43 results about "Dependent model" patented technology

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention discloses a reinforcement learning training method and system for relieving illusion of a multi-modal large model, and belongs to the field of reinforcement learning training of a multi-modal large language model. Firstly, a planning and visual description generation step is introduced in an early stage to guide a model to perform structured reasoning, then a grouping relative strategy optimization algorithm is used, reward values are calculated for multiple candidate responses generated by the model after cold start, and particularly, a visual perception reward mechanism is set. The reward mechanism evaluates the consistency of the generated text description and the visual information by using an external large language model. Then, based on a vision description attention score advantage distribution method, learning of the model on key vision signals is dynamically enhanced, and the perception ability of the model on the vision signals is improved; and finally, the perception and reasoning performance of the model is further improved by adopting multiple rounds of rejection sampling and supervised fine tuning. The scheme does not depend on a model architecture, the extra overhead is small, the illusion problem caused by early image-text inconsistency is effectively solved, and the accuracy and the reliability are improved.

The invention discloses an electromechanical industry digital process evaluation method based on data analysis, and particularly relates to the technical field of digital evaluation, a behavior path map, a time sequence causal dependence model and an operation frequency density model are constructed based on information provided by the electromechanical industry, whether structural deviation or behavior abnormity exists is analyzed, and the evaluation result is obtained. If the initial deviation label is generated, calculating a behavior consistency score and a process integrity score, inputting the scores into a fuzzy logic device for risk reasoning, and outputting a credible level; according to the credibility level, judging whether the data is suitable for digital process evaluation, and if the data does not meet the preset requirement, feeding back a corresponding optimization strategy; according to the method, digital behavior structured analysis is realized by constructing a behavior path map, a time sequence causal dependence model and an operation frequency density model; a deviation triggering mechanism is set, and abnormal operation behaviors are accurately recognized; the credibility level is output based on the scoring result, the information applicability is judged according to the credibility level, an optimization strategy is fed back, and the evaluation intelligence level is improved.

The invention discloses a multi-granularity data cascade updating method and device based on a dynamic topological graph and a medium, and relates to the technical field of data processing. The method maintains a multi-level data dependency model (DAG) in memory. The system recursively updates a target node by monitoring the change of a source node and utilizing an incremental propagation algorithm. For a high-concurrency scene needing isolation verification, the method introduces an overlay shadow topology mechanism: on the premise of not destroying a main graph structure and not copying a total graph, only instantiating a shadow copy of an affected node based on a Context ID, and constructing an overlay Mapping pointing to a father node; when the propagation path is calculated, the numerical value of the shadow node is preferentially read based on the context, and the propagation path of the original node is logically blocked in the current context. The invention further provides a path convergence mechanism and rendering frame synchronization technology based on topology in-degree, and the problems of resource competition, data consistency and front-end rendering flicker under the complex dependence network are effectively solved.

The invention relates to the technical field of software engineering automation of digital data processing, and discloses a game development automation method based on a multi-agent system. The method is used for solving the problem that a machine-readable cross-product dependence model and consistency verification are lacked in a traditional method. According to the method, based on a task list freezing project context, a constraint packet and a threshold aperture, and based on a demand account book and registration demands, constraints, codes, configuration, resources and other entry abstracts, version information and strong and weak reference relationships; the multiple agents only submit change packets in a temporary storage area by writing tokens and occupying fragmentation write-in gating, and an influence range report binding verification list is generated for each change packet; boundary compliance, reference integrity, compilation verification, determinacy operation verification and acceptance assertion verification are sequentially carried out on the temporary storage area snapshots, and when conflicts occur, rollback or confinement repair is triggered to output traceable and acceptance deliverables based on reproducible evidence judgment.

