239results about How to "Realize a reasonable distribution" patented technology

The invention relates to a temperature control method, device and equipment of semiconductor equipment and a storage medium. The method comprises the following steps: performing finite element mesh division and modal coordinate transformation on a plurality of temperature control areas of semiconductor equipment to obtain a temperature control area dynamic model; collecting measurement data of a temperature sensor and performing time-frequency domain transformation to generate a temperature prediction data sequence; gaussian mixture modeling is carried out to obtain prior distribution, and temperature parameter probability distribution is obtained; performing sub-domain division on the temperature control range to obtain a temperature inter-domain feature mapping matrix; and a temperature interval thermal coupling coefficient matrix is calculated based on the temperature control area dynamic model, a temperature control objective function is constructed in combination with the temperature prediction data sequence and the temperature inter-domain feature mapping matrix, and a temperature control quantity sequence is solved by adopting a model prediction control algorithm. According to the invention, the accuracy and dynamic response speed of temperature control are improved, and the stability of temperature control and the energy utilization efficiency are ensured.

The invention discloses an operation and maintenance mode analysis model generation method and system, a medium and a program product, and relates to the field of electric digital data processing. Establishing an equipment basic life model based on the equipment fault time data and the non-fault working time data; calculating an environment correction coefficient according to the equipment operation environment parameter data and a preset environment influence factor, and combining the environment correction coefficient with the basic life model to obtain a corrected life prediction model; calculating the correlation degree of each equipment node based on the equipment topological structure, and constructing an equipment importance evaluation model; calculating equipment maintenance cost, replacement cost and shutdown loss according to a prediction result of the corrected life prediction model to obtain a cost loss model; and carrying out weighted fusion on an output result of the equipment importance evaluation model and a calculation result of the cost loss model to generate an operation and maintenance mode analysis model. The scientificity and accuracy of operation and maintenance mode analysis are improved.

The invention discloses a charging pile dynamic power scheduling method, and relates to the technical field of charging pile power scheduling, and the method specifically comprises the following steps: obtaining node state information of each remaining normal node in real time, and generating a remaining bearing capability index and a load balancing index of each normal node; and constructing a power distribution evaluation model for the generated residual bearing capacity index and the load balancing index of each normal node, generating a power distribution evaluation coefficient of each normal node, analyzing the power distribution evaluation coefficient after generation, and evaluating the power bearing capacity of each normal node. Dividing each normal node into a high-bearing node, a middle-bearing node and a low-bearing node according to an evaluation result; and respectively adopting a corresponding power scheduling strategy according to a division result of each normal node. According to the invention, the problem that the power resource cannot be quickly and efficiently allocated after the fault node exits is solved, and efficient and stable dynamic balanced power scheduling and system operation are realized.

The invention relates to the technical field of lithium battery temperature regulation and control, in particular to a temperature regulation method and system based on an embedded lithium battery. According to the invention, accurate real-time monitoring and dynamic regulation and control of the temperature of the lithium battery are realized by constructing the temperature regulation and control sensing model and the self-adaptive power management system. And a temperature propagation model, a dynamic battery model and a compensation factor are introduced, so that the temperature prediction precision and the system response capability are improved. A comprehensive thermal state evaluation function and an innovative temperature adjusting method are adopted, and comprehensive thermal state evaluation and flexible temperature control are achieved. And the multi-level temperature risk management ensures the safety of the system. The multi-layer and multi-angle temperature control strategy obviously prolongs the service life, safety and performance of the battery, and provides a reliable and efficient energy solution for equipment such as electric automobiles.

The invention discloses an intelligent filling system and method based on a large model, and the method comprises the steps: collecting the current call content, converting the call content into a text through a voice recognition large model, and obtaining a call text; carrying out preprocessing operation on the call text, and carrying out data analysis on the preprocessed call text by utilizing a text analysis large model to obtain an analysis result; making a plurality of work order templates, forming a work order database, and matching the analysis result with the work order database to obtain a current work order template; automatically filling an analysis result into a current work order template; and obtaining the emergency degree of the current service according to the analysis result, updating the emergency degree to the current work order template, and obtaining the service work order of the current call. The invention relates to the technical field of customer service, and solves the technical problems of single application scene and low intelligent degree in the existing intelligent form filling method.

