CN114757797B - Power grid resource service central platform architecture method based on data model drive - Google Patents
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Abstract
The invention provides a data model-driven power grid resource service middleware framework method, which comprises the following steps: performing entity extraction on real-time service requirements to obtain a core entity and an application entity; establishing a unified data model aiming at the core class entity; constructing a shared service center based on the application entity, and determining a service link between the shared service centers; performing historical service portrayal according to historical service indexes of a shared service center in a service link, performing real-time service portrayal according to real-time service indexes of all the shared service centers, and generating a service list to be distributed with elastic resources; and performing service matching on the service list to be elastically distributed, updating the resource pool, and dynamically optimizing the architecture of the power grid resource service center station based on an elastic distribution strategy. The invention realizes dynamic elastic resource allocation of the power grid resource service center by combining historical service index characteristics and real-time service index characteristics and service characteristics, thereby improving the efficiency of providing application services.
Description
Technical Field
The invention belongs to the field of big data platforms, and particularly relates to a power grid resource service middle platform architecture method based on data model driving.
Background
The power grid data model is the basis for realizing power grid informatization and digitization, is a core support for changing a business mode and promoting power grid technology upgrading, and is a basic premise for promoting equipment management, equipment operation and maintenance and equipment overhaul to participate in energy informatization and virtualization. In order to change the mode of the power grid resource service and promote the upgrading of the power grid technology, and to promote the participation of power grid resources, equipment management, equipment operation and maintenance and equipment overhaul in energy informatization and virtualization, it is urgently needed to construct a uniform power grid resource service middle platform to fuse power grid resource service data so as to eliminate a data maintenance island and break a data access barrier.
The power grid resource service center platform is an aggregation with standardized model data, standardized service interfaces and basic common service capability, and is a novel infrastructure platform different from a traditional single service system. In order to bring the data benefit of the power grid resource service center into play, a large number of services related to power grid resources, equipment and services are fused in the power grid resource service center, for example, management services for the power grid resources and the equipment, real-time detection services for power grid operation, dynamic image sharing services for power grid topology and the like.
Disclosure of Invention
In the aspect of power grid service center platform construction, service departments are mostly used as units to independently maintain respective data platforms at the present stage, and the difficulty of constructing the power grid service center platform is increased due to the fact that the aspects of modeling modes, data format definitions and the like are inconsistent, and in order to solve the existing defects and shortcomings, the invention provides a power grid resource service center platform architecture method based on data model driving, which comprises the following steps:
s100: acquiring a real-time service demand, and performing entity extraction on the real-time service demand to obtain a core entity and an application entity in the real-time service demand;
s200: respectively establishing a unified data model aiming at the power grid operation logic, the full life cycle of the power equipment and the operation and maintenance overhaul process of the core entity, wherein the unified data model comprises a power grid resource model, an equipment asset model and an operation and maintenance overhaul model;
s300: establishing a shared service center in a power grid resource service central station based on the application entity, determining service links among the shared service centers corresponding to the unified data model, and forming a power grid resource service central station framework through the service links;
s400: performing historical service portrayal according to historical service indexes of the shared service centers in the service link, performing real-time service portrayal according to real-time service indexes of all the shared service centers in the power grid resource service middling, and dynamically generating a to-be-elastic resource distribution service list by combining the historical service portrayal and the real-time service portrayal;
s500: and based on the service characteristics of the service link, performing service matching on the service list to be elastically distributed, updating the resource pool according to the service matching result, and dynamically optimizing the architecture of the power grid resource service middling stage through the resource pool based on the elastic distribution strategy.
Optionally, S200 includes:
establishing a power grid resource model according to the power grid operation logic of the core class entity, wherein the power grid resource model comprises a logic model of the network topology and the electrical parameters of primary equipment and secondary equipment of a power grid;
establishing an equipment asset model according to the whole life cycle of the power equipment of the core entity, wherein the equipment asset model comprises a life stage model of equipment such as purchase, warehousing, delivery, maintenance, overhaul and retirement;
and establishing an operation and maintenance model according to the operation and maintenance process of the core entity, wherein the operation and maintenance model comprises a process model of maintenance tool management, maintenance plan management, operation and maintenance plan management, acceptance management and equipment scrapping management.
