11346 results about "Power consumption" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention provides a machine room overall electrical scheme configuration method and system for cabinet layout, and relates to the technical field of electrical design, and the method comprises the steps: building a machine room three-dimensional digital model, and carrying out the function partitioning; optimizing the cabinet layout and determining the maximum capacity; constructing an electricity load prediction model; uPS load balancing is carried out by adopting deep reinforcement learning; designing an intelligent bus duct wiring and circuit breaker access scheme; constructing an equipment topological graph and collecting operation parameters; extracting equipment association features based on the graph attention network; and identifying an abnormal power consumption mode and optimizing a regulation and control scheme.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention discloses an intelligent electric meter abnormal electricity utilization detection method and system based on data analysis, and belongs to the technical field of electricity utilization abnormal detection, and the method specifically comprises the steps: obtaining the historical electricity utilization data of a user according to an intelligent electric meter, and carrying out the preprocessing of the obtained historical electricity utilization data of the user, extracting features of the preprocessed historical power consumption data of the user, carrying out three-layer clustering analysis on the extracted features, analyzing historical power consumption behaviors of the user, identifying abnormal behaviors obviously deviating from a normal power consumption mode according to the historical power consumption behaviors of the user, and adaptively adjusting model parameters through a feedback mechanism. The abnormal electricity utilization of the intelligent electric meter is monitored in real time, the method can adapt to the electricity utilization mode change of the user, the dynamic threshold value adjustment can be adaptively adjusted according to the electricity utilization behavior change of the user, and therefore more accurate anomaly detection is achieved.

The invention discloses a low-power-consumption single-emission out-of-order execution RISC-V processor, which comprises seven stages of pipeline architecture, namely an instruction fetch unit, an instruction buffer unit, a decoding unit, an emission unit for single instruction emission, a physical register file, an execution unit for parallel out-of-order calculation and a retirement unit in sequence, and the instruction fetch unit is used as the front end of the RISC-V processor; a five-level pipeline architecture formed by the decoding unit, the transmitting unit, the physical register file, the execution unit and the retirement unit is used as the rear end of the RISC-V processor; through the technical scheme, the invention provides a micro-architecture which is more balanced in performance and power consumption, so that the applicability of a processor core in a scene with high computing requirements and sensitive power consumption is improved.

The invention discloses an operation regulation and control method and system for a distributed new energy photovoltaic power station, and relates to the technical field of new energy, and the method comprises the steps: recognizing a power station, building a monitoring system, collecting and monitoring key equipment data through the system, so as to obtain historical and real-time operation data, and predicting photovoltaic output and load based on the data to obtain predicted values, calculating power deviation, inputting the deviation into an optimal scheduling model to obtain a strategy, and adaptively regulating and controlling the operation state of the power station according to the strategy. The technical problems of unbalanced power generation and power utilization, low operation efficiency, poor stability, high cost and poor power grid compatibility caused by the fact that efficient and self-adaptive optimal scheduling cannot be performed according to accurate photovoltaic output and load prediction in the prior art are solved, accurate balance of power generation and power utilization is achieved, and the power generation efficiency and the power utilization efficiency are improved. The operation efficiency and stability of the power station are remarkably improved, the operation cost is reduced, and the compatibility with a power grid is enhanced.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention relates to a dynamic adjustable multi-core cooperation mechanism and power consumption optimization method and system based on an RISC-V architecture. The method comprises the following steps: 1) initializing a dynamic adjustable multi-core cooperation system; comprising the steps that a hardware module is initialized, and the hardware module comprises a high-performance core P-core, a low-power-consumption core E-core, a hardware scheduling unit HSU and a power management unit PMU; loading a task queue, and marking task characteristics; 2) core state monitoring; comprising the following steps: an HSU monitors a core state; the power management unit PMU monitors voltage and frequency; 3) dynamic task scheduling; comprising the steps that task characteristics are allocated to a high-performance core P-core or a low-power-consumption core E-core; the idle core enters a low-power-consumption mode; and 4) self-adaptive energy saving is realized, and task migration is carried out. And through a dynamic task scheduling and energy-saving optimization algorithm, the flexibility and efficiency of the RISC-V multi-core processor are remarkably improved.

