150 results about "Resource supply" patented technology

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention provides an operation and maintenance process optimization method and system applied to operation and maintenance technical services, and relates to the technical field of operation and maintenance technical services. Firstly, scene features, execution association relationships and resource demand attributes in an operation and maintenance task demand document are collected to form an operation and maintenance task information set; and meanwhile, collecting real-time operation state data and capability dimension information of the service nodes to construct dynamic capability portraits of the service nodes. And then, based on the scene features and the scene adaptation capability, executing bidirectional adaptation processing to generate a preliminary adaptation result, identifying and resolving resource supply conflicts in the preliminary adaptation result, and generating a final adaptation scheme. And finally, integrating an operation and maintenance task execution sequence and service node resource supply arrangement according to the final adaptation scheme to form an operation and maintenance process optimization scheme, and dynamically evolving and calibrating the scheme content according to real-time data in the execution process. The operation and maintenance task can be accurately allocated, the flexibility and adaptability of the operation and maintenance process are improved, and the operation and maintenance service quality and efficiency are improved.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention discloses a GIS resource management method, system and device and a medium, and belongs to the technical field of resource management, and the method comprises the steps: obtaining a city planning map, constructing a city three-dimensional model, and collecting multi-source city feature information; constructing a multi-source data fusion matrix of a GIS graph and generating three layers of attribute tags; a spatial gravity model is constructed, and reachability analysis is carried out on urban resources; constructing a dynamic prediction model, and triggering a model iteration mechanism; and generating a resource health degree index based on a model iteration mechanism result, and triggering a resource allocation control signal. The method has the beneficial effects that through multi-source data fusion matrix construction, precise space-time association of geographic space, Internet of Things time sequence and visual feature data is realized, and the integrity and accuracy of data integration are improved; quantitative evaluation and prospective pre-judgment of resource supply and demand are realized based on reachability analysis of a space gravity model and a dynamic prediction model of a space-time diagram convolutional network.

The invention relates to the technical field of data management, and discloses a cloud computing-based technical service integrated management system, which comprises a resource management module, a service monitoring module, a security control module, a data processing module and a user interface module, when dynamic allocation of cloud computing resources is carried out, resource load indexes are monitored in real time through an elastic telescopic unit in the resource management module, and resource demand estimation is carried out in combination with the prediction model, so that the system can actively adjust resource supply according to the fluctuation trend of technical service demands, the hysteresis of a resource allocation strategy is avoided, and the resource allocation efficiency is improved. Dynamic matching between cloud computing resource allocation and a real load is ensured, the resource utilization efficiency is improved, multi-dimensional monitoring data is collected and associated through a fault detection unit and a log analysis unit in the service monitoring module, so that the system can quickly position a core problem from an isolated alarm event, and the system reliability is improved. And the troubleshooting complexity and the time cost are reduced.

The application discloses a data management platform for smart park construction and belongs to the technical field of smart park management, and specifically comprises the following steps: collecting resource demand data and resource supply data of each enterprise in the park, performing demand feature extraction operation on the collected resource demand data to generate a structured demand description, performing supply feature extraction operation on the resource supply data to generate a structured supply description, calculating the matching degree between demand and supply based on the structured demand description and the structured supply description and generating an initial matching scheme, performing constraint condition checking operation on the initial matching scheme according to the park operation strategy to identify matching pairs that violate the constraint conditions, performing scheme adjustment operation on the matching pairs that violate the constraint conditions to generate an optimized matching scheme, and finally scheduling the park resources to complete resource data sharing between enterprises; and the application realizes intelligent management and efficient sharing of resource data in the park, and improves the overall resource utilization efficiency of the park.

