CN110610292B - Cloud service-oriented power grid primary model collaborative modeling method and device - Google Patents
Cloud service-oriented power grid primary model collaborative modeling method and device Download PDFInfo
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- CN110610292B CN110610292B CN201910740664.4A CN201910740664A CN110610292B CN 110610292 B CN110610292 B CN 110610292B CN 201910740664 A CN201910740664 A CN 201910740664A CN 110610292 B CN110610292 B CN 110610292B
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Abstract
The invention discloses a cloud service-oriented power grid primary model collaborative modeling method and device, wherein the method comprises the following steps: determining a subscription scope; generating a derivative canvas; automatically generating corresponding derivative canvas microservices according to user information of different users so as to maintain the model and update the model to the main canvas; detecting whether the data of the derivative canvas updated by each user has conflict or not; if the conflict exists, combining the conflict data by applying a model dynamic combination method, otherwise, judging whether the updated model has errors; and if no error exists, updating, and issuing the updated main canvas model to the corresponding derivative canvas micro-service, otherwise, feeding error data back to the corresponding derivative canvas service. According to the method provided by the embodiment of the invention, the cloud service mode-oriented power grid primary equipment model collaborative modeling can be realized, so that the fast, accurate and efficient collaborative modeling of multiple users can be supported in the cloud service mode.
Description
Technical Field
The invention relates to the technical field of power system relay protection, in particular to a cloud service-oriented power grid primary model collaborative modeling method and device.
Background
With the rapid development of cloud computing technology and the successful application thereof in the field of internet commerce, the power informatization construction of the power industry gradually develops the 'clouding' of various business systems, and the establishment of a power informatization system based on a cloud service mode becomes one of the key works of the current power industry. The relay protection setting calculation service is one of indispensable specialties of a scheduling center, and the traditional deployment and application modes of servers/clients, which are applied by professional analysis systems at present, are limited by the influences of development tools, operation environments, operation logics and the like of the systems, so that the existing systems cannot be directly subjected to cloud processing and cannot meet the requirements of scheduling information construction.
In the related technology, a basic model oriented to the application of a relay protection setting calculation service related system is a power grid primary equipment model, and all services such as setting value calculation, setting value order management and the like are developed based on the power grid primary equipment model. In the conventional setting calculation system in the industry, when a primary power grid model is maintained, operation conflicts and data conflicts caused by a multi-user application system are avoided by manually triggering a plant station locking mechanism and a main graph locking mechanism, although the accuracy of the system model can be guaranteed, other users cannot maintain and apply corresponding models when graphs are locked, and whether the models are in a locking state or not is manually controlled, so that the cooperative modeling of the primary power grid model in the system is difficult to realize; influence the whole application efficiency of system and user's work efficiency to a certain extent to along with the continuous introduction of cloud computing, little service technology, information-based system "clouding" work is constantly promoted, and current station locking, main map locking mechanism have obvious technical defect under the cloud service system: firstly, locking and unlocking are triggered manually, original copy adding, deleting and modifying operations are carried out on the same canvas in the drawing process, when a single user carries out current model maintenance, other users cannot carry out model adding, deleting and modifying, and the advantage of the cloud service facing multi-user application is limited to a certain extent; secondly, when the existing system in the prior art carries out model maintenance, the interaction of obtaining and storing data is carried out only once with the database, all the maintenance data is completed at the client, no monitoring mechanism exists, other users cannot know whether the model is available in advance, abnormal risk of state judgment exists when multiple users lock the graph at the same time, and the existing system cannot automatically combine the maintenance models of the multiple user terminals.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the first purpose of the invention is to provide a cloud service-oriented power grid primary model collaborative modeling method, which can be used for carrying out cloud service-oriented power grid primary equipment model collaborative modeling, so that the fast, accurate and efficient collaborative modeling of multiple users can be supported in a cloud service mode.
The invention aims to provide a cloud service-oriented power grid primary model collaborative modeling device.