The invention relates to the technical field of power systems, in particular to an indirect carbon emission method considering new energy and green power transactions, which comprises the following steps: S1, load flow calculation and network loss consideration: determining load flow distribution and forming a matrix required by calculation; s2, unit carbon emission intensity calculation and node carbon potential determination: determining the carbon emission property and intensity of the connected unit; s3, carbon emission responsibility affiliation and green power transaction allocation: distinguishing carbon emission responsibilities of contract power and non-contract power; s4, carbon emission property right distribution and time weighted calculation: after obtaining the carbon emission flow rate allocated by the power supply and the load of each unit, multiplying the carbon emission flow rate by a preset time scale to obtain the carbon emission property right limit of each part of each power grid; according to the method, the accuracy of carbon emission accounting is improved, the definition of the carbon emission distribution characteristics of the power system is enhanced, and the method is suitable for analysis of a lossy network and carbon emission management.

The invention discloses a collaborative optimization decision-making method for an active power distribution network including production and consumer renting shared energy storage, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a multi-agent collaborative transaction architecture containing energy storage shared by diasters; based on the multi-agent collaborative transaction architecture, through a multi-agent two-stage collaborative pricing strategy, the bargaining ability of a shared energy storage operator and a producer and a consumer is quantified; taking social benefit maximization and cooperative benefit distribution maximization as targets, and based on preset constraint conditions, constructing a multi-agent collaborative optimization model based on improved Nash bargaining; and optimizing the multi-agent collaborative optimization model in combination with bargaining capabilities of a shared energy storage operator and a producer and a consumer to obtain an optimal energy transaction volume and an optimal energy transaction price. The DDS-production and consumer-SESO collaborative decision-making framework provided by the invention can realize multi-agent cooperation and win-win, and reduce the scheduling difficulty of the power distribution network; the problems of non-uniform income distribution and high optimization scheduling difficulty in the existing electricity market transaction are solved.

The invention belongs to the technical field of task scheduling, and provides an artificial intelligence task scheduling method based on deep learning, which comprises the following steps: analyzing and processing a task by using a deep learning algorithm, performing deep learning and processing on the task based on performance data to obtain a task evaluation value, grading the task according to the task evaluation value, and performing task scheduling. The method comprises the following steps: acquiring task data based on comparison of a task evaluation value and a task evaluation standard value, acquiring a task completion value and an advanced task completion value based on the task data, processing to obtain an advanced performance value, comparing the advanced performance value with an advanced performance threshold value, marking a worker according to a comparison result, and marking the worker according to the comparison of the advanced performance value. And comparing the number of the high-level tasks with the number of the high-level personnel to obtain an optimized scheduling value, carrying out combined analysis on the optimized scheduling value YH and the high-level performance value BX to obtain an optimized scheduling coefficient, redistributing the high-level tasks according to the optimized scheduling coefficient, and establishing a redistribution task library.

The invention discloses an energy storage regulation and control method and system for a virtual power plant, and the method comprises the following steps: S1, data collection: a metering sensor and metering equipment are installed at DERs to collect the data of the generating capacity, the energy storage state and the load state, the DERs are various distributed energy equipment or systems forming the virtual power plant, and the DERs are distributed energy equipment or systems forming the virtual power plant; a wired or wireless communication network is configured to connect the metering sensor and the metering device to ensure that data can be transmitted from the DERs to the central control system. The method has the advantages of high efficiency and stability, and solves the problems that in the energy storage regulation process of an existing virtual power plant, efficient and reliable energy storage regulation is inconvenient to carry out, unbalance of power regulation is easily caused, maximization of economic benefits and environmental protection benefits are affected, and stability of energy storage regulation is reduced.

The invention provides a PLC-based dosing control system for sewage treatment. The system comprises a power supply module, a data acquisition module, an Internet of Things communication module, a processor module, a dosing control module and a central control system module which are connected in sequence, wherein the data acquisition module is used for acquiring the water inflow of sewage and various water quality parameters in the sewage treatment process and the sewage discharge process; the Internet of Things communication module is used for realizing communication interaction of the dosing control system; the processor module is used for analyzing the water inflow and various water quality parameters based on the PLC, and calculating the dosing amount and adjusting the dosing control parameters in cooperation with the dosing control module according to the analysis result; and the central control system module is used for dynamically displaying sewage treatment related data and interacting with workers. According to the system, the overall operation efficiency of the dosing system is improved, the operation difficulty of operators is reduced, and the dosing accuracy and convenience during sewage treatment are improved.