The invention provides a modular power supply system configuration method and system for a micro-module machine room, and relates to the technical field of electric power, and the method comprises the steps: collecting power utilization state data and load distribution data of functional equipment in the micro-module machine room, building a power supply network topology model, dividing a power supply system into a first-stage load, a second-stage load and a third-stage load, an independent power supply loop and a data acquisition unit are respectively configured in the first-stage load, the second-stage load and the third-stage load; a power supply network topology model is updated according to the power utilization data acquired by the data acquisition unit, a reinforcement learning algorithm is adopted to generate an adaptive power distribution strategy, and a modular energy storage unit is arranged to provide a standby power supply for a primary load and a secondary load; and constructing a machine learning fault prediction model based on the power consumption data, and dynamically adjusting a power supply path and a power distribution scheme of a power supply loop.

The invention relates to the technical field of computers, in particular to a temperature control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an estimated temperature of a heat dissipation part based on a pre-established physical model, and obtaining an actual temperature of the heat dissipation part based on the estimated temperature and the actual temperature of the heat dissipation part; judging whether parameters of a pre-established physical model meet preset updating conditions or not; if the parameters of the physical model meet preset updating conditions, the parameters of the pre-established physical model are updated, and based on the updated parameters, regulation and control parameters of a proportional integral differential controller are determined according to a preset objective function, so that the proportional integral differential controller controls the operating temperature of the server based on the regulation and control parameters. Therefore, the problems that in PID regulation and control of the server fan, parameter setting depends on artificial experience, efficiency is low, subjectivity is high, and power consumption, noise and heat dissipation performance cannot be effectively balanced in the prior art are solved, and effective control over the temperature, the power consumption and the noise of the server is achieved.

The invention relates to the technical field of data processing, and discloses a load demand prediction method and system for a distributed new energy power generation system, and the method comprises the steps: obtaining historical data corresponding to power generation equipment and a load cluster in the distributed new energy power generation system, extracting feature parameters from the historical data, and carrying out the clustering analysis, dividing the power generation equipment and the load cluster into different subsets; processing the historical data corresponding to the power generation subsets and the load subsets to obtain a power generation output curve and a power utilization load curve; by analyzing the relevance between historical power generation and consumption data, obtaining a relevance index so as to fuse a power generation output curve and a power consumption load curve into a power generation load fusion model; and a parameter identification method is used to optimize the power abrupt change rate and the load change rate corresponding to the historical power generation data and the historical power utilization data, so that the power generation load fusion model is integrated into a generalized load model structure, a fusion equivalent model is obtained, and the load demand of the distributed new energy power generation system is accurately predicted.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention discloses a user abnormal electricity consumption identification method and system based on spatio-temporal joint modeling, and the method comprises the steps: obtaining electricity consumption monitoring data of a user in a monitoring region, inputting the electricity consumption monitoring data into a preset electricity consumption abnormality detection coupling model, and obtaining an abnormal electricity consumption diagnosis result; the construction process of the electricity consumption anomaly detection coupling model comprises the following steps: coupling a multivariable time sequence correlation learning model, a space-time diagram neural network model and an anomaly diagnosis module to obtain the electricity consumption anomaly detection coupling model; preprocessing the electricity consumption storage data and adding a real electricity consumption label to obtain an electricity consumption training data set; training the power utilization anomaly detection coupling model by using a power utilization training data set to obtain a trained power utilization anomaly detection coupling model; electricity utilization characteristics in time and space dimensions are fully considered; and the accuracy of electricity consumption abnormity detection and diagnosis of the user is improved.

Power flow in a power distribution system is controlled using control commands generated based on values of system parameters. In this regard, generation prediction data, load prediction data, and real-time device status of equipment are utilized to generate scheduling data for power generation and power consumption and a network topology of the system is also obtained. The values of the system parameters are generated as an outcome of an unbalanced load flow analysis of the power distribution system using the scheduling data and the network topology. The unbalanced load flow analysis utilizes a compact multi-bus based nodal admittance model to represent relationships between nodal injected currents and nodal voltages at a plurality of non-overlapped phases of buses for a section formed by zero impedance branch connected with impedance branches in the power distribution system.