The invention relates to the technical field of manufacturing execution systems, in particular to an intelligent scheduling method integrating demand prediction and supply matching, which comprises the following steps of: acquiring to-be-scheduled order information and a workshop real-time resource state in a manufacturing execution system, constructing an order demand feature vector, the method comprises the following steps: acquiring an equipment precision retentivity value, a current load rate value, an energy consumption level value and an operator skill proficiency value, and constructing a resource supply capability vector; according to the method, when an urgent order insertion instruction is received, a clear decision basis is provided, a rigid scheduling locking block which must be kept unchanged and an adjustable liquid adjustable scheduling block which can be adjusted can be distinguished, only the latter is subjected to accurate splitting and backward moving operation, and the target-clear local adjustment mode is adopted, so that the operation efficiency is greatly improved. The huge calculation overhead and the violent fluctuation of the production plan caused by the traditional global rescheduling are avoided, and the agility of the manufacturing system to deal with the market change is enhanced.

According to the resource optimization allocation method and system for regulating and controlling the factory-like planting environment of the ganoderma lucidum mushrooms, stage division is performed on the factory-like planting period of the ganoderma lucidum mushrooms, the environmental parameter threshold value of each stage is determined, the growth characteristics of each stage are accurately matched, a quantifiable judgment basis is provided for an environment judgment standard, and the economic benefits of the ganoderma lucidum mushrooms are improved. Based on historical ganoderma lucidum mushroom planting data, a correlation model between environmental parameter deviation and resource adjustment amount in each stage is established, rationalization of resource supply is guaranteed, real-time environmental parameters of ganoderma lucidum mushroom factory planting are collected in real time, the deviation value between the real-time environmental parameters and the environmental parameter threshold value of the current stage is determined, and the resource adjustment amount is adjusted according to the deviation value. The method comprises the following steps: acquiring a deviation value of the environment parameter, determining a resource optimization parameter corresponding to the deviation value based on a correlation model, realizing timely regulation and control of resources, running according to the resource optimization parameter, and performing iterative adjustment in the running process until the real-time environment parameter reaches the standard, so as to ensure that the environment parameter in each stage in the whole planting period is continuously in a threshold interval.

The invention relates to the technical field of charging pile management, in particular to an intelligent management method for charging piles, which comprises the following steps of: acquiring a fault reporting position, a fault type and a fault priority of each charging pile, simultaneously acquiring professional skills, a current position, a working time window and a central spare part library position of a maintenance engineer, and constructing a to-be-scheduled maintenance task list. According to the method, the fault position and the fault priority are mapped to the urban map grid and weighted counting is carried out to form the demand side pressure expression, the relationship between the service radius and the position of an engineer is introduced, the supply side space distribution is formed through superposition, the regional maintenance resource supply and demand situation is dynamically constructed in a quantifiable mode, and the task allocation efficiency is improved. According to the resource supply and demand diagram, an initial task sequence is allocated for an engineer, and sequence adjustment and task exchange are performed, so that a scheduling scheme can be optimized according to actual capability, path accessibility and a time window.

The application discloses a kind of engineering progress and resource early warning method and system based on dynamic regulation, method includes:1) real-time acquisition engineering progress and resource data, constructs multi-source data fusion model;2) based on the multi-source data fusion model constructed, whether there is abnormality in construction progress and resource consumption is judged, if yes then execute step 3);Otherwise, continue to carry out normal construction progress tracking and resource management;3) trigger hierarchical early warning mechanism, generate and execute optimization control strategy, adjust construction progress and resource allocation;4) the effect of regulation is evaluated, and after collecting feedback data, return to step 2).The application considers actual construction progress deviation and resource supply and demand imbalance condition by comprehensively, analyzes the overall operation of current construction project, flexibly carries out the switching of construction management strategy and the adjustment of management parameter, can quickly and effectively guarantee construction progress, optimizes resource utilization, significantly improves the intelligent level and response efficiency of construction management.

The invention provides a task scheduling method and device, equipment and a medium, and the method comprises the steps: building an inter-task communication matrix based on the task communication traffic between every two sub-tasks in a plurality of to-be-scheduled sub-tasks; constructing a network affinity matrix based on the bandwidth and the congestion index between every two task processing nodes in the plurality of task processing nodes to be scheduled; performing clustering processing on the plurality of subtasks based on the inter-task communication matrix to obtain a task cluster set, and performing clustering processing on the plurality of task processing nodes based on the network affinity matrix to obtain a node cluster set; and according to the resource demand information of each sub-task and the resource supply information of each task processing node, mapping the task cluster set and the node cluster set, and determining a mapping result between the plurality of sub-tasks and the plurality of task processing nodes. Therefore, the task cluster with dense communication can be mapped to the node cluster with high network affinity, the communication overhead is reduced, and the end-to-end time delay of the task is reduced.

