236 results about "Empirical data" patented technology

The invention provides a man-machine cooperation method and system based on robot process automation, and relates to the technical field of automation, and the method comprises the steps: building a data perception processing network, collecting a user operation instruction, a system operation state and task environment data, obtaining a task feature vector through semantic analysis, and matching a business model to generate a task processing strategy. A man-machine cooperative processing framework is constructed based on the strategy, a robot unit processes a structured task, a manual operation unit processes an unstructured task and is connected through an information interaction channel, and transmission of a data analysis result and decision suggestions and updating of processing regulation information when deviation exceeds a threshold value are achieved. Besides, a knowledge migration processing network is constructed, business rules and expert experience data features are extracted, a scene migration model is trained, business feature vectors are mapped to a preset scene feature space, and after validity verification, parameters of the data perception processing network and the man-machine cooperative processing framework are updated.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

The invention discloses an efficient medicine logistics order scheduling and distribution method and system, and relates to the technical field of medicine logistics, and the method comprises the steps: constructing a multi-modal medicine feature extraction module, and outputting an order constraint matrix; establishing an adaptive space-time distribution network according to the order constraint matrix, and performing dynamic grid division on a distribution area to generate a multi-level distribution area model; constructing a double-layer heterogeneous graph neural network, and generating a distribution path planning candidate scheme set considering drug characteristics; defining a state space and an action space, constructing a dual-target reward function, and carrying out iterative training to obtain a distribution scheduling strategy model; and aggregating multi-region distribution empirical data by adopting a federated learning framework, and periodically updating the scheduling strategy model based on a differential privacy mechanism. According to the method, the multi-modal drug feature extraction module is constructed, and the image, text and storage condition information of the drug are fused, so that the drug storage requirement and the distribution limitation condition can be identified more accurately, and the identification accuracy of the distribution constraint is improved.

The invention provides a filling process prediction method and device based on a discrete element method and data driving, and relates to the technical field of bulk material forming, and the method combines the physical modeling advantage of the discrete element method and the powerful prediction capability of a data driving method. The filling process of the granular material under different process parameters is simulated through a discrete element method, key quality indexes are obtained to serve as input of a data driving model, and the process parameters serve as output for training. The model not only can accurately predict the filling quality, but also can adjust the process parameters in real time according to the target quality index, so that the intelligent optimization of the filling process is realized. According to the method, the calculation efficiency is greatly improved, the generalization ability of the model is enhanced, the dependence on empirical data is reduced, a more efficient and accurate solution is provided for the filling process in the industries of building materials, pharmacy, powder metallurgy and the like, the product quality is improved, the production cost is reduced, and the research, development and application of novel materials and equipment are accelerated.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

The invention discloses a sewage treatment drug delivery control method based on reinforcement learning and particle swarm optimization, and the method achieves the precise control of turbidity and the optimization of drug cost through the dynamic adjustment of particle swarm parameters and the fusion of a reinforcement learning strategy. According to the technical scheme, the method comprises the following steps: combining a PSO algorithm with a DDPG algorithm to construct a drug control model; designing a reward function; the empirical data with the state coverage rate higher than a first preset threshold value are stored in a full-reservation empirical pool, the empirical data with the current policy access larger than a second preset threshold value are reserved in a first-in first-out mode through a policy empirical pool, and mixed sampling is carried out according to a preset proportion; training a drug control model; and inputting the current turbidity value, the drug delivery amount of the last time step, the water inlet flow, the parameters of the water quality detection camera and the future turbidity value into a trained drug control model, and outputting the current drug delivery amount. Through prediction-control closed-loop integration, a raw water turbidity prediction result is fed back to the optimizer, and the method is suitable for real-time adaptive control of a sewage treatment system.