Optionally, the shared service center includes a primary center for maintaining static grid account information, a secondary center corresponding to the application entity and used for dynamically managing the middle station data, and a tertiary center used for power grid service expansion application, where the primary center includes a power grid asset center, a power grid resource center, a power grid topology center, and a work resource center, the secondary center includes a model management center, a measurement point management center, a metering application center, and a power grid environment center, and the tertiary center includes a power grid pattern center, an equipment state center, a power grid analysis center, and a work management center.
Optionally, the S300 includes:
for the power grid resource model, the service links comprise that the power grid resource center establishes service links with the power grid graphic center, the power grid analysis center and the operation management center respectively, and the power grid topology center establishes service links with the power grid graphic center, the equipment state center and the power grid analysis center respectively;
for the equipment asset model, the service link comprises a service link established by the power grid asset center and the equipment state center and the operation management center respectively;
for the operation maintenance overhaul model, the service link comprises a service link established between the operation resource center and the operation management center.
Optionally, the S400 includes:
s410: respectively inputting historical service indexes into different time series prediction models to obtain a peak period prediction interval of a service link, and generating a prediction recommendation result of a peak period service portrait and a low peak period service portrait according to the service condition of the service link in the peak period prediction interval;
s420: according to the real-time service index, performing peak-to-peak and low-to-peak real-time detection on the services of all the shared service centers in the power grid resource service center to obtain real-time recommendation results of a peak-to-peak service portrait and a low-to-peak service portrait;
s430: filtering and combining the prediction recommendation result and the real-time recommendation result to obtain peak recommendation service and low peak recommendation service;
s440: and respectively filtering services which do not need capacity expansion in the peak period recommendation service and services which do not need capacity reduction in the low peak period recommendation service, and generating a list of services to be distributed with elastic resources by the filtered peak period recommendation service and the filtered low peak period recommendation service.
Optionally, the S410 includes:
respectively inputting the historical service indexes into a plurality of different time sequence prediction models to obtain prediction results of a plurality of peak period intermediate values;
and carrying out weighted average processing on the prediction result to obtain a reference time value, and calculating a peak period prediction interval of the service link by combining the average time length of the service peak period of the power grid resource service center station and the time offset.
Optionally, the peak prediction interval is [ recomm _ time-average _ time/2-offset _ time/2], recomm _ time + average _ time/2 + offset _ time/2], where recomm _ time represents a reference time value, average _ time represents an average duration of the service peak, and offset _ time represents a time offset.
Optionally, the S420 includes:
and acquiring real-time operation characteristics of each service based on a preset period, if the times that the real-time operation characteristics continuously exceed a preset threshold reach a preset value, marking the service as a peak service, otherwise, marking the service as a low peak service.
Optionally, the S500 includes:
distinguishing a peak period recommendation service and a low peak period recommendation service in a to-be-elastic resource allocation service list, determining the service characteristics of the peak period recommendation service, and matching the peak period recommendation service and the low peak period recommendation service based on the service characteristics to obtain the low peak period recommendation service with the same service characteristics;
the idle resources of the low peak period recommended service obtained by matching are recycled into the resource pool, and the idle resources in the resource pool are allocated to the high peak period recommended service by generating an elastic allocation strategy;
wherein the service characteristics include CPU intensive, IO intensive, and GPU intensive.
Optionally, the historical service index and the real-time service index include IO operation times of the service, a CPU utilization rate, a network delay, a real-time QPS, a QPS preset threshold, an average delay of the request, a dependent service, and an average time-consuming threshold.