The invention discloses a network flow directional transmission optimization method, and relates to the technical field of data transmission. Comprising the following steps: establishing a regional priority model based on regional distribution, dynamically adjusting a network link, deploying multi-level cache nodes, determining isolation and multiplexing rules of data traffic, coordinating network resource allocation, establishing a feedback mechanism, and constructing an energy-aware transmission scheduling strategy. According to the network traffic directional transmission optimization method, the flexibility and the accuracy of network traffic scheduling are remarkably improved by setting the combination of a regional priority model and a dynamic link reconstruction mechanism, the transmission efficiency of key service traffic can be guaranteed preferentially, and the network traffic directional transmission optimization method can be used for optimizing the network traffic through real-time link optimization and load prediction. And performance reduction caused by network congestion is avoided. And meanwhile, the low-priority flow is effectively managed, and the reasonable distribution of global network resources is realized. In practical application, the method can greatly reduce the network delay, improve the bandwidth utilization rate and improve the user experience.

The invention belongs to the technical field of power grid load prediction, and particularly provides a micro-grid load prediction method and system based on big data, and the method comprises the steps: collecting micro-grid multi-source data of a preset service area through employing an Internet of Things sensor, carrying out the denoising cleaning of the micro-grid multi-source data, and obtaining the processed multi-source data; performing data-level fusion on the processed multi-source data to obtain fused data; performing multi-scale decomposition on the fused data based on an empirical mode decomposition method combined with multi-scale permutation entropy to obtain a multi-scale sub-data sequence; based on the multi-scale sub-data sequence, constructing a micro-grid load prediction model by adopting a time convolution and time sequence mode attention mechanism combined algorithm; and on the basis of the micro-grid load prediction model, predicting a micro-grid load to be predicted to obtain a load prediction result. According to the technical scheme, the accuracy, robustness and calculation efficiency of load prediction can be remarkably improved.

The invention discloses a low-altitude airspace resource allocation method and device and a storage medium, belongs to the technical field of airspace management, and is used for solving the problem of non-uniform low-altitude airspace resource allocation. The method comprises the steps that grid division is carried out on a low-altitude airspace to obtain airspace grids, the use rate corresponding to the airspace grids is calculated, the use rate is used for indicating the use cost of the airspace grids in unit time, the air use cost corresponding to the air use activity of the aircraft is calculated according to the use rate, and the air use activity of the aircraft is calculated according to the air use cost. The air use activity at least comprises an air use route, the air use route is composed of a plurality of airspace grids, and when the air use cost is higher than a preset cost, the airspace grids in the air use route are adjusted according to the use rate corresponding to the airspace grids so as to allocate a new air use route for the aircraft. According to the scheme, balanced allocation of low-altitude airspace resources can be realized.

The invention provides a motor, a power assembly and a vehicle, and relates to the field of electric vehicles, and the motor comprises a motor shell and a plurality of stator cores. And the winding slots of each stator core are arranged along the circumferential direction of the motor. The plurality of stator iron cores comprise two first stator iron cores and a second stator iron core, and the second stator iron core is arranged between the two first stator iron cores along the axial direction of the motor. The second stator iron core, the two first stator iron cores and the motor shell form a first gap. One opening of one first through hole and one opening of the other first through hole in each first stator iron core in the axial direction of the motor are exposed in the first gap, and the maximum distance between one first through hole and the axis of the motor in the radial direction of the motor is smaller than the minimum distance between the other first through hole and the axis of the motor. The cooling efficiency of the motor can be improved by adjusting the flow channels of the stator cores, and normal running of a vehicle is ensured.

The invention, which relates to the technical field of agricultural management, discloses a digital ecological agriculture integrated production and supply coordination system comprising a model establishment unit, a crop growth monitoring unit, a fertilizer reserve prediction unit, a reserve control unit and a supply management unit. According to the invention, plant growth data over the years is obtained through the model establishment unit to draw a historical average seedling height growth cycle curve graph, a reference is provided for subsequent crop growth monitoring, and the crop growth progress is monitored in real time through the crop growth monitoring unit. A fertilizer reserve prediction unit predicts a fertilization node of a current crop according to a historical fertilization node and an environmental influence factor, and then a reserve control unit monitors the fluctuation of a fertilizer price according to the predicted fertilization node and selects a proper price to purchase a required fertilizer in a purchase monitoring period. Therefore, the efficiency and sustainability of agricultural production are improved, and reasonable distribution of funds is realized.