The invention relates to the technical field of parameter optimization, in particular to an automatic kernel network parameter optimization method, which comprises the steps of constructing an enhanced deep Q network model, and integrating the enhanced deep Q network model with a priority playback buffer area, a meta learning module, a Bayesian optimizer and a neural architecture search module; using performance index data to train an enhanced deep Q network model, the training process including using a priority playback buffer to store and sample empirical data, using a meta-learning module to perform task adaptation, and monitoring training indexes of multiple dimensions to evaluate the convergence state of the model; selecting a kernel parameter adjustment action according to the current state through the trained enhanced deep Q network model; executing the selected kernel parameter adjustment action, and evaluating a parameter adjustment effect based on the multi-target reward function; and updating the enhanced deep Q network model according to an evaluation result, wherein the priority playback buffer area and the Bayesian optimizer are utilized in the updating process.

The invention relates to the technical field of traffic transportation environment engineering and carbon emission evaluation, in particular to a construction method and equipment of a plateau gradient zone vehicle CO2 emission factor calculation model and a medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. And a specific and quantitative design basis is provided for low-carbon highway design. It is revealed for the first time that the CO2 emission rate of the plateau gradient zone has a remarkable altitude segmentation effect through empirical data, and a method for constructing a calculation model in a partition mode is innovatively provided. According to the method, the defects that a traditional single model is poor in adaptability and misaccurate in prediction in a complex plateau environment are overcome, and differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) are established for different altitude intervals; the finally formed partition model provides a core theoretical tool and decision basis for plateau highway carbon emission accurate accounting, low-carbon route optimization and green traffic construction.

The invention relates to the technical field of operation and maintenance of new energy electric field fan equipment, in particular to a fan fault decision-making method based on region, model and knowledge graph dynamic evolution, which comprises the following steps: S1, constructing a maintenance experience database according to historical data, and updating data according to a newly added maintenance work order; s2, carrying out quantitative modeling according to regional features and model features in the maintenance experience database, constructing a feature matrix, and carrying out dynamic updating according to a newly added maintenance work order; s3, constructing a knowledge graph according to the maintenance experience database, fusing the feature matrix and the knowledge graph, and performing dynamic knowledge graph updating through real-time data driving and closed-loop feedback optimization double-introduction; and S4, adopting a self-optimization aided decision, predicting a fault occurrence probability through a reinforcement learning strategy network, and matching a solution in the knowledge graph. The method is suitable for wind generating set fault auxiliary decision making, and the wind power operation management efficiency is greatly improved.

The invention discloses an intelligent agent optimization method and device, equipment, a storage medium and a product, relates to the technical field of artificial intelligence, and discloses a method for recording a task execution track of an intelligent agent in a process of executing a task based on a target large model in the intelligent agent; extracting execution empirical data from the task execution track, wherein the execution empirical data comprises problems existing in the task execution process, generation reasons of the problems and solutions; and generating a target training sample based on the execution experience data, the target training sample being used for training the target large model to optimize the agent. According to the method, the efficiency of obtaining the training sample can be remarkably improved, so that the optimization efficiency of the intelligent agent is improved, and the ability of the intelligent agent to deal with rapidly changing task requirements and complex application scenes is enhanced.

The embodiment of the invention provides an unmanned ship cooperative hunting control method, and belongs to the technical field of control, and the method specifically comprises the steps: distributing a main hunting target for each chasing unmanned ship; for each chasing unmanned ship, an original observation vector containing a variable-length interaction sequence is formed according to the distributed main hunting target of the chasing unmanned ship; a variable-length interaction sequence in the original observation vector is converted into a standardized state vector of a fixed dimension through a two-stage attention mechanism; inputting the standardized state vector into a strategy network of the unmanned ship, and outputting a continuous action control instruction; and calculating an instant reward based on empirical data obtained after executing an action control instruction, and performing iterative updating on a strategy neural network and a corresponding value neural network by adopting a centralized training and decentralized execution mode based on a multi-agent depth deterministic strategy gradient framework to obtain a control model to generate a cooperative control scheme. Through the scheme disclosed by the invention, the control precision and adaptability are improved.