The present application discloses a calculation method for multi-scale electric-carbon energy efficiency optimization for a power system, which includes designing a carbon flow calculation improvement model of the power system that takes into account a photovoltaic cluster auxiliary service, converting a power lossy transmission network of the power system equivalent to a lossless power consumption network, and calculating a carbon flow of the power system that takes into account a photovoltaic reactive power cluster auxiliary service; designing a decision-making system for low-carbon operation of the power system based on a coupling of electric-carbon energy efficiency, and calculating a correlation between electric quantity and the carbon flow of the power system that takes into account the photovoltaic cluster auxiliary service; the output varies in the range of 0-1, larger values represent a stronger correlation between electric-carbon energy efficiency.

The invention relates to the technical field of energy dispatching, in particular to a power dispatching optimization method based on optical energy storage, which comprises the following steps: extracting photovoltaic output and charge state fluctuation based on partitions, synchronously identifying abnormal intervals, analyzing direction matching and energy connectivity, screening abnormal units, and extracting linkage units in combination with load and power utilization characteristics. Evaluating power flow deviation, determining an unbalance level, matching a response list, and outputting an automatic linkage scheduling adjustment instruction set. According to the method, photovoltaic output and energy storage state fluctuation are identified in partitions, the accuracy of capturing an abnormal space-time coupling phenomenon is enhanced, the identification depth of scheduling imbalance hidden danger is improved, regional load distribution and power utilization behavior consistency judgment are combined, dynamic load feature evaluation of abnormal units is completed, the abnormal units are scheduled in a layered mode according to risk levels, and the scheduling efficiency is improved. The scheduling range is accurately adjusted, and the response precision and the handling capacity of the scheduling mechanism to the dynamic difference of the load side under new energy fluctuation are improved.

The present application relates to a multi-terminal linkage energy scheduling method and apparatus. The method comprises: acquiring multi-terminal data, wherein the multi-terminal data comprises power supply side data, power grid side data, load side data, and energy storage side data; on the basis of the acquired multi-terminal data, predicting multi-terminal electricity amount related information within a future preset time period by means of a prediction algorithm, wherein the multi-terminal electricity amount related information comprises photovoltaic power generation related information, power grid power generation related information, power consumption information of a load side device, and electricity charging and discharging related information of an energy storage device; on the basis of an energy efficiency target, acquiring energy regulation information by means of the multi-terminal electricity amount related information; and controlling the energy storage device and / or the load side device on the basis of the energy regulation information. According to the solution of the present application, by means of a multi-party linkage mechanism, a scheduling plan formed on the basis of a preset scheduling target is more suitable for actual requirements and more accurate, and the energy storage device and the load side device can be automatically controlled on the basis of the scheduling plan.

The invention discloses an electric energy meter error prediction method and system based on big data analysis, and relates to the technical field of electric power measurement, and the method comprises the steps: firstly reading the device attribute, the voltage fluctuation threshold value and the power utilization load threshold value of a target electric energy meter, dividing parameters into a plurality of voltage fluctuation intervals and load proportion intervals, and constructing a plurality of power utilization scenes; thirdly, obtaining a sample training set based on the power big data by taking device attributes and power consumption scenes as constraints, and training through a BP neural network to obtain a plurality of error predictors; and finally, by establishing an error prediction model, inputting a real-time voltage fluctuation coefficient, an electricity load proportion and an environment factor, and outputting a prediction error coefficient of the electric energy meter. The technical problem that an existing electric energy meter is inaccurate in error prediction in a complex environment is solved, and the technical effect that metering of the electric energy meter is more accurate under different voltage fluctuation, environment conditions and load conditions is achieved.

The invention relates to the technical field of dust collector control, and discloses a multi-motor cooperative speed regulation and energy efficiency optimization control method and device for a dust collector, and the method comprises the steps: monitoring the rotating speed, current, voltage, temperature and other operation parameters of a plurality of motors in real time, and determining the working load state of each motor through a load recognition model. Different cooperative speed regulation strategies are matched according to load states, such as PID control for low loads, fuzzy logic control for medium loads and model prediction control for high loads. A dynamic weight distribution algorithm is used for adjusting the target rotating speed, and an energy efficiency optimization algorithm is used for optimizing power distribution. Meanwhile, environmental parameters are considered, and control parameters are optimized through a self-adaptive parameter adjustment model. And the system also has the functions of adjusting strategies according to load fluctuation and power consumption, motor priority ranking during high load, energy efficiency algorithm parameter optimization, rotating speed adjustment during filter screen blockage, motor state monitoring and prediction and the like, so that accurate speed regulation and efficient energy conservation are realized, and the performance and the intelligent level of the dust collector are improved.