The invention provides a dynamic scheduling method supporting power distribution network multi-type fault first-aid repair, which comprises the following steps: 1) constructing a power distribution network-road network coupling topology model, mapping power distribution equipment nodes and road traffic network nodes, and uniformly describing geographic positions and path constraints; 2) establishing a system model of a first-aid repair team, a material deliveryman and multiple types of fault events, and defining key parameters and problem constraints; (3) a rolling time domain control mechanism is adopted, dynamic time window management and rescheduling multi-level triggering condition control are carried out, and real-time response to a new fault, fault priority change and consumption of resources carried by a first-aid repair team is achieved; (4) when scheduling is triggered, a genetic scheduling optimization algorithm based on improved task priority coding is adopted, task urgency, path accessibility and resource matching degree are comprehensively considered, task allocation, resource supply and a path planning scheme are subjected to joint optimization, and an optimal scheduling strategy is output; according to the invention, the efficiency of power distribution network fault first-aid repair is improved.

The application discloses a kind of emergency communication equipment dispatch management method and system related to dispatch management technical field, obtain the communication demand information corresponding to each task point and the resource supply information corresponding to each emergency communication equipment, the communication demand information and the resource supply information are matched based on emergency dynamic weight, obtain initial scheduling scheme;Based on the initial scheduling scheme and the real-time traffic information of emergency area, generate initial scheduling strategy;The initial scheduling strategy includes the initial scheduling scheme and the scheduling path corresponding to the implementation initial scheduling scheme;Based on multi-objective optimization algorithm, the initial scheduling strategy is optimized, and target scheduling strategy is obtained, can according to the communication demand information of task point and the resource supply information of emergency communication equipment, real-time determination reasonable emergency communication equipment scheduling strategy, improve the real-time performance and reliability of emergency dispatch.

The invention relates to a game information processing method and device and a storage medium, and the method comprises the steps: responding to a user to configure a resource supply task for a first virtual object, and displaying a resource supply rate of executing the resource supply task by the first virtual object as a first resource supply rate on a game interface; displaying a plurality of options on the game interface on the basis that the progress of executing the resource supply task at the first resource supply rate by the first virtual object meets a set condition; wherein the option indicates a first adjustment coefficient for adjusting the resource supply rate and a first time window for adjusting the effect; in response to a selection operation of a user on a first option in the plurality of options, displaying that the resource supply rate is adjusted from a first resource supply rate to a second resource supply rate in a first time window associated with the first option on the game interface; wherein the second resource supply rate is determined based on the first resource supply rate and a first adjustment coefficient associated with the first option.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method relates to the field of data processing, and comprises the following steps: when a computing power purchase request of a computing power purchase object is received, determining computing power resource demand information and a purchase strategy of the computing power purchase object from the computing power purchase request; screening computing power resource supply schemes provided by the plurality of computing power supply objects based on the computing power resource demand information to obtain a plurality of candidate supply schemes; scoring each candidate supply scheme based on a scoring rule matched with the purchase strategy to obtain a score value of each candidate supply scheme; and determining a target supply scheme from the plurality of candidate supply schemes based on the score value of each candidate supply scheme, and recommending the target supply scheme to the computing power purchase object. Through the method and the device, the problem of poor accuracy of recommending the computing power resources to the computing power purchaser in a computing power market transaction scene in related technologies is solved.