In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.

The invention relates to the technical field of artificial intelligence, in particular to a gynecological traditional Chinese medicine data processing method and system based on four diagnosis data weight analysis. The method comprises the following steps: acquiring four diagnosis data; map mapping is carried out according to the four-diagnosis data to obtain four-diagnosis map mapping data; obtaining expert experience data and historical case data according to the four-diagnosis map mapping data; performing decision tree construction according to the expert experience data to obtain an expert experience judgment tree model; performing hierarchical weighted regression according to the historical case data to obtain historical case hierarchical weighted data; performing four-diagnosis feature weighting processing according to the expert experience judgment tree model and the historical case level weighting data to obtain four-diagnosis data weight analysis data. According to the method, accurate butt joint of four diagnosis features and a traditional Chinese medicine knowledge system is realized, and a complete link from four diagnosis original feature collection, semantic mapping and rule data collaborative modeling to context adaptive reasoning is constructed.

The invention relates to the technical field of artificial intelligence, and provides a training method and device of a reinforcement learning model, equipment and a medium, and the method comprises the steps: obtaining experience data of an intelligent agent interacting with an environment through employing a historical strategy provided by a to-be-trained reinforcement learning model; determining an original action probability ratio of the reinforcement learning model to be trained between a historical strategy and a current strategy by using empirical data, and performing shear constraint on the original action probability ratio according to a current shear coefficient to obtain a shear action probability ratio; the current shearing coefficient is obtained by updating the difference value between the strategy divergence and the target divergence, the strategy divergence is determined through the shearing action probability ratio, the training loss is determined according to the empirical data and the shearing action probability ratio, and the current strategy of the reinforcement learning model to be trained is updated and trained by using the training loss. According to the invention, by optimizing the shear constraint mechanism, the dynamic adjustment of the current shear coefficient is realized, the training efficiency of the model is improved, and the scene adaptability of the model is improved.

The invention relates to the technical field of computer risk control, in particular to a big-data-based collaborative self-optimization method and system for a self-growth risk control system, and the method comprises the steps: obtaining the operation data of the risk control system, so as to construct a state vector; inputting the state vector into a reinforcement learning agent, and selecting one coordination action from a preset coordination action space for output; the coordination action is executed in the risk control system, and a scalar reward value used for evaluating the effect of the coordination action is obtained through calculation; and forming an empirical data tuple by using the state vector, the coordination action, the scalar reward value and a new state of the system after the action is executed, and training the reinforcement learning agent to update a decision strategy of the reinforcement learning agent. According to the risk control system, the rule engine and the model engine in the risk control system can be considered as a unified whole, and respective parameters are dynamically and cooperatively adjusted, so that the overall comprehensive efficiency of the system is maximized, and the self-adaptive capability and the iteration efficiency of the system are improved.

The invention discloses a multi-agent collaborative decision-making method based on federal reinforcement learning, and the method comprises the steps: firstly, enabling a central server to be initialized, then randomly selecting a client, enabling the client to initialize a local model into a global model, training and updating parameters, and then uploading the parameters; and the central server aggregates and updates the global model parameters until the model training is completed, and the multi-agent collaborative decision-making is completed. According to the method, the reinforcement learning model is trained in a federated learning mode, and similarity weighted staggering of two types of empirical data is combined, so that the effectiveness of the reinforcement learning model can be ensured, and possibility is provided for multiple parties to jointly train the same model; and the data sample richness and the model training efficiency are improved while the data privacy security is ensured.