The invention discloses a learner cognitive diagnosis method oriented to an intelligent education scene, and belongs to the technical field of cognitive diagnosis and education data analysis. According to the method, concept-level multi-dimensional modeling is carried out on the ability and exercise difficulty of a learner by constructing multi-dimensional representation of concept perception so as to describe mastering characteristics of the learner on different cognitive levels; and meanwhile, distinguishing a pre-correction dependency relationship and a semantic approximation relationship, establishing a relationship-perceived concept dependency model, and deducing a potential exercise-concept association structure according to the relationship-perceived concept dependency model. Further, the annotated exercise-concept incidence matrix and the inference incidence matrix are fused in a unified diagnosis layer, and comprehensive evaluation of the knowledge mastering state of the learner is achieved. According to the method, an end-to-end mode is adopted for optimization, the fineness, knowledge coverage and stability of cognitive diagnosis can be effectively improved, better prediction performance and generalization ability are shown on multiple real education data sets, and the method is suitable for intelligent education application scenes such as learning analysis and personalized teaching.

The invention discloses a method and device for determining an operator shape of an AI model and related equipment, and relates to the technical field of artificial intelligence. Determining the degree of influence of each parallel strategy on the shape of a first operator included in the AI model in a plurality of parallel strategies provided by a computing system running the AI model; obtaining a target parallel configuration of the AI model, wherein the target parallel configuration comprises a configuration value of each parallel strategy in a plurality of parallel strategies; and determining the shape of the first operator under the target parallel configuration according to the configuration value of each parallel strategy in the plurality of parallel strategies, the basic shape of the first operator and the influence degree of each parallel strategy on the shape of the first operator. Therefore, according to the influence degree of each parallel strategy on the shape of the operator in the AI model, the shape of the operator under the target parallel configuration can be determined without depending on the specific structure of the AI model, so that the generalization and universality of determining the shape of the operator can be effectively improved, and meanwhile, the efficiency of determining the shape of the operator can also be improved.

The application discloses a cross-media model error attribution and auxiliary correction method based on causal decoupling, which comprises the following steps: image segmentation is performed on an input image, and the input image is divided into a plurality of sub-regions; a target function is solved according to the sub-regions, the minimum key region which supports the generation of a target word element to the greatest extent and removes the generated ability is sorted according to the contribution to the generation decision, and an attribution saliency map is generated; an influence score is calculated according to the change of the generation probability of the target word element in the process of inserting the sub-regions in the ordered subset; the ordered subset, the attribution saliency map and the influence score are evaluated from the dimensions of fidelity, positioning ability and error correction guiding ability, and an evaluation result is obtained; the evaluation result is obtained without relying on the internal gradient, attention weight or activation map of the model, the dependence on the internal structure of the model is reduced, the regional attribution of an arbitrary word element set is realized, and the relative dependence of the generation process on visual evidence and language prior is quantified.

A computer-implemented method for ascertaining an item of environment information is provided. The item of environment information relates to material in surroundings of a third article and / or an interaction of the third article with the material. The method includes receiving an X-ray image and an item of sensor information, and determining model parameters or limiting possible parameter values of the model parameters of a three-dimensional model of the third article as a function of the X-ray image in order to specify an X-ray-dependent model. The three-dimensional model describes a three-dimensional shape and / or pose of the third article as a function of the model parameters. The method includes ascertaining the item of environment information as a function of the X-ray-dependent model, where the item of environment information and / or the X-ray-dependent model additionally depends on the item of sensor information. The item of environment information is provided.

The application relates to a periodic operation component multivariate perception abnormal mode time sequence dependence mining method, which comprises the following steps: S1, a component multivariate perception variable abnormal event capture platform is constructed; S2, abnormal mode time sequence data of the perception variable is captured, and an abnormal time sequence database Event_seqDB is constructed; S3, a multivariate perception abnormal mode time sequence database KDD_SeqDB is constructed; and S4, multivariate perception abnormal mode time sequence dependence mining is carried out based on an improved PrefixSpan. Through the construction of a key component multitransmission variable perception mode time sequence dependence model and algorithm, the time sequence cause-effect relationship formed in the component operation and maintenance process can be effectively mined, thereby providing support for preventive maintenance and design iteration optimization.

The invention relates to the technical field of reliability management of electromechanical equipment, in particular to an electromechanical equipment reliability evaluation method based on a switching Markov process and fractional Brownian motion, which comprises the following steps: constructing a switching Markov process model, the description module is used for describing a multi-stage degradation process experienced by equipment in a life cycle and state transition thereof; based on the external working condition parameters and the switching Markov process, constructing a stage dependency model; establishing a nonlinear degradation model of the electromechanical equipment based on the fractional Brownian motion process, and introducing a random effect into the model to represent individual differences; and constructing a two-stage parameter estimation method, and sequentially estimating unknown parameters in the switching Markov process and the degradation model. The performance degradation of the electromechanical equipment is accurately modeled, and the nonlinearity, randomness, individual difference, long-term memorability and stage dependence of the multi-stage degradation of the electromechanical equipment are effectively captured, so that the reliability of the electromechanical equipment is accurately evaluated.