The invention provides a computing power matching method and system for large-model cross-modal retrieval, and relates to the field of large-model technology application, and the method comprises the steps: carrying out scene computing power demand mapping processing on a large-model cross-modal retrieval demand, and obtaining a scene computing power demand result containing a basic computing power demand and a dynamic computing power demand; constructing a resource supply capability portrait of each computing power node in a preset computing power pool, executing double-loop adaptation adjustment processing based on the result to obtain an inner-loop local adaptation conclusion and an outer-loop global adaptation conclusion, generating a computing power adaptation judgment conclusion according to a double-loop adaptation result, and determining the computing power adaptation according to the computing power adaptation judgment conclusion. According to the method, a scene-based computing power demand is determined to indicate the adaptation state of the scene-based computing power demand and the resource supply capability, finally, a computing power scheduling instruction is generated according to a determination conclusion, and computing power tasks are accurately allocated to adaptive computing power nodes, so that the efficiency and performance of large-model cross-modal retrieval are effectively improved, and the computing power cost is reduced.

The application relates to the technical field of cloud-edge collaboration, in particular to a production scheduling intelligent management and control system based on cloud-edge collaboration, which comprises a task characteristic aggregation module, which extracts a task type code based on an input task request, queries a task type and calculation amount mapping table in a pre-stored knowledge base, matches the task type and extracts a calculation amount parameter. In the application, the task data packet size, the real-time requirement threshold and the bandwidth benchmark value are linked to calculate the transmission time consumption parameter by dynamically matching the task type and the pre-stored calculation amount mapping relationship, the quantitative mapping of task demand and resource supply is realized, and the parameter deviation caused by traditional artificial experience estimation is eliminated. The CPU occupancy rate value of the edge node process queue is collected in real time, the multi-dimensional node state vector is generated by combining the cloud virtual machine instance quantity and the memory residual quantity data, the limitation of traditional single resource index monitoring is broken through, and the time and space fine granularity of load evaluation is improved.

This invention provides a deep learning-based adaptive allocation method and system for cloud storage resources, relating to the field of cloud storage technology. First, a business resource symbiotic network is constructed to reflect the dynamic balance between business demand and resource supply. Next, a deep learning model is invoked to extract network evolution characteristics. Based on these characteristics, a business resource adaptation demand sequence is derived to determine the adaptation direction and pattern of storage resources for different time periods. Then, the deep learning model dynamically matches and deduces the adaptation demand sequence with the resource supply spectrum to generate a set of elastic resource allocation schemes. Finally, a real-time allocation instruction stream is generated based on the set of elastic resource allocation schemes to drive dynamic reorganization of storage resources, achieving real-time adaptation between business demand and resource supply.

The invention relates to the technical field of data communication networks, in particular to a switching chip resource dynamic allocation management method. The method comprises the following steps: normalizing resource state information, service flow feature information and network environment information of a switch chip to obtain a target state data set; based on the target state data set, classifying and identifying the service flow, and determining service level information and resource constraint information corresponding to each type of service flow to obtain a target service characterization result; constructing a resource supply-demand relationship based on the target service characterization result and the resource state information, and performing analysis in combination with the service flow feature information to obtain a target allocation strategy; generating a resource configuration instruction based on the target allocation strategy, and controlling the switching chip to execute; and obtaining the executed operation monitoring information, and correcting the target allocation strategy based on the operation monitoring information to update a subsequent resource allocation instruction.

The application provides a method for evaluating matching degree of logistics resources and demand in a sharing mode, and relates to the field of information technology.The method first determines the characteristics of logistics resources according to the key resources of each link of express logistics and the resources required by logistics tasks, then classifies the resource characteristics according to the nature, divides them into fuzzy characteristics and containing characteristics, determines the fuzzy characteristic boundary by using normal distribution, then calculates the combination value of different logistics resource characteristics by using Euclidean distance, Manhattan distance and Chebyshev distance, and puts different characteristics into the same measure, and finally comprehensively evaluates the matching of shared logistics resources and demand by using function matching degree, capacity matching degree and transaction matching degree.The method realizes the integration of different logistics links and different function parameters, can evaluate the whole logistics process or a certain link in the logistics process, realizes comprehensive analysis of the matching of logistics resource supply and demand, and is beneficial to improving the operation efficiency of the logistics network.