The technical scheme provided by the invention has the beneficial effects that:
the design of a data model top-level framework of the power grid resource service center platform is oriented to uniformly fuse the power grid operation logic, the power equipment life cycle and the operation, maintenance and repair process of a power grid to the service framework of the power grid resource service center platform in a model-driven mode, breaks through the barriers of data access and application, gives consideration to the application service of the power grid resource service center platform to users, and adds a shared service center in the framework of the power grid resource service center platform.
In order to solve the problem of service resource allocation caused by an excessively complicated architecture of the power grid resource service center, dynamic elastic resource allocation of the power grid resource service center is realized by a service portrayal means combining historical and real-time service index characteristics and service characteristics of each shared service center in the power grid resource service center, and the efficiency of providing application services by the power grid resource service center is improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the description of the embodiments will be briefly introduced below, and it is apparent that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a data model-driven method for building a platform in a power grid resource service according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a core class entity and an application class entity;
fig. 3 is a schematic diagram of service links between shared service centers.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Embodiment as shown in fig. 1, this embodiment provides a data model-driven power grid resource service middleware framework method, including:
s100: acquiring a real-time service demand, and performing entity extraction on the real-time service demand to obtain a core entity and an application entity in the real-time service demand;
s200: respectively establishing a unified data model aiming at the power grid operation logic, the power equipment life cycle and the operation and maintenance overhaul process of the core entity, wherein the unified data model comprises a power grid resource model, an equipment asset model and an operation and maintenance overhaul model;
s300: establishing a shared service center in a power grid resource service center based on the application entity, determining service links among the shared service centers corresponding to the unified data model, and forming a power grid resource service center framework through the service links;
s400: acquiring historical service indexes of the shared service centers in the service link, performing service portrayal by combining the real-time detection of the peak period and the low peak period of all the shared service centers in the power grid resource service center, and dynamically generating a to-be-elastic resource distribution service list according to the result of the service portrayal;
s500: and based on the service characteristics of the service link, performing service matching on the service list of the elastic resource allocation to be processed, updating the resource pool according to the service matching result, and dynamically optimizing the architecture of the power grid resource service center platform through the resource pool based on the elastic allocation strategy.
In this embodiment, the grid service center is an internet data centralized processing platform that integrates standardized model data, standardized service interfaces, and basic common service capabilities, and is a novel infrastructure platform that is different from a conventional monolithic service system. In the power grid service center, the power grid services can be integrated on a unified general data platform in a sharing application mode, so that the effects of eliminating a power grid service data island and breaking a data access barrier are achieved.
The embodiment provides a unified modeling mode facing a power grid resource service center from the perspective of data unified modeling, opens integration barriers of power grid resource data, equipment assets and measurement and acquisition data, eliminates data islands, promotes data quality improvement and exerts data efficacy benefits. Meanwhile, a series of sharing application centers are arranged to promote standard and comprehensive equipment description, accurate and reasonable power grid connection, clear data organization logic and dynamic sharing of graphic services, promote multi-professional cooperation and full-service fusion and comprehensively promote the establishment of a power grid digital middle station.
In this embodiment, the core class entity and the application class entity are shown in fig. 2, where the core class entity is a basic service element of a power grid, and includes:
(1) power System resources (Power System resources), such as generator sets, transmission lines, substations and auxiliary equipment, converter station direct current lines, and Power grid unit modules of Power grid topology;
(2) equipment assets (Asset), such as individual power devices for transmission (overhead, cable, pipe network), transformation (primary and auxiliary devices), distribution, usage, etc.;
(3) the operation and inspection services (Activity Record, Document), such as work plan and schedule, process and procedure, result and report, etc. specific services of the power grid.
The application entity is various data elements corresponding to auxiliary services of the power grid, and comprises the following steps:
(1) measurement data (Measurement) such as electric quantity Measurement values of real-time voltage, real-time current and the like;
(2) location (Location), such as asset Location, weather Location, etc.;
(3) environmental Information such as lightning, icing, mountain fire, typhoon, geological disasters, etc.