This invention relates to the field of data processing technology for predictive purposes, and discloses a method for adjusting the power generation performance of an energy system throughout its entire life cycle. The method determines the parameter boundaries of a wind-solar-storage power supply system, divides the entire life cycle planning period into multiple consecutive life stages, and determines the load demand input for the entire life cycle. It sets a baseline power generation performance at the initial commissioning stage, calculates the degradation maintenance level for each stage, establishes a monotonic mapping relationship between the degradation maintenance level and maintenance intensity, constructs a stage-dependent model for calculating available power generation capacity coupled with degradation and maintenance, and obtains available power supply capacity parameters for each stage of the entire life cycle. It generates a maintenance time series distribution through Monte Carlo simulation and constructs a set of life cycle power generation performance evolution scenarios. With the goal of minimizing the total cost throughout the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance throughout the entire life cycle. This method solves the problems of ignoring degradation maintenance and inaccurate planning, achieving the goal of accurate and reliable life cycle data at a low cost.

The invention provides a decoration project intelligent construction planning system based on artificial intelligence, and relates to the technical field of construction planning, and the system comprises the steps: obtaining a resource library and a construction task sequence in a decoration project; constructing a resource task constraint model by using the resource library and the construction task sequence, determining a regulation and control optimization feature matrix, and performing constraint optimization on the resource task constraint model according to the regulation and control optimization feature matrix to obtain a resource task optimization model; constructing a construction process dependence model in the decoration engineering process, and performing decoration core measurement on each process dependence node in the construction process dependence model to obtain a decoration core coefficient of each process dependence node; and generating a resource scheduling planning sequence in the decoration project on the basis of the decoration core coefficients of all the process dependent nodes and the resource task optimization model, and planning and scheduling resources in the decoration project according to the resource scheduling planning sequence. Therefore, resource collaborative scheduling in the decoration project is realized.

The invention discloses a crane track seam real-time detection method and system, and belongs to the technical field of cranes, and the method comprises the steps: obtaining seam images under various different track surface conditions, extracting a region of interest, combining a model activation consistency index and background disturbance sensitivity, calculating a background dependency index, and carrying out the dependency classification of a model; according to the method, the recognition accuracy of a high-dependency model in a new environment is predicted based on track background features, a prediction result and dependency are subjected to joint analysis, and directional optimization is performed on the model by adopting an attention-guided confrontation decorrelation training mechanism and the like, so that the problem that the recognition capability of a traditional CNN model is reduced in a background change environment is effectively solved, and the recognition accuracy of the model is improved. The generalization ability and deployment robustness of the model are significantly improved, and the environment migration cost and the misjudgment risk are reduced.

The invention provides a super junction structure parameter optimization method and apparatus, a terminal device and a storage medium. The method comprises the steps of establishing a temperature dependence model of each physical parameter in a super junction structure; wherein the temperature dependence model is used for describing the change rule of the physical parameters along with the temperature; establishing constraint conditions according to a planar junction theory and an avalanche breakdown principle; wherein the constraint conditions comprise a collision ionization integral breakdown condition and a buffer layer boundary condition; obtaining target information based on the design target of the super junction structure; wherein the target information comprises breakdown voltage, depth-to-width ratio and temperature; solving a performance parameter optimization value of the super junction structure under the target information according to the temperature dependence model and the constraint condition; wherein the performance parameters comprise depletion layer thickness, doping concentration and specific on-resistance. According to the method, the optimization calculation of the performance parameters of the super junction structure can be realized in a larger temperature range.