The invention relates to the technical field of geographic information survey, and particularly discloses a geographic information survey calibration method and system, and the method specifically comprises the following steps: 1, obtaining survey data of a to-be-surveyed geographic region, and carrying out the analysis and calculation to obtain a change judgment value; 2, comparing the change judgment value with a change judgment threshold value to obtain a large change degree sub-region; and step 3, based on the obtained large-change-degree sub-region, obtaining survey change information of the large-change-degree sub-region of the historical data, obtaining a change frequency ratio in the historical data, and comparing the change frequency ratio with a change frequency ratio threshold to generate a high-change-frequency signal. By optimizing the surveying period and integrating the surveying area, reasonable allocation of surveying and calibrating resources, improvement of surveying efficiency and calibration and guarantee of surveying accuracy are achieved.

The invention discloses an embedded firmware audio data modification method and device, a medium and computer equipment, and relates to the technical field of audio processing, and the method comprises the steps: carrying out the correlation analysis of adjacent audio frames in a second-level cache, carrying out the coding processing, and storing the coded audio frames in a third-level cache; dividing the audio frames in the three-level cache into M frames and N frames according to the computing power and bandwidth condition of the embedded device, locally encoding the M frames by the embedded device, and transmitting the N frames to a rear-end device; the back-end equipment receives and processes the N frames of audio signals, and returns a processing result to the embedded equipment; the embedded device combines the locally coded M frames of audio signals and the back-end processed N frames of audio signals according to a time sequence, and outputs complete audio signal data; through timestamp synchronization, interpolation frame supplementation and frame skipping processing, the problems of audio frame loss and discontinuous time sequence are solved, the integrity and continuity of audio signals are ensured, and the audio processing efficiency and quality are remarkably improved.

The invention discloses a stability improvement control method for an offshore wind power low-frequency networking system, and the method comprises the steps: designing a low-frequency power grid stability control module in a land M3C converter control strategy, achieving the reasonable distribution of the power of different M3C converters in an offshore wind power low-frequency AC power grid, and avoiding the overcurrent problem; the inertia of an offshore wind power low-frequency networking system can be improved, the stability of a low-frequency power grid during offshore wind power output fluctuation is improved, and the method has a good application prospect.

The invention relates to the technical field of parking space management, in particular to a parking space management system based on linkage of a video pile and a parking space flat plate lock. The video pile and the parking space flat plate lock are directly arranged on the parking space and used in cooperation with the management platform, the parking mode of an existing parking lot is changed, payment operation needs to be completed firstly when a vehicle drives away from the parking space, and the situation of congestion caused by payment queuing at an exit of the parking lot is avoided; it is ensured that the vehicle cannot leave before payment or parking regulations are met, bad behaviors such as fee evasion are effectively prevented, and the safety of parking management is enhanced; by setting different charging modes, the user can select one or more frequently-going parking lots to open the parking permission according to requirements, and compared with the charging mode of the existing parking lot, the opening cost is distributed according to the parking duration proportion for the user with the high-level member permission, so that the multi-parking-lot use requirement of the user is met, and the user experience is improved. And the reasonable distribution of the cost is realized.

The invention provides a hybrid energy storage capacity optimal configuration method, and relates to the technical field of hybrid energy storage, and the method comprises the steps: building a hybrid energy storage system, obtaining the unbalanced power of the hybrid energy storage system, and determining a preset unbalanced power distribution strategy of the hybrid energy storage system; carrying out power distribution on the unbalanced power according to a preset unbalanced power distribution strategy, obtaining the total reference power of the hybrid energy storage system, carrying out output calibration on the actual output power, and determining the rated power and capacity configuration of the hybrid energy storage system; verifying the validity of the power distribution of the hybrid energy storage system according to the actual operation data of the wind power plant; according to the method, reasonable distribution of the total reference power of the hybrid energy storage system is achieved, the mode component aliasing phenomenon is improved, meanwhile, advantage complementation of multiple types of energy storage characteristics is achieved through the hybrid energy storage system, and the requirements of a power grid for multiple performances are met.