The invention discloses a clock tree synthesis method and related equipment, and the method comprises the steps: dividing a plurality of time sequence units which need to receive clock signals from a clock source into clustering clusters of different levels, and enabling the load capacitance of each clustering cluster of the same level to be balanced; according to a hierarchical sequence of clusters starting from the clock source, taking each cluster as a node of a clock tree, recursively constructing the clock tree, and inserting a target buffer at the position of each node for driving the corresponding node; analyzing the delay and offset of the constructed clock tree to obtain an analysis result; and if the analysis result indicates that the delay and offset of the clock tree do not meet the preset constraint condition, adjusting the clock tree until the adjusted delay and offset of the clock tree meet the preset constraint condition. According to the technical scheme, power consumption and offset can be effectively reduced, calculation efficiency and design reliability are improved, and the method is suitable for high-performance chip design under the advanced technology.

The invention discloses a hybrid energy storage system charging prediction and energy regulation and control method, and relates to the technical field of power systems and energy management, and the method comprises the following steps: obtaining the real-time operation data of an optical storage charging system; by adopting the dynamic regulation and control algorithm and the self-adaptive learning mechanism, the light storage charging system can predict and correct the power change of photovoltaic power generation in real time, the photovoltaic power is utilized to the maximum extent, the power consumption of a power grid is reduced, and the energy utilization efficiency and the economic benefit are improved. Meanwhile, the dynamic charging and discharging strategy based on error correction effectively avoids the problems of overcharging and deep discharging of the battery, prolongs the service life of the battery, and improves the long-term stability and safety of the system. The system optimizes the charging and discharging plan according to the power grid electricity price fluctuation, and combines an early warning mechanism to monitor the battery state in real time, thereby reducing the operation cost, reducing the potential safety hazard, and guaranteeing the efficient and reliable operation in the whole life cycle.

Embodiments of the invention disclose a method, a device and a system for processing a wake-up lock, and belong to the field of computer technology. The method comprises the following steps: detecting whether a wake-up lock is in a working state, and if so, obtaining a wake-up parameter corresponding to the wake-up lock, wherein the wake-up parameter comprises an arithmetic operation corresponding to the wake-up lock and an application program corresponding to the wake-up lock; judging whether the arithmetic operation corresponding to the wake-up lock requires a sensor of a terminal; if not, generating a wake-up transfer request according to the wake-up parameter, sending the wake-up transfer request to a server, and releasing the wake-up lock, wherein the server is used for executing the arithmetic operation corresponding to the wake-up lock; and receiving a wake-up message sent by the server, and waking up the terminal according to the wake-up message. By adopting the method disclosed by the invention, after a display screen of the terminal is closed, the system time occupancy of the application program can be reduced, the power consumption of the terminal is reduced in the case that the display screen of the terminal is closed, and the standby time of the terminal is prolonged.

Embodiments pertain to reducing power consumption in a computing system comprising one or more deterministic processors. A controller generates a plurality of control signals for a voltage regulator to regulate a supply voltage of a respective one of the one or more deterministic processors. A power management module determines an initial profile for power consumption and performance of an algorithm executed on the respective deterministic processor having an initial value for the supply voltage and an initial value for a clock frequency. The power management module further determines a target profile for power consumption and performance of the algorithm executed on the respective deterministic processor. The controller modifies the plurality of control signals based on the initial profile and the target profile. The respective deterministic processor executes the algorithm while the supply voltage is dynamically modified by the voltage regulator based on the modified plurality of control signals.