A resource supply system, comprising: a floating platform (900) arranged at a water surface; a gas compression device (100) arranged on the floating platform (900) and provided with, communicated with each other, a first gas inlet (110) and a first gas outlet (120; a gas expansion device (200) arranged on the floating platform (900) and used for electrically connecting to a power supply device, the gas expansion device (200) being provided with, communicated with each other, a second gas inlet (210) and a second gas outlet (220), and the second gas outlet (220) being used for discharging gas; a first heat exchange device (300) arranged on the floating platform (900) and provided with, communicated with each other, a first inlet (311) and a first outlet (312), the first inlet (311) being communicated with the first gas outlet (120), and the first outlet (312) being communicated with the second gas inlet (210); and a seawater distillation device (400), the seawater distillation device (400) being able to make a heat exchange with the first heat exchange device (300) and used for communicating with a water supply device.

This invention provides a resource optimization allocation method and system for environmental control in the industrialized cultivation of Ganoderma lucidum. By dividing the industrialized cultivation cycle of Ganoderma lucidum into stages and determining the environmental parameter thresholds for each stage, the growth characteristics of each stage are accurately matched, providing a quantifiable basis for environmental judgment. Based on historical Ganoderma lucidum cultivation data, a correlation model is established between environmental parameter deviations and resource adjustment amounts at each stage to ensure the rationalization of resource supply. By collecting real-time environmental parameters of industrialized Ganoderma lucidum cultivation, the deviation between the real-time environmental parameters and the environmental parameter thresholds of the current stage is determined, and the resource optimization parameters corresponding to the deviation values ​​are determined based on the correlation model, enabling timely resource control. By operating according to the resource optimization parameters and iteratively adjusting during the operation until the real-time environmental parameters meet the standards, the environmental parameters at each stage throughout the entire cultivation cycle are ensured to remain within the threshold range.

The invention discloses a model training detection method and electronic equipment, and relates to the technical field of model training detection, and the method comprises the steps: obtaining training task execution data of a target model and resource state data of a target operation entity; determining a current training trend of the target model and comparing the current training trend with an expected training trend of the target model; determining the training resource support degree of the target operation entity to the target model, and comparing the training resource support degree with the resource pressure intensity of the target model; in response to the deviation risk of training and the imbalance risk of resource supply, generating a training correction parameter to correct the training process of the target model and acquiring a training correction result; and in response to the training correction result representing that the training correction fails, determining that the target model is trained abnormally. According to the invention, the method can achieve the efficient detection of anomalies in the model training process, and at least solves a problem that the detection information is scattered, the postmortem analysis positioning lags behind, and the recoverable risk is misjudged as anomalies, thereby causing the waste of resources in the related technologies.

The invention discloses a network resource management system suitable for network slicing, relates to the technical field of network resource management, solves the problem that in the prior art, resource supply cannot be deeply monitored through service flow resource supply, and resource supply efficiency is abnormal, and particularly provides a network resource management system suitable for network slicing through whole-process closed-loop management of monitoring-optimization-decision-recovery. Three core values are realized: in a user level, full-link management and control from an application layer index to resource supply adjustment are realized, the problems of jamming, retransmission and the like are effectively reduced, and the experience stability of different service users is guaranteed; in the resource level, through accurate prediction, dynamic interference elimination and scientific recovery, resource scrambling, waste and idling are avoided, and efficient distribution and turnover of network resources are achieved; according to the system level, traditional static management and control are replaced by data driving, so that resource management of network slices is more intelligent and flexible, different requirements of different services can be met, and the bearing capacity and operation stability of the whole network are improved.

This invention proposes an automated construction organization design platform and its design method for water conservancy and hydropower projects. The platform includes a structured experience database system, a dynamic optimization calculation system for earthwork balance, an intelligent construction schedule generation system, an automated construction resource design system, and a 3D visualization and decision support system for the overall construction layout. The platform system uses the correlation data between building quantities and schedule time as a link to achieve data exchange and collaborative linkage. The design method includes: constructing and initializing the structured experience database system; automatically generating a construction schedule plan; outputting an earthwork balance scheme; automatically calculating and configuring labor force, work area, and facilities of each construction plant; generating a construction layout scheme and outputting it in 3D visualization. This invention realizes intelligent processing of the entire process of construction organization design calculation, resource supply planning, and site layout, significantly improving design efficiency and scheme accuracy, while also possessing good operability and system integration.