The embodiment of the invention provides an enterprise information architecture (4A)-based AI Agent planning enhancement system, a control device and equipment, and the system comprises a Prompt construction module which is used for analyzing an unstructured demand to obtain a structured demand, and cooperating with other modules to generate a standardized Prompt comprising a business process, a data entity and a tool strategy; the knowledge matching module is used for matching related knowledge based on a knowledge graph employment number fusion knowledge base and sorting the related knowledge into a knowledge list containing business processes, practical experience, a data list and a tool list; and the context reasoning module is used for generating a reasoning result based on the historical data and the knowledge list and feeding back the reasoning result to the Prompt construction module so as to assist in generating the normalized Prompt. According to the system, the integration capability of the AI Agent on business logic and data resources in the task planning process is remarkably improved, so that the AI Agent can more accurately understand enterprise-level business scenes, and standardized and intelligent planning support is provided for practical application of the AI technology in enterprise digital transformation.

The invention discloses a government affair material auditing method and system based on historical case analysis, and relates to the technical field of intelligent government affair platforms, and the method comprises the steps: obtaining archived historical case data and a to-be-audited target government affair material in a government affair auditing system, carrying out the structural modeling of the historical case data, and obtaining a to-be-audited target government affair material; forming a case database containing content features, audit conclusions and business categories, and realizing computable representation of empirical data; based on the multi-dimensional feature extraction of the target material, combining the reliability weight of the business category obtained by clustering, the similarity weight between the target material and the historical case and the importance weight of each auditing dimension; a multi-layer weighted voting mechanism is adopted, a material comprehensive auditing score is generated through fusion calculation integrating three types of weights, and a machine auditing conclusion is automatically output according to a score threshold value. Therefore, the conversion from artificial experience to data-driven decision is realized, and the accuracy, consistency and intelligent level of government affair material auditing are effectively improved.

The invention discloses a passenger protection restraint system scheme intelligent type selection method and system based on an AI model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal passenger protection restraint system data; integrating the test data, the simulation model data and the expert experience data into a multi-modal input data set; feature extraction is carried out on the multi-modal input data set by utilizing feature extraction models corresponding to multiple different modalities, and multi-modal feature data of the multi-modal input data set is obtained; the prediction models of the different model selection targets are trained through the multi-modal feature data, vehicle passenger protection restraint system reference model selection schemes are output for vehicle design, and an optimal restraint system model selection scheme meeting the design requirements is obtained from the multiple vehicle passenger protection restraint system reference model selection schemes according to expert experience data; and updating the model selection model based on a simulation test result corresponding to the optimal constraint system model selection scheme and expert experience data.

The invention provides a numerical control machine tool dual-system cooperative control method and device and a storage medium, and belongs to the field of numerical control machining. The method comprises the steps that real-time loads of a first main shaft and a second main shaft are obtained from a numerical control system; and if the load of any one of the two spindles exceeds the corresponding load upper limit, the rotating speeds of the two spindles are linearly reduced from the standard rotating speed to the corresponding transition rotating speed, and meanwhile, the feeding speeds of the two spindles are gradually reduced within the preset adjusting range. The loads of the two main shafts are monitored in real time, when any main shaft load reaches the corresponding load upper limit and machining resonance is about to be generated, the rotating speed of the main shafts is automatically lowered to eliminate the resonance of the main shafts, manual intervention of operators is not needed, full-automatic vibration elimination is achieved, and the working efficiency is improved. And the load upper limits of the two main shafts are obtained through calculation of empirical data of a numerical control machine tool dual-system database, different load upper limits are generated under different working conditions, control is flexible, and the damping effect is good.

The invention provides a printed board assembly defect automatic detection method and system based on an AOI technology. The method comprises the following steps: obtaining a complete image of an actual production PCB through a printed board assembly image acquisition system; correcting the complete image to be detected; positioning and acquiring a component image and a component bonding pad image; analyzing the obtained component image, and extracting effective identification information of the component; and according to the effective identification information of the component, comparing and identifying the defect by using empirical data, and feeding back a result. According to the method, the effective identification information of the component is combined, the detection result is compared with the empirical data and the effective identification information of the component, the detection precision is improved, and the category of the defect is determined, so that the specific affiliation relationship between the defect and the component on the PCB is pointed out, and the detection of different defect conditions can be more targeted; and the method has certain self-adaptability and flexibility.