A third object of the invention is to propose an electronic device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a cloud service-oriented power grid primary model collaborative modeling method, including the following steps: determining the attention range of a user selected model, subscribing the required model and determining the subscription range; generating a derivative canvas according to the subscription range, and determining the relationship among the derivative canvas, the main canvas and the user; when a user logs in a system application, automatically generating corresponding derivative canvas micro-service according to user information of different users, so as to perform model maintenance through the canvas micro-service, and updating data of the derivative canvas to the main canvas; detecting whether the data of the derivative canvas updated by each user has conflict or not; if the conflict exists, dynamically combining conflict data according to a preset model dynamic combination method, and otherwise, further judging whether the updated model has errors; and if no error exists, updating the main canvas model, and issuing the updated main canvas model to the corresponding derivative canvas micro-service, otherwise, feeding error data back to the corresponding derivative canvas service for re-maintenance.
The cloud service-oriented power grid primary model collaborative modeling method provided by the embodiment of the invention has the advantages that a setting computing system development technical route under the cloud service is developed, the primary equipment model modeling of the setting computing system is ensured to realize multi-user collaboration and dynamic combination of basic models, the technical route requirement of the cloud service on the system is adapted, the problem of cloud service operation requirement is effectively met, and the cloud service-oriented power grid primary equipment model collaborative modeling can be realized, so that the rapid, accurate and efficient collaborative modeling of multiple users can be supported under the cloud service mode, and a good technical foundation is laid for the cloud formation of a relay protection setting computing professional system.
In addition, the cloud service-oriented power grid primary model collaborative modeling method according to the above embodiment of the present invention may further have the following additional technical features:
in an embodiment of the present invention, dynamically merging conflict data according to a preset model dynamic merging method includes: judging whether the equipment parameter attribute in the data of the derived canvas meets the requirement of the service specification; if the data model does not meet the requirements of the service specification, the data model which does not meet the service specification eliminates the data set of the dynamic merging method and records the data set as final feedback abnormal data; if the data information meets the requirements of the service specification, further judging whether the data information in each derivative canvas has data corresponding to the consistent equipment ID; if the data corresponding to the consistent equipment ID exist, accepting or rejecting the model data for the data information with conflict according to the priority, and preliminarily combining the model information; if the data corresponding to the consistent equipment ID does not exist, mapping the graph modification information of the derivative canvas to the main canvas, and judging whether the coordinate layout mapped to different derivative canvases has an overlapping area; if the overlapped area exists, judging whether the equipment ID connected with the equipment corresponding to the graph of the overlapped area is repeated, if so, performing graph selection on the data with repeated equipment connection relation in the overlapped area according to a priority rule to form new model data of the derivative canvas; and if the overlapping area does not exist or the overlapping area does not exist, irregular data records exist and are fed back to the system log and the corresponding derivative canvas microservices, the graphs of the derivative canvases are merged to the main canvas, the relative position of coordinates is calculated, a brand-new main canvas model is formed, and the dynamic model merging process is ended.
Further, in one embodiment of the present invention, the method further comprises: and after the main canvas model is successfully updated, quitting the collaborative modeling process, otherwise, sending an abnormal data prompt, ending the collaborative modeling process, and controlling the related derivative canvas microservice to suspend data modification.
Further, in an embodiment of the present invention, generating the derivative canvas according to the subscription scope includes: deriving and deriving a power grid model canvas, deriving a virtual canvas facing a user by taking station elements and equipment elements in the model canvas as a unit according to subscription requirements of the user, and forming a connection with a main canvas of a power grid equipment model through IDs of stations and equipment and connection relations of the equipment.
In order to achieve the above object, an embodiment of a second aspect of the present invention provides a cloud service-oriented power grid primary model collaborative modeling apparatus, including: the determining module is used for determining the attention range of the model selected by the user, subscribing the required model and determining the subscription range; the generating module is used for generating a derivative canvas according to the subscription range and determining the relationship among the derivative canvas, the main canvas and the user; the maintenance module is used for automatically generating corresponding derivative canvas microservices according to user information of different users when a user logs in the system application, so as to carry out model maintenance through the canvas microservices and update data of the derivative canvas to the main canvas; the detection module is used for detecting whether the data of the derivative canvas updated by each user conflict or not; the merging module is used for dynamically merging conflict data according to a preset model dynamic merging method when a conflict exists, and otherwise, further judging whether the updated model has errors; and the updating module is used for updating the main canvas model when no error exists, issuing the updated main canvas model to the corresponding derivative canvas microservice, and otherwise, feeding error data back to the corresponding derivative canvas service for re-maintenance.