The application discloses a kind of based on double-layer dependent perception Web3D application mobile edge cache method, first according to the data of content object in Web3D application mobile network, double-layer dependent relationship modeling is carried out, the logical dependence model and space dependence model between Web3D application content object are obtained, then based on double-layer dependent model, mobile edge cache problem with the minimum system expected loading total delay as target is constructed, and edge server carries out caching and replacement to the resource module in each content object according to the cache decision variable matrix obtained by solving.The logical dependence model and space dependence model between Web3D application content object are obtained by double-layer dependent relationship modeling in the application, and caching decision is carried out based on the above two models, and the hit rate and resource loading efficiency of Web3D application mobile edge cache are improved.

The invention provides a cloud configuration item intelligent optimization system and method, and relates to the technical field of cloud configuration data optimization. Comprising the following steps: constructing a dynamic configuration relation graph by mapping configuration item data to different hierarchies of a rule dependence model, generating a multi-modal evidence body and a dynamic trust chain rule based on the dynamic configuration relation graph and key attributes of configuration items, constructing a causal dependence graph through instantiation, calculating a robustness index for the causal dependence graph, and establishing a dynamic trust chain rule. Performing conflict resolution arbitration according to the state of the arbitrated configuration item; the data accuracy and the self-healing capability are improved through automatic conflict resolution, the real-time performance and the consistency of configuration optimization are enhanced through causal dependence analysis, and the optimization efficiency is improved while the reliability is ensured.

The invention relates to the technical field of combustion safety management and control, and discloses a combustion safety management and control system and method, and the system comprises an acquisition module which is used for collecting spectral data in real time, processing and analyzing the spectral data, obtaining real-time key parameters, and calculating a real-time combustion evaluation value according to the real-time key parameters; the judgment module is used for judging whether management and control are needed or not according to the real-time combustion evaluation value, and if yes, a plurality of real-time management and control characteristics and corresponding parameters needing to be adjusted are determined; the generation module is used for generating a to-be-controlled parameter of each parameter needing to be adjusted based on the control parameter-key parameter dependency model and obtaining a plurality of first management and control strategies; and the management and control module is used for performing management and control simulation on the plurality of first management and control strategies, determining a second management and control strategy according to a simulation result, issuing a management and control instruction, accurately evaluating a parameter state, formulating a reasonable management and control strategy and improving combustion safety management and control efficiency.

The application relates to the technical field of computer vision and deep learning, and particularly discloses a fabric defect detection method based on a visual saliency model, which utilizes a convolutional neural network to construct a spatial detail extraction branch, accurately captures the edge and texture details of local tiny defects through a self-adaptive weighted context coordination mechanism, simultaneously introduces a visual state space model to construct a global information extraction branch, and efficiently establishes a long-distance dependence model of a global background with the aid of a two-dimensional space selective scanning technology. On this basis, a bidirectional feature fusion decoder is used to perform deep cascading and dynamic integration on local details and global contexts, thereby effectively suppressing the interference of regular texture backgrounds, enhancing the feature saliency of irregular defects, and realizing high-precision and high-efficiency automatic detection of fabric defects in a low-contrast scene.

The application discloses a Go language library migration method based on semantic dependency graph and complexity scheduling, and comprises the following steps: S1, source code is acquired, and a unified semantic dependency graph is generated; S2, a minimum migration unit is generated; S3, multi-dimensional complexity quantification and risk modeling calculation are carried out on each minimum migration unit, a risk score is generated, all minimum migration units are sorted according to the risk score from low to high, and a migration priority queue is constructed; S4, according to the migration priority queue, the migration unit with lower risk is preferentially executed, and a migration state is recorded; S5, a test verification process is carried out; and S6, migration scheduling is carried out. The application realizes automatic migration planning based on program analysis, graph theory structure and complexity scheduling algorithm, does not depend on model training or external knowledge enhancement mechanism, and has high engineering applicability.

A computer-implemented method for determining environmental information (22) based on at least one two-dimensional X-ray image (23) depicting at least a section of a third object (24-26) located within a patient (42), and at least one sensor information (27) based on at least one measurement value from at least one sensor (28-31) of the third object (24-26), wherein the environmental information (22) relates to material (32-34) in the vicinity of the third object (24-26) and / or an interaction of the third object (24-26) with this material (32-34), comprising the steps: - Receiving the X-ray image (23) and the sensor information (27), - Determining model parameters (35) or restricting the possible parameter values ​​of the model parameters (35) of a three-dimensional model (36) of the third object (24-26) depending on the X-ray image (23) to specify an X-ray-dependent model (37), wherein the three-dimensional model (36) describes a three-dimensional shape and / or pose of the third object (24-26) depending on the model parameters (35), - Determining the environmental information (22) as a function of the X-ray image-dependent model (37), wherein on the one hand the environmental information (22) and / or on the other hand the X-ray image-dependent model (37) additionally depend on the sensor information (27), and - Providing the environment information (22).