In this embodiment, Power System resources (Power System resources), equipment assets (Asset), and inspection activities (Activity Record, Document) are mainly used as cores, and Measurement data (Measurement), Location (Location), and environment (Environmental Information) profile are associated with core class entities for describing the influence of the electrical environment and the natural environment on the Power grid.
In this embodiment, the unified data model is respectively established corresponding to the power grid operation logic, the power equipment full life cycle, and the operation and maintenance process of the 3 core entities, that is, the unified data model includes a power grid resource model frame, an equipment asset model frame, and an operation and maintenance model frame. The power grid operation logic of the core entity comprises the logic relation of the network topology and the electrical parameters of primary equipment and secondary equipment of the power grid, the full life cycle of the power equipment of the core entity comprises stage models of equipment purchasing, warehousing, commissioning, maintenance, overhaul and retirement, and the operation, maintenance and overhaul process of the core entity comprises the processes of maintenance tool management, maintenance plan management, operation and maintenance plan management, acceptance management and equipment scrapping management.
Therefore, in this embodiment, the unified data model corresponding to the power grid resource is a logical relationship model, which is used to logically model the primary devices and the secondary devices of all voltage classes from the operating perspective of the power grid, and focuses on describing the functions, electrical parameters and network topology of the power grid devices, and the main contents include the container relationship, connection relationship, device electrical parameters, electrical quantity measurement value, and the like of the power grid primary devices.
The unified data model corresponding to the equipment assets is an equipment asset management stage model, which is a model for modeling the assets from the perspective of a single physical entity and reflecting the whole life cycle processes of purchasing, warehousing, commissioning, maintenance, overhaul, retirement and the like of the assets, wherein the asset model establishes and removes an association relationship through commissioning and retirement business processes of the equipment and a power grid resource model, and the main contents of the asset model comprise an asset container relationship, primary and secondary equipment asset parameters, an equipment asset state, environmental conditions, an asset evaluation analysis model and the like.
The unified data model of the corresponding operation and maintenance service is an operation and maintenance process model, which is a unified modeling of service objects related to equipment management from the viewpoints of operation and maintenance, evaluation, scrapping and the like of power grid equipment, and the main contents of the unified data model comprise transaction management operation tool management, plan management, operation and maintenance management, detection management, acceptance management, evaluation management, state monitoring, scrapping management, technical improvement and overhaul management, engineering management and the like.
On the basis of the unified data model, the unified data model is specifically extended through a shared application center, and therefore, the present embodiment decouples the power grid service function into 12 shared application centers based on the application entity, where a relationship architecture of a primary center, a secondary center, and a tertiary center is shown in fig. 3, the primary center includes a power grid asset center, a power grid resource center, a power grid topology center, and an operation resource center, the secondary center includes a model management center, a measurement point management center, a metering application center, and a power grid environment center, and the tertiary center includes a power grid pattern center, an equipment state center, a power grid analysis center, and an operation management center. The power grid asset center, the power grid resource center, the power grid topology center and the operation resource center mainly maintain static power grid account information, the model management center realizes the management function of various power grid assets, power grid resources and operation and inspection service models of the center, the measurement point management center, the metering application center and the power grid environment center mainly maintain dynamic power grid acquisition data, and the power grid graphic center, the equipment state center, the power grid analysis center and the operation management center are mainly close to power grid services. Specifically, the method comprises the following steps:
the power grid asset center surrounds real assets such as power grid equipment, channel facilities, overhead line corridors, auxiliary systems and the like, uses lean management as a main line, and supports business development such as operation, overhaul, maintenance, supplier performance and the like. The functions of newly adding and transferring resources of the power grid assets, managing spare parts, depreciating, scrapping for retirement, checking, inquiring and maintaining information and the like are provided externally; the core services of the method comprise asset information query, asset life cycle management and asset memorial management.