The invention relates to the field of road engineering and civil engineering, in particular to an inverted asphalt pavement structure thickness optimization design method which comprises the following steps: selecting thicknesses and moduli of an asphalt surface layer, a granular material transition layer and a semi-rigid layer as control variables, and taking a granular material layer stress level and surface layer fatigue performance as preliminary design targets; a D-optimal solution response surface design method is adopted to formulate a test scheme, through finite element analysis, in combination with a nonlinear constitutive model, the stress dependence characteristic of the aggregate transition layer is simulated, the influence of a variable combination on a preliminary design target is evaluated, asphalt pavement structure performance checking calculation indexes are compared, and finally an optimal design scheme is determined. According to the invention, the material consumption is effectively reduced, the construction cost is reduced, the fatigue cracking resistance and deformation resistance of the pavement are obviously improved, and the service life of the pavement is prolonged.

The invention provides a large-scale road network parallel microcosmic simulation method, device, equipment and medium, and relates to the technical field of traffic simulation, and the method comprises the steps: carrying out the path flow distribution processing according to road network data and user heterogeneous data, carrying out the iterative calculation through combining with the sensitivity of different users to the average travel time, the travel time standard deviation and the pass cost, and obtaining a path flow distribution result; obtaining a path flow distribution scheme; matrix construction processing is carried out according to the path flow distribution scheme, and a Laplacian matrix is constructed by analyzing connectivity and structural characteristics of a traffic network; road network segmentation processing is carried out according to the eigenvalue and the eigenvector of the Laplacian matrix, and a road network segmentation result is obtained; and performing spatial decomposition processing on the road network segmentation result to obtain a spatial decomposition result, and defining vehicle driving information in a boundary region in the spatial decomposition result to obtain a simulation model. According to the invention, the problem that traffic decision makers cannot be assisted to make scientific decisions in time due to the fact that accurate prediction results cannot be fed back in time in the prior art is solved.

The invention provides a large model content generation auditing method and system based on man-machine collaboration, and relates to the technical field of large models, and the method comprises the steps: deconstructing the generation process data of to-be-audited content, constructing a content risk propagation map, mapping a multi-level auditing strategy library, and recognizing an uncertainty region needing manual intervention. And obtaining manual auditing judgment and correction operation, forming auditing knowledge precipitation and perfecting an auditing strategy library. According to the method, the content risk source can be accurately positioned, the auditing efficiency is improved, a closed-loop optimized man-machine collaborative auditing mechanism is formed, the misjudgment rate is effectively reduced, and the large model content security is improved.

The invention relates to a unit load scheduling method and device, a medium, equipment and a program product, and relates to the technical field of hydraulic engineering, and the method can comprise the steps: determining a plurality of first load distribution schemes corresponding to the unit load of a target unit; determining the fitness corresponding to each first load distribution scheme, wherein the fitness is used for evaluating the advantages and disadvantages of the first load distribution scheme; determining a target load distribution scheme meeting a target constraint condition according to the plurality of first load distribution schemes and the fitness corresponding to each first load distribution scheme; and controlling the target unit to execute the target load distribution scheme. Therefore, the optimal load distribution scheme can be accurately found out from the plurality of first load distribution schemes through a small calculation amount. Meanwhile, under the condition that the target constraint condition is met, reasonable distribution of unit loads can be achieved, and therefore maximum resource utilization of unit operation is achieved, and economic operation is achieved.

The invention discloses an open source component security scheduling method, which comprises the following steps: acquiring a task demand of a target system, and screening an open source component cluster matched with the task demand function from a preset component library according to a semantic similarity algorithm; extracting a risk assessment index of each open source component in each open source component cluster by using a software component analysis tool; inputting the risk assessment index into a pre-trained risk assessment model for prediction to obtain a risk assessment result corresponding to each open source component; constructing a scheduling strategy according to the task demand and the risk assessment result; in the actual scheduling process, the real-time task request is scheduled based on the scheduling strategy. According to the open source component security scheduling method provided by the embodiment of the invention, the open source component is automatically screened and evaluated, so that the time cost of manual screening and testing is reduced.

