.