The power grid resource center aims at describing an objective power grid, and realizes the uniform maintenance of power grid resources and the orderly management of system-level, transformer substation-level, feeder-level and station-area-level power grid resources by abstracting and maintaining objects, attributes and relationships of power grid primary equipment, auxiliary measuring equipment, terminal equipment, direct-current equipment, pumped storage, conventional hydropower, power generation equipment, distributed power supplies and other equipment. The core service of the method comprises the functions of inquiry, format conversion, model mapping, maintenance and the like of power grid resources. The core services of the system comprise resource query and resource management.
The power grid topology center realizes the management of power grid topology network relation under multiple states of power grid planning, construction and operation through maintenance, analysis and storage of power grid topology; the system has the functions of providing the shortest communication path, tracing the power supply, analyzing the power supply stopping range, analyzing the communication area, analyzing the power supply radius, analyzing the topology of the large feeder line and the low-voltage transformer area, analyzing the communication line, analyzing the equipment tree, analyzing the communication between stations, analyzing a station diagram, analyzing the indoor diagram and the like.
The operation resource center realizes resource allocation, allocation and account management based on operation resources such as vehicles, tools, instruments, operators, unmanned aerial vehicles and robots. The core service includes job resource inquiry, statistics, addition, deletion, modification and positioning.
The model management center immediately meets the requirements of power grid service and computational analysis through maintaining, updating and releasing metadata. And the normalized authentication is provided in the aspects of model compliance, relevance, reliability, availability, connectivity, graph-model consistency, information interaction and the like, and the standardization of the power grid model is guaranteed. The core services of the method comprise metadata management, model detection and version management.
The measuring point management center carries out operation and maintenance management on sensing measuring points such as electrical quantity information, equipment operation state information, operation environment information, topology identification information and the like, defines measuring points of power grids of various voltage levels, constructs an association relation between the measuring points and primary equipment, and realizes access management, measuring point analysis management, measuring point equipment monitoring and configuration management of the measuring point equipment. The functions of inquiring and maintaining machine accounts, measurement data, abnormal events and the like of sensing measuring points such as measuring equipment, sensing equipment, remote sensing equipment and the like are provided to the outside; the core services of the method comprise measurement point ledger management, measurement data query, event subscription and query.
The metering application center realizes access management and configuration management of metering equipment, maintenance of incidence relation between metering points and superior equipment and alarm management of metering abnormal events around the development of metering related services. The core services of the method comprise real-time acquisition information of the metering equipment, freezing acquisition information and inquiry of event information.
The power grid environment center integrates pre-warning data of environmental information such as thunder, icing, mountain fire, typhoon, geological disasters and the like which influence the safe operation of a power grid, and realizes the analysis of natural disasters of power grid equipment and a line channel. The core services of the method comprise inquiry of power grid environment information, monitoring and early warning, rule analysis, risk assessment and medium-long term forecast analysis.
The power grid graphic center provides integration and one-stop graphic service for each shared application center, cross-professional application and multi-state business application, and flexible front-end display, friendly man-machine interaction and multi-dimensional analysis of graphics and application are realized. The core services of the system comprise space inquiry, GIS map, thematic map and thematic map.
The equipment state center takes power grid equipment as a main body, and realizes multidimensional analysis and evaluation of the equipment operation state based on asset information, resource information, topology information, equipment operation information, equipment body perception information, environment information and the like. The equipment state center provides the functions of inquiring the running state of the equipment, carrying out multi-dimensional evaluation, early warning, inquiring an abnormal equipment list and the like.
The power grid analysis center realizes the whole network analysis from the power supply, the power grid to the user through the information of power grid pattern model, measurement type, operation type, meteorological environment type and the like. The core services of the method comprise safety analysis, economic analysis, reliability analysis, running state analysis, state estimation and power flow analysis related to the power grid.
The operation management center is around the power grid equipment, according to the operation standard, combines the actual demand of scene, and accurate the standardization management of controlling operation workflow and business logic realizes all kinds of operations, supports the standard development of power grid operation such as tour, detection, trouble rush-repair, disappearance, maintenance plan execution, test, live working. The core services of the system comprise defect analysis and query, maintenance plan query and maintenance, fault query, inspection plan query and maintenance, hidden danger analysis and query, live working plan query and maintenance and test record query and maintenance.