The invention provides a dual-drive deepwater wellbore integrity failure risk evaluation method, and belongs to the technical field of deepwater drilling safety, and the dual-drive deepwater wellbore integrity failure risk evaluation method comprises the following steps: based on fault tree analysis in a bowknot model, identifying a wellbore integrity failure mode; constructing a differentiated evaluation path according to the failure mode, and quantifying a static equipment failure risk and a dynamic structure failure risk respectively; constructing a wellbore system-level failure probability model through a fault tree and Bayesian network dynamic mapping mechanism; based on event tree analysis, modeling is conducted on the safety barrier state after shaft integrity failure, accident consequences are deduced, and the occurrence probability of the accident consequences is calculated; and outputting the system-level failure probability, the key path contribution degree, the bottom event sensitivity sequence and the accident consequence probability, and solving the technical problem that the system-level failure risk is difficult to accurately predict and dynamically prevent and control because static empirical data and a dynamic mechanical mechanism are separated and cannot be coupled and updated in real time.

The invention discloses a spacecraft Hall propulsion device test working medium flow stability control system, which comprises a sensing module for detecting working condition state parameters of working medium supply equipment, and an adaptation module for exploring current working condition control parameters by combining detection data of the sensing module and similar working condition control parameter empirical data fusion, the adjusting module is used for receiving the control parameters and dynamically adjusting the adjusting weight of the adjusting device according to different phase change periods, an experience library unit and a self-adaption unit are arranged in the adaption module, the experience library unit provides experience data of similar working conditions and control parameters, and the self-adaption unit provides self-adaption data of the control parameters. And the autonomous adaptation unit fuses and explores control parameters by constructing a neural network adaptation model, so that working condition automatic identification and control parameter autonomous adaptation under the environment that the working medium type or the working medium state rapidly changes are realized; the problems of low control precision and slow response speed caused by manual presetting of control parameters of different working media in a traditional control technology are solved, and stable control of the working medium flow is realized.

The invention relates to the technical field of data processing, and discloses a safety distance monitoring method and system for net rack and pipe truss construction. The method comprises the following steps: acquiring key node safety distance data, construction state data and environment interference data, and integrating the data into a safety distance monitoring data set; performing anomaly screening and error compensation on the data set to generate a standardized data stream; analyzing the data flow, establishing a deformation feature table, and generating a deformation state report; positioning a dangerous area based on report matching early warning conditions, and outputting early warning information; determining an intervention node according to the early warning information, and generating an adjustment instruction; and in combination with the adjustment instruction and historical data, establishing an associated network to generate a management knowledge base. Intelligent monitoring of the safety distance in the construction process of the net rack and the pipe truss is achieved, an early warning mechanism is established through fusion analysis of multi-source data, a scientific disposal scheme is provided based on historical experience data, and therefore the accuracy and reliability of construction safety management are improved.

The invention belongs to the technical field of appearance detection, and particularly relates to a product external inspection AI co-driving system based on a large model, a construction method and a storage medium. The invention discloses an external inspection AI co-driver system construction method based on a large model, and the method comprises the steps: obtaining the historical experience data of an enterprise, carrying out the fragmentation modularization processing of the obtained historical experience data, and forming an external inspection post knowledge base; data in the external inspection post knowledge base are subjected to vector model coding vectorization processing and stored in a vector database; establishing a vector index model and a vector recall model; establishing a language interaction relationship between the vector database and the large model through a vector index model and a vector recall model; the user side obtains the content of the vector database through large model questions to complete real-time questions and answers; and performing information supplement on real-time dialogue data of the user side, performing key information extraction on the dialogue data, and supplementing the dialogue data into the vector database. Standardized operation guidance is provided for an on-site appearance detection post, so that the appearance detection defect identification efficiency and disposal efficiency are improved.