The application discloses a GPU resource-aware matrix multiplication parallel performance analysis model construction method, and relates to the technical field of high-performance computing, which is based on the Roofline principle, combines a bandwidth model, a resource load model and an instruction dependency model to construct a performance analysis model, and is successfully applied to a matrix multiplication scene to realize parallel performance quantification under resource-aware matrix multiplication application.The application can realize resource awareness without platform limitation, can distinguish parameter settings for maximizing resource use and optimizing performance in the matrix multiplication scene through the performance analysis model, and lays a foundation for subsequent parallel program optimization work; the hardware parameters used by the application are completely based on publicly available GPU hardware parameters, so that non-GPU professionals can also use the model to evaluate the performance of programs.

The application discloses a data-driven nonlinear multi-agent system online cooperative control method, relates to the technical field of multi-agent system cooperative control, considers an affine nonlinear multi-agent system with external disturbance and unknown system model under a non-directional connected graph, and realizes consistent terminal value bounded control of the system. The scheme comprises the following steps: considering a discrete time affine nonlinear multi-agent system with external disturbance and unknown system model, a distributed state feedback controller is designed; based on an online updating data collection mechanism, state data and input data of the system are collected, and a data-based system parameterized representation is constructed; a state-dependent model is constructed, a data-based distributed robust controller is solved online, and consistent terminal value bounded control of the unknown model affine nonlinear multi-agent system is realized.

The invention provides a human body motion prediction method and system based on space-time modeling of a diffusion model, relates to the technical field of human body motion analysis, and aims to solve the problems that a noise prediction technology in existing human body motion prediction cannot fully capture close association of space-time interaction, and prediction is completed only by depending on a single prediction result of the model. The real condition of the motion sequence cannot be accurately and faithfully reflected, and the calculation complexity is high. According to the method, a noise prediction network model is constructed by combining a plurality of space-time crossing fusion graph convolutional networks with a stacked motion residual learning network, accurate prediction of human motion noise is realized, and human motion is accurately predicted according to a noise prediction result. According to the method, the problems existing in the noise prediction technology in existing human motion prediction are solved, the spatial topological relation between joints is fully considered, effective information of data can be fully utilized, meanwhile, the calculation efficiency is considered, the requirement of real-time application is met, and then the accuracy of human motion prediction is improved.

The invention provides a three-dimensional variation data assimilation method based on a flow dependence model, and the method comprises the following steps: S1, carrying out the preprocessing of multi-source data and the setting of a region-element adaptive time window: carrying out the preprocessing of multi-source meteorological data, and dynamically setting a time window [tau] based on the characteristics of meteorological elements and the characteristics of a region; s2, constructing and training a flow dependency model: constructing a deep learning flow dependency model of a CNN-LSTM hybrid architecture; and S3, constructing a cost function to carry out gradient solution. According to the three-dimensional variation data assimilation method based on the flow dependence model, a deep learning flow dependence model is introduced, a meteorological element association model in a small-scale time dimension is accurately constructed through a high-precision multi-feature extraction capability, traditional static prior information is replaced, and the new three-dimensional variation method has dynamic time adaptability; time windows are dynamically set for different regions and different meteorological elements for rapid model training, and the problem of adaptability of fixed time windows in a traditional method is solved.

The application relates to the technical field of automation control systems, in particular to an intelligent substation sequence control method and system, which comprises the following steps: based on the equipment operation instruction of the intelligent substation, the logical sequence of equipment operation is analyzed, the state change information of the equipment is extracted, the operation analysis of the equipment state and time correlation is carried out, the dependence relationship of the equipment state is judged through the time difference of the equipment state, and an equipment operation dependence model is generated. In the application, the resource occupation of the equipment is monitored through a data flow compression algorithm, load balancing is carried out, the equipment load level is evaluated in real time, and dynamic distribution and adjustment of the load are realized, the pressure of high-load equipment is effectively relieved, an operation conflict judgment mechanism is introduced, potential conflicts of the equipment in parallel execution are identified and solved in real time, the smooth execution of parallel tasks is ensured, the delay and conflict conditions possibly occurring in the equipment execution process are effectively reduced, and the continuity and reliability of the power system are ensured.