In this embodiment, a basic architecture of a power grid resource service center is constructed through the above process, and a service link between shared service centers is formed according to a service requirement on the basis of the basic architecture, including:
for the power grid resource model, the service links comprise that the power grid resource center establishes service links with the power grid graphic center, the power grid analysis center and the operation management center respectively, and the power grid topology center establishes service links with the power grid graphic center, the equipment state center and the power grid analysis center respectively;
for the equipment asset model, the service link comprises a service link established by the power grid asset center and the equipment state center and the operation management center respectively;
for the operation maintenance overhaul model, the service link comprises a service link established between the operation resource center and the operation management center.
It should be noted that the interaction relationship between the shared application centers in this embodiment is not limited to the relationship shown in fig. 3, fig. 3 only shows one definition of the interaction relationship in this embodiment, and in other embodiments, the definition of the interaction relationship is different with the difference of the unified data model.
The power grid resource service central platform architecture based on the data model and aiming at different service requirements is obtained quickly, the defects of repeated development, dispersed storage and multiple interaction of a traditional single application system are overcome, unified standard and homologous maintenance are achieved by integrating various power grid resource data of a power transmission line, a transformer substation, a medium-voltage feeder line, a low-voltage platform area, users, a distributed power supply, a micro grid and the like, common services are fused to the central platform, basic and stable service capacity is formed, and flexible construction and quick iteration of various service applications can be supported.
In order to solve the problem of complex and variable service resource allocation in the power grid resource service, the embodiment implements flexible resource allocation by performing service representation on the service link. In this embodiment, the main purpose of constructing a service profile is to detect and predict the peak periods and the low periods of the service. Since the services have different peak periods and peak periods, the redundant resources of the services in the peak periods can be recovered and allocated to the services in other peak periods.
Specifically, the S400 includes:
s410: respectively inputting the historical service indexes into different time sequence prediction models to obtain a peak period prediction interval of a service link, and generating prediction recommendation results of peak period service and low peak period service according to the service condition of the service link in the peak period prediction interval;
s420: performing peak-to-peak and low-to-peak real-time detection on all services of a power grid resource service center to obtain real-time recommendation results of peak-to-peak services and low-to-peak services;
s430: filtering and combining the prediction recommendation result and the real-time recommendation result to obtain peak recommendation service and low peak recommendation service;
s440: and respectively filtering services which do not need capacity expansion in the peak period recommendation service and services which do not need capacity reduction in the low peak period recommendation service, and dynamically generating a service list to be elastically distributed according to filtering conditions.
Wherein the S410 comprises:
respectively inputting the historical service indexes into a plurality of different time sequence prediction models to obtain prediction results of a plurality of peak period intermediate values; in this embodiment, the time series prediction model includes Auto-ARIMA, Neural _ Prophet, and explicit smoothening models;
carrying out weighted average processing on the prediction result to obtain a reference time value, and calculating a peak period prediction interval of a service link by combining the average time length of the service peak period and the time offset of a station in the power grid resource service, wherein the peak period prediction interval is [ recomm _ time-average _ time/2-offset _ time/2, recomm _ time + average _ time/2 + offset _ time/2], the recomm _ time represents the reference time value, the average _ time represents the average time length of the service peak period, and the offset _ time represents the time offset.
The S420 includes:
and acquiring real-time operation characteristics of each service based on a preset period, if the times that the real-time operation characteristics continuously exceed a preset threshold reach a preset value, marking the service as a peak service, and otherwise, marking the service as a low peak service.
In this embodiment, to prevent service jitter, S420 is executed every 20S, and loops for 2-4 times, and if the real-time running characteristic exceeds the preset threshold for 3 consecutive times, it is determined as peak service.