The invention discloses a multi-cluster container scheduling method based on a mirror image layer in an edge computing environment, which comprises the following steps of: establishing a system model of a multi-cluster edge computing environment, and comprehensively describing communication modes inside and outside clusters, resource constraints of nodes, a dependency relationship of a container mirror image layer and the like; analyzing task scheduling requirements and optimization requirements of mirror image layer downloading, establishing a corresponding mathematical model by combining node computing resource limiting conditions, task scheduling requirements and mirror image downloading time delay which need to be met in an edge computing environment, and proposing a method for optimizing the task scheduling requirements and the mirror image downloading time delay by minimizing container starting delay. A scheduling optimization problem aiming at ensuring resource load balancing between clusters is solved; using a heuristic method to quickly screen out candidate edge nodes, and using linear programming to optimize task allocation; calculating the load balance degree between clusters in the edge computing environment, and adjusting the task allocation condition according to the resource load degree of the edge cluster; and for the mirror image layer downloaded by the edge node, the downloading sequence is optimized by using dynamic planning.

The invention discloses an intelligent cost compilation and auditing method based on artificial intelligence in a communication network, and the method comprises the steps: carrying out the dynamic evaluation of network resources based on a multi-service demand analysis model, generating an optimized resource distribution scheme according to an evaluation result, carrying out the intelligent compilation of cost through a deep learning algorithm, and carrying out the auditing of the cost. Automatically auditing the compiled cost to ensure the rationality and accuracy, and adjusting a resource allocation strategy based on an auditing result; according to the intelligent cost compiling and auditing method based on artificial intelligence in the communication network, the problem that in the prior art, under the condition that multiple service requirements coexist, resource allocation is unbalanced in the method is solved, specifically, when multiple projects or tasks are carried out at the same time, the system cannot effectively process all the requirements due to unreasonable resource allocation, and the system is not easy to carry out. The resource allocation problem may cause reduction of service quality, and further affects the overall efficiency of project cost management and user experience in a communication network construction process.

The invention relates to a virtual power plant optimization scheduling method and system. The method comprises the following steps: establishing an operation model of a multi-type adjustable resource main body; constructing a master-slave game model of the aggregator and the resource main body, wherein the master-slave game model comprises an upper-layer model taking the aggregator as a leader and a lower-layer model taking the resource main body as a follower; solving a master-slave game model of the aggregator and the resource main body to obtain an optimal compensation price strategy and an optimal response power strategy, and constructing an optimal power scheduling plan according to the optimal response power strategy; the optimal power scheduling plan is used for being issued to each adjustable resource main body for execution so as to realize optimal scheduling of the virtual power plant. According to the method, the participation enthusiasm of the resource main body can be stimulated, the user participation degree can be improved, the scheduling instruction execution rate can be improved, the model accuracy can be improved, the cooperative capability of heterogeneous resources can be improved, and the overall operation economy and stability of the virtual power plant can be improved.

The invention provides a distributed fuzzy flexible job shop scheduling method and system based on an MSDBO algorithm, a storage medium and electronic equipment, and relates to the field of flexible job shop scheduling. According to the method, firstly, production resources and workpiece machining tasks are obtained; secondly, representing the machining time of each process of any workpiece on the machine and the transfer time of any workpiece on different machines by adopting a triangular fuzzy number so as to obtain the fuzzy completion time of any workpiece; thirdly, on the basis of the production resources and the workpiece machining tasks, a mathematical model is constructed with the minimum maximum fuzzy completion time as an optimization target; and finally, solving the mathematical model by adopting a multi-strategy dung beetle optimization algorithm to obtain an optimal machine distribution sequence and an optimal workpiece processing sequence. The fuzzy machining time is introduced, the actual production condition is met, meanwhile, production resources of multiple factories are utilized, reasonable distribution of workpieces is achieved, and beneficial help is provided for improving the production efficiency of enterprises.

The invention relates to a combined frequency modulation coordination control method and system for thermal power generating unit control and energy storage state management, computer equipment and a storage medium, and the method comprises the steps: obtaining an AGC instruction PAGC and the active power PACT of a unit at the moment, and calculating a power deviation # imgabs0 #; if the power deviation # imgabs1 # crosses a dead zone, state information of the energy storage system and the thermal power generating unit is collected, parameter information of the thermal power generating unit is calculated, a variable load rate instruction and a unit target power instruction are sent to a coordination control system of the thermal power generating unit, the SOC of each unit of the energy storage system is obtained, and the output power, the energy storage output unit and the output sequence of the energy storage system are calculated; and when the thermal power generating unit reaches the load specified in the AGC instruction, controlling the charging and discharging of the energy storage system. According to the invention, deep coordination control of the thermal power generating unit and the energy storage device is realized, the overall response level of the combined unit is improved, the service life of the energy storage system is prolonged, and the long-term response capability to an AGC instruction is improved.