This application provides an intelligent routing function optimization method and system based on an intelligent vehicle power domain control platform, belonging to the field of intelligent vehicle electronic control technology. It addresses the problems of resource contention, excessive processor load, insufficient real-time performance, and poor scalability in in-vehicle network routing functions in related technologies. This method employs an architecture that separates the management plane and data plane. It pre-generates routing numbers encoding source, path, and destination information for communication relationships, and quickly extracts information based on these numbers through bit operations to execute routing decisions. Furthermore, it combines doubly linked list cache management, multi-core affinity scheduling, and multi-level resource provisioning strategies to achieve efficient cache resource management, significant optimization of processor load, and an overall improvement in system real-time performance and reliability.

The invention discloses a dynamic task scheduling method based on edge computing and cloud edge collaboration, which relates to the technical field of edge computing, and comprises the following steps: when an edge node receives a task request, analyzing the task request through a neural symbol engine, and outputting a task priority mark, a resource demand vector and a dynamic scheduling strategy; carrying out edge local resource matching judgment, and if matching fails, generating a cloud edge collaborative scheduling parameter packet; according to the resource allocation parameter and the target node identifier, dynamically calculating a resource allocation rate through a gene expression model, and calculating a resource quota based on the resource allocation rate; and based on the resource quota, the target node executes task processing and monitors a resource utilization rate deviation, updates a gene expression model parameter based on the resource utilization rate deviation, and feeds back the updated gene expression model parameter to the neural symbol engine to iteratively optimize the dynamic scheduling strategy. According to the method, through enzyme kinetics differential equation modeling and Runge-Kutta numerical solution, load-sensitive elastic resource supply is realized.

This application relates to a modular non-human primate rearing cage, belonging to the technical field of animal husbandry. It includes a cage frame, a resource supply system, a hygiene and cleaning system, an activity and enrichment system, and a connection and management system. The cage frame includes a cage base, a cage top, and cage side frames. The resource supply system includes a food supply device and a water supply device detachably installed on the cage frame. The connection and management system includes a connecting channel on the cage side frames and a connecting cage door movably installed on the cage side frames for changing the opening and closing state of the connecting channel. The activity and enrichment system includes a lookout component installed on the cage side frames, a climbing component located between the lookout component and the cage base, and multiple monitoring units installed on the cage top frame. This application can improve the husbandry and management efficiency of non-human primates.

The invention provides a risk prevention and control method and device, a medium and a product. The method comprises the following steps: acquiring marketing data from a plurality of platforms; extracting resource data and resource supplier data carried by each resource from the marketing data; the resource data comprises a resource ID and a marketing ID, and the resource supplier data comprises an account ID of a resource account; constructing data structure models in one-to-one correspondence with the account IDs, and storing the account IDs, the resource IDs of the resources corresponding to the account IDs and the marketing IDs of the marketing into the corresponding data structure models; for each account ID, according to the data structure model to which the account ID belongs, monitoring whether a resource account of the account ID accords with a set early warning rule; and outputting an early warning message of the to-be-early-warned resource account under the condition that the to-be-early-warned resource account conforming to the resource early warning rule is found through monitoring.

The invention discloses a source network storage cooperative game scheduling method considering node flexibility, and aims to solve the problems of new energy output and load peak valley dislocation, climbing mismatching and new energy discarding caused by insufficient node flexibility margin and source load uncertainty of a power system. According to the invention, an independent scheduling main body including a virtual power plant, new energy and a power grid is built; with operation cost minimization as a target, a target function covers power market interaction, equipment operation, flexibility, carbon emission cost and new energy consumption assessment punishment cost of each main body; five models of source-load multivariate association mapping, flexibility resource supply capability, node flexibility demand, node flexibility evaluation and cooperative game main body scheduling are constructed, and power generation power and energy storage operation constraints are defined; cooperative game loop iteration solution is adopted, the optimal scheduling result of each main body is output, the flexibility margin of the system is improved, the new energy disposal amount and the whole system operation cost are reduced, low-carbon and efficient operation of a power system is assisted, and the method is suitable for a high-proportion wind-solar new energy power distribution network.