In this embodiment, in order to accurately screen out a service that definitely needs to elastically allocate resources, the specific process of filtering and merging the predicted recommendation result and the real-time recommendation result in S430 is as follows:
and determining peak period service H1 and low period service L1 in the prediction recommendation result, and determining peak period service H2 and low period service L2 in the real-time recommendation result. During filtering and combining, the intersection of H1 and H2 is used as a peak period recommendation service, and because the probability that the services in the intersection are in the peak period in the current and future is higher, the resource shortage of the services is more serious, and the services can be used as the peak period recommendation service; the union of L1 and L2 is used as a low peak recommended service, and the centralized service ensures that the service has the capability of scheduling out idle resources. Through the process, the peak-period service needing to be allocated with the resources and the low-period service capable of allocating the resources in the service link can be accurately screened out. Compared with a conventional elastic resource allocation mode, the present embodiment accurately limits the range of the services participating in the elastic allocation through S400, and can be more suitable for the power grid resource service middlings with frequent service condition changes.
In this embodiment, the to-be-elastic resource allocation service list is synchronized to Redis at a timing by a data synchronization service, and then the Redis sends a task at a timing to trigger the elastic resource allocation and recovery execution engine of the grid resource service to execute S500.
The S500 includes:
distinguishing a peak period recommended service and a low peak period recommended service in a to-be-elastic resource allocation service list, determining the service characteristics of the peak period recommended service, and screening out the low peak period recommended service with the same service characteristics;
recycling the screened idle resources of the low peak period recommendation service into a resource pool, and allocating the idle resources in the resource pool to the high peak period recommendation service by generating an elastic allocation strategy;
wherein the service characteristics include CPU intensive, IO intensive, and GPU intensive.
In this embodiment, the elastic allocation policy is configured based on service characteristics of the service link, for example, the current service link is determined to be GPU-intensive because it relates to a grid graphic center in a three-level application. When elastic resource allocation is carried out, the elastic allocation strategy preferentially selects GPU-intensive low peak period recommended service in a service list to be elastically allocated, and preferentially selects idle resources of services belonging to the service link in the low peak period recommended service, so that the idle resources are ensured to meet service characteristics, and the resource allocation efficiency is improved to the maximum extent.
In this embodiment, the elastic allocation policy is configured in a Json format, and is synchronized into Redis through a data synchronization service.
The above embodiments have been described with reference to the accompanying drawings, which are not intended to limit the scope of the invention.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A data model-driven power grid resource service central station architecture method is characterized by comprising the following steps:
s100: acquiring a real-time service demand, and performing entity extraction on the real-time service demand to obtain a core entity and an application entity in the real-time service demand;
s200: respectively establishing a unified data model aiming at the power grid operation logic, the full life cycle of the power equipment and the operation and maintenance overhaul process of the core entity, wherein the unified data model comprises a power grid resource model, an equipment asset model and an operation and maintenance overhaul model;
s300: establishing a shared service center in a power grid resource service central station based on the application entity, determining service links among the shared service centers corresponding to the unified data model, and forming a power grid resource service central station framework through the service links;
s400: performing historical service portrayal according to historical service indexes of the shared service centers in the service link, performing real-time service portrayal according to real-time service indexes of all the shared service centers in the power grid resource service middling, and dynamically generating a to-be-elastic resource distribution service list by combining the historical service portrayal and the real-time service portrayal;
s500: performing service matching on the service list to be elastically distributed based on the service characteristics of the service link, updating the resource pool according to the service matching result, and dynamically optimizing the architecture of the power grid resource service center platform through the resource pool based on the elastic distribution strategy;
the core class entity is a basic service element of the power grid and comprises the following steps: power grid resources, equipment assets, and operation and inspection services; the application entity is various data elements corresponding to auxiliary services of the power grid, and comprises the following steps: measuring data, position and environment;
the S200 includes:
establishing a power grid resource model according to the power grid operation logic of the core class entity, wherein the power grid resource model comprises a logic model of the network topology and the electrical parameters of primary equipment and secondary equipment of a power grid;