The invention belongs to the field of artificial intelligence, and discloses a distributed data acquisition system based on multi-agent collaborative decision and a construction method, and the method comprises the steps: 1, collecting heterogeneous data of production equipment, an environment sensor, a man-machine interaction terminal and an enterprise information system in real time through the deployment of a distributed data acquisition network, cleaning, de-noising and standardizing the edge computing nodes to generate a structured data stream; 2, automatically extracting features and laws of structured data through a multi-dimensional engine analysis model, and completing semantic alignment and fusion of heterogeneous data; 3, multiple agents are adopted to form empirical data according to a deterministic strategy gradient model in combination with a multi-dimensional reward function optimization model; and step 4, performing incremental learning on the empirical data to optimize the anomaly detection model, realizing multi-dimensional information visualization display of first-line decision and center command, and deploying corresponding functional agents according to the multi-dimensional information. The problems that a multi-agent system is low in control precision and low in efficiency are solved.

The invention discloses an intelligent load distribution system and method for a centrifugal compressor unit. The system comprises a data sensing module, an intelligent decision-making module, an instruction execution module and a model updating module. The method comprises the steps that operation parameters of multiple parallel centrifugal compressors are synchronously collected in a high-precision mode through an industrial real-time network supporting hardware timestamps, and time-aligned system state observation data are obtained; constructing a state vector based on the data and a load prediction result, inputting a deep reinforcement learning agent fused with compressor physical characteristic constraints, and generating a load distribution instruction and a unit start-stop suggestion; after safety boundary verification is conducted on the original action, the guide vane opening degree is adjusted through a feedforward speed planning and fuzzy self-adaption PID composite servo control algorithm, and starting and stopping operation is executed; meanwhile, empirical data are collected online, and model parameters are periodically and finely adjusted. According to the invention, efficient, safe, accurate and self-evolution compressor group control is realized.

The invention discloses a railway roadbed settlement prediction method and system, and belongs to the technical field of railway engineering. According to the method, basic data such as a roadbed structure, filler parameters, geological conditions and historical monitoring data are collected, a differential prediction model is matched according to roadbed strength grades, risk grading and monitoring resource dynamic allocation are carried out after parameter optimization and multi-index precision evaluation, and iterative optimization is realized based on an empirical database. The system comprises a data acquisition module, a model matching module, a settlement simulation module, a precision evaluation module, a post-processing module and a data storage module, and all the modules cooperate to achieve full-process automation. According to the method and the system, the problem of insufficient adaptability of a traditional single model is solved through accurate model adaptation, the prediction efficiency and the data reliability are improved by means of automatic processing, the safety and the cost are balanced through risk hierarchical management and control, and the adaptability of the system to complex working conditions is continuously enhanced through an iteration mechanism.

The invention discloses an energy storage frequency modulation automatic configuration method, a storage medium and electronic equipment, and the method comprises the steps: firstly, enabling a motor group frequency modulation demand of a to-be-configured energy storage frequency modulation system to be matched with an energy storage frequency modulation configuration feature knowledge base, and obtaining an initial frequency modulation configuration scheme; an energy storage frequency modulation configuration verification model is established according to the energy storage system basic model, the thermal power generating unit AGC response model, the energy storage frequency modulation control model and the economical efficiency analysis model, and then the initial frequency modulation configuration scheme is optimized by adopting an optimization algorithm; calculating a typical daily cost return rate of each optimized frequency modulation configuration scheme in combination with an energy storage frequency modulation configuration verification model, selecting the optimized frequency modulation configuration scheme with the optimal typical daily cost return rate as a target frequency modulation configuration scheme, and determining an initial configuration scheme according to empirical data of frequency modulation configuration; and then the schemes are optimized by adopting an algorithm, and the scheme with the optimal cost yield is selected, so that the energy storage frequency modulation configuration scheme with better economical efficiency can be determined at a relatively high speed.