The embodiment of the invention discloses a service deployment method and device, electronic equipment and a storage medium, and can solve the problems that when a dependency relationship is configured, the whole document needs to be set or modified, the complete dependency relationship among multiple services cannot be visually reflected, and the visibility is poor. Obtaining initial dependency information, wherein the initial dependency information is used for describing a dependency relationship between system service nodes; according to the initial dependency information, an initial dependency graph is constructed, the initial dependency graph comprises a dependency relationship network of at least one version, and the dependency relationship network comprises a plurality of system service nodes and at least one dependency relationship existing between the system service nodes; performing data configuration on the dependency relationship network of at least one version through pre-stored target configuration data to obtain a target dependency model, the target configuration data comprising configuration data for the plurality of system service nodes and configuration data for at least one dependency relationship; in response to the deployment instruction, the target dependency model is deployed.

The invention discloses an electric power system evaluation method based on wind power decision uncertainty, and relates to the technical field of electric power system reliability evaluation, and the method comprises the steps: obtaining the basic parameters of an electric power system, and carrying out the modeling of the reserve capacity of a wind driven generator according to the basic parameters; the reserve capacity is quantified based on the adjustable range of the pitch angle of the wind generating set, and a decision dependence model of the wind generating set is established according to the supply of the reserve capacity; and establishing a power system reliability evaluation method according to the decision relation between the wind speed prediction error and the pitch angle of the wind driven generator, and establishing a Markov model to monitor the randomness characteristics of power system elements. According to the method, the challenge on the uncertainty of the wind power system in a traditional evaluation method is effectively solved, the accuracy and the reliability of the operation state of the wind power system are improved, and the adaptability and the stability of a power system are improved. The operation state of the wind power system is monitored and evaluated in real time, and powerful support is provided for operation, maintenance and management of a power system.

The invention discloses an intelligent analysis method, system, equipment and medium for fire coal test data of a thermal power plant, and relates to the technical field of fire coal quality intelligent monitoring and control, and the intelligent analysis method comprises the following steps: collecting test data of a fire coal retest sample, executing nonparametric rank correlation analysis on non-normal distribution data, quantifying negative correlation intensity of a calorific value difference and retest interval time, and analyzing the heat value difference and the retest interval time. The method comprises the following steps: constructing a time-dependent model of calorific value attenuation based on negative correlation intensity by associating coal oxidation sensitivity parameters, predicting calorific value variation by utilizing linear regression, comparing calorific value-time quality control charts of a standard coal sample and a to-be-detected coal sample, and dynamically correcting a prediction equation coefficient. According to the method, the limitation of coal heat value static prediction is broken through, the three technical bottlenecks that coal oxidation difference is not quantified, model correction lags behind and manual decision is extensive are solved, and a core support is provided for fine management of fuel of a thermal power plant.

The invention belongs to the technical field of power system steady-state standby and dynamic support resource cooperative scheduling, and provides a power system steady-state-dynamic resource scheduling method considering frequency modulation capability dependency, which comprises the following steps: constructing a data-driven multi-wind power plant frequency modulation capability and system decision variable dependency high-dimensional linear mapping model; based on the high-dimensional linear mapping model, constructing a system steady-state operation and frequency support characteristic integrated optimal scheduling model considering a frequency modulation capability dependent characteristic; and solving the integrated optimal scheduling model by adopting a two-stage decomposition solving strategy. According to the method, the frequency modulation capability dependent model is embedded into the system operation mode and frequency support characteristic integrated optimization framework, and a master-slave decomposition solving strategy is adopted, so that the calculation complexity is effectively reduced, and the efficiency of the system is improved on the premise that the system frequency safety and the fan operation safety are guaranteed. And economic optimal configuration of steady-state energy reserve and frequency support reserve of the system is realized.