establishing an equipment asset model according to the whole life cycle of the power equipment of the core entity, wherein the equipment asset model comprises life stage models of equipment purchasing, warehousing, commissioning, maintenance, overhauling and decommissioning;
establishing an operation maintenance model according to the operation maintenance process of the core entity, wherein the operation maintenance model comprises a process model of maintenance tool management, maintenance plan management, operation maintenance plan management, acceptance check management and equipment scrapping management;
the S300 includes:
for the power grid resource model, the service links comprise that a power grid resource center respectively establishes service links with a power grid graphic center, a power grid analysis center and an operation management center, and a power grid topology center respectively establishes service links with the power grid graphic center, an equipment state center and the power grid analysis center;
for the equipment asset model, the service link comprises a power grid asset center, and the service link is respectively established with an equipment state center and an operation management center;
for the operation maintenance and overhaul model, the service link comprises a service link established by the operation resource center and the operation management center;
the S400 includes:
s410: respectively inputting historical service indexes into different time series prediction models to obtain a peak period prediction interval of a service link, and generating prediction recommendation results of a peak period service portrait and a low peak period service portrait according to service conditions of the service link in the peak period prediction interval;
s420: according to the real-time service index, performing peak-to-peak and low-to-peak real-time detection on the services of all the shared service centers in the power grid resource service center to obtain real-time recommendation results of a peak-to-peak service portrait and a low-to-peak service portrait;
s430: filtering and combining the predicted recommendation result and the real-time recommendation result to obtain a peak period recommendation service and a low peak period recommendation service;
s440: filtering services which do not need capacity expansion in the peak period recommendation services and services which do not need capacity reduction in the low peak period recommendation services respectively, and generating a list of services to be distributed with elastic resources by the filtered peak period recommendation services and the filtered low peak period recommendation services;
the S500 includes:
distinguishing a peak period recommendation service and a low peak period recommendation service in a to-be-elastic resource allocation service list, determining service characteristics of the peak period recommendation service, and matching the peak period recommendation service and the low peak period recommendation service based on service characteristics to obtain a low peak period recommendation service with the same service characteristics;
the idle resources of the low peak period recommended service obtained by matching are recycled into the resource pool, and the idle resources in the resource pool are allocated to the high peak period recommended service by generating an elastic allocation strategy;
wherein the service characteristics include CPU intensive, IO intensive, and GPU intensive.
2. The method according to claim 1, wherein the shared service center includes a primary center for maintaining static grid ledger information, a secondary center corresponding to the application entity for dynamically managing the substation data, and a tertiary center for grid service development application, wherein the primary center includes a grid asset center, a grid resource center, a grid topology center, and a work resource center, the secondary center includes a model management center, a measurement point management center, a metering application center, and a grid environment center, and the tertiary center includes a grid pattern center, an equipment state center, a grid analysis center, and a work management center.
3. The data model-driven grid resource service staging architecture method according to claim 1, wherein the step S410 includes:
respectively inputting the historical service indexes into a plurality of different time sequence prediction models to obtain prediction results of a plurality of peak period intermediate values;
and carrying out weighted average processing on the prediction result to obtain a reference time value, and calculating a peak period prediction interval of a service link by combining the average time length of the service peak period of the power grid resource service middlebox and the time offset.
4. The data model-driven power grid resource service center architecture method according to claim 3, wherein the peak prediction interval is [ recomm _ time-average _ time/2-offset _ time/2, recomm _ time + average _ time/2 + offset _ time/2], where recomm _ time represents a reference time value, average _ time represents an average time duration of the peak service period, and offset _ time represents a time offset.
5. The method according to claim 1, wherein the S420 includes:
and acquiring real-time service indexes of each service based on a preset period, if the times that the real-time service indexes continuously exceed a preset threshold reach a preset value, marking the service as a peak service, and otherwise, marking the service as a low peak service.
6. The data model-driven power grid resource service middleware framework method according to claim 1, wherein the historical service index and the real-time service index comprise IO operation times of service, CPU utilization rate, network delay, real-time QPS, QPS preset threshold, request average delay, dependent service and average time consumption threshold.
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