The cloud service-oriented power grid primary model collaborative modeling device provided by the embodiment of the invention can develop a setting computing system development technical route under the cloud service, ensure that the primary equipment model modeling of the setting computing system realizes multi-user collaboration and dynamic merging of basic models, is suitable for the technical route requirement of the cloud service on the system, effectively meets the problem of cloud service operation requirement, and can support multi-user rapid, accurate and efficient collaborative modeling under the cloud service mode, thereby laying a good technical foundation for the cloud of a relay protection setting computing professional system.
In addition, the cloud service-oriented power grid primary model collaborative modeling apparatus according to the above embodiment of the present invention may further have the following additional technical features:
wherein, in an embodiment of the present invention, the merging module includes: the first judging unit is used for judging whether the equipment parameter attribute in the data of the derived canvas meets the requirement of the service specification; the processing unit is used for discarding the data set of the dynamic merging method from the data model which does not meet the service specification when the data model does not meet the service specification requirement, and recording the data set as final feedback abnormal data; the second judging unit is used for further judging whether the data information in each derivative canvas has data corresponding to the consistent equipment ID when the service specification requirement is met; the first merging unit is used for accepting or rejecting model data of the data information with conflict according to priority when the data corresponding to the consistent equipment ID exists, and preliminarily merging the model information; the mapping unit is used for mapping the graph modification information of the derivative canvas to the main canvas when the data corresponding to the consistent equipment ID does not exist, and judging whether the coordinate layout mapped to different derivative canvases has an overlapping area or not; a third judging unit, configured to judge whether the device IDs of the device connections corresponding to the images in the overlapping area are repeated when the overlapping area exists, where if so, the data that the device connection relationship in the overlapping area is repeated is subjected to image selection according to a priority rule, so as to form new model data of the derivative canvas; and the second merging unit is used for feeding back the abnormal data records to the system log and the corresponding derivative canvas microservices when the overlapping area does not exist or the overlapping area is not repeated, merging the graphs of the derivative canvases to the main canvas, calculating the relative position of coordinates, forming a brand-new main canvas model and ending the dynamic model merging process.
Further, in one embodiment of the present invention, the apparatus further comprises: and the reminding module is used for quitting the collaborative modeling process after the main canvas model is successfully updated, otherwise, sending an abnormal data reminding, finishing the collaborative modeling process and controlling related derivative canvas microservice to suspend data modification.
Further, in an embodiment of the present invention, the generating module is further configured to derive a power grid model canvas, derive a virtual canvas facing a user from plant station elements and device elements in the model canvas by using the user as a unit according to a subscription requirement of the user, and form a connection with a main canvas of the power grid device model through an ID of the plant station, the device, and a connection relationship between the main canvas and each device.
To achieve the above object, a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the cloud service oriented grid primary model collaborative modeling method of the above embodiments.
In order to achieve the above object, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the cloud service-oriented power grid primary model collaborative modeling method of the above embodiment.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a cloud service-oriented power grid primary model collaborative modeling method according to an embodiment of the present invention;
fig. 2 is a flowchart of a cloud service-oriented power grid primary model collaborative modeling method according to an embodiment of the present invention;
FIG. 3 is a diagram of a canvas hierarchy according to one embodiment of the present invention;
fig. 4 is a flowchart of a cloud service-oriented power grid primary model collaborative modeling method according to another embodiment of the present invention;
fig. 5 is a schematic block diagram of a cloud service-oriented power grid primary model collaborative modeling apparatus according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The cloud service-oriented power grid primary model collaborative modeling method, apparatus, electronic device and storage medium according to the embodiments of the present invention are described below with reference to the accompanying drawings. Aiming at the problems mentioned in the background technology center, and under the continuous promotion of the cloud work of the relay protection professional system, the primary model modeling method and the technology of the existing system cannot meet the requirements of technical development and business application, a cloud service mode-oriented power grid primary equipment model collaborative modeling method needs to be developed, the rapid, accurate and efficient collaborative modeling of multiple users can be supported in the cloud service mode, and a good technical basis is laid for the cloud work of the relay protection setting calculation professional system.
Specifically, with the transformation from the scheduling information system to the cloud service mode, the cloud service mode requires that various service application functions and data processing should meet the requirement of simultaneous