An air conditioner load monitoring and anomaly detection method and system. The method comprises: acquiring user power consumption data and meteorological data, the user power consumption data and the meteorological data comprising the total power of an electricity meter, the power of an air conditioner, and the outdoor temperature, preprocessing the data, and computing electrical characteristics of the user power consumption data; on the basis of the electrical characteristics, learning by using a two-tower neural network so as to obtain operating rules of the air conditioner, and extracting an air conditioner operation curve; on the basis of the air conditioner operation curve, computing electrical characteristics and energy consumption characteristics of the air conditioner, and performing cluster analysis by using a K-means clustering algorithm; and, on the basis of clustering results, comparing the clustering results with clustering results of collected electrical data of the air conditioner under normal and abnormal operating conditions to determine whether the air conditioner is in an abnormal state, so as to implement detection of anomalies in the operation of the air conditioner, thereby improving the precision of load decomposition of the air conditioner, reducing application costs, providing a decision-making reference for equipment maintenance personnel, and prolonging the service life of the equipment.

The invention belongs to the field of electricity consumption information acquisition, and provides an anomaly detection method and system for redundancy elimination perception multi-dimensional electricity consumption data fusion, and the method comprises the steps: obtaining historical electricity consumption data, carrying out the preprocessing, and obtaining a time sequence feature vector of the historical electricity consumption data through a trained convolution recursion auto-encoder; based on the preprocessed historical power consumption data, a space mapping function is adopted to obtain a basic feature vector; based on the preprocessed historical power consumption data, obtaining an incidence relation feature vector by adopting a trained graph convolutional neural network model; fusing the time sequence feature vector, the basic feature vector and the incidence relation feature vector by adopting a multi-head attention mechanism to obtain a fused representation vector; filtering redundant information in the fusion representation vector by using the trained redundancy elimination fusion model to obtain a comprehensive representation vector of the time sequence power consumption data; and performing anomaly detection according to the comprehensive representation vector of the time sequence power consumption data to obtain a historical power consumption data evaluation result.

Disclosed in the present application are a server, and an aerodynamic-noise and heat-dissipation optimization control system therefor. The system comprises a heat dissipation fan, a temperature measurement module, a noise measurement module, a power consumption measurement module, a controller and a phase-change air guide cover, wherein the aerodynamic area of the heat dissipation fan is adjustable; the temperature measurement module is used for measuring the temperature of a case; the noise measurement module is used for measuring the noise inside the case; the power consumption measurement module is used for acquiring a load requirement of a program currently running in a server; and the controller is used for adjusting the working condition of the heat dissipation fan, analyzing the lowest operating power of each server assembly on the basis of the load requirement when internal noise reaches a preset threshold value, and adjusting the current power of each server assembly on the basis of the lowest operating power. The phase-change air guide cover is used for guiding air and absorbing heat by means of a phase-change material.

Systems and methods for managing the distribution of electrical power are disclosed. The system can maintain a status of each account of a plurality of accounts. Each account can correspond to an electrical power generation device, an electrical power consumption device, and an electrical power transmission device. The system can generate a first record object in response to the generation of a unit of power. The system can identify a first path identifying a source device and a destination device to transmit the unit of power. The system can generate a control signal to route the unit of power from the source device to the destination device. The system can update an event tracker to indicate the routing of the unit of power. The system can generate a second record object corresponding to the destination device. The system can update a second account to include the second record object.

The invention relates to the technical field of communication machine room energy conservation, in particular to a temperature energy-saving control method and system for a communication machine room, and the method comprises the steps: obtaining multi-region temperature and humidity and equipment load data in the machine room, inputting a pre-training model to obtain a cabinet heat dissipation risk level, judging an environment state through combination with an incidence matrix, entering an operation mode if a requirement is satisfied, and entering a control mode if a requirement is satisfied. If not, a local temperature control adjusting mechanism is triggered; the method comprises the following steps: calculating a refrigeration efficiency ratio according to the air volume of ventilation equipment, air conditioner power consumption, real-time electricity price and the like, generating a candidate scheme set by using a dynamic weight multi-target optimization algorithm in combination with factors such as equipment life, and selecting a target scheme through virtual verification; and according to air conditioner waste heat and cabinet heat dissipation and storage heat, a heat energy distribution strategy is dynamically generated by using a deep Q network reinforcement learning algorithm. Therefore, the problems that in the prior art, energy is wasted, cost is high, heat dissipation requirements of all areas are difficult to accurately adapt, the service life and stability of equipment are affected, and fault risks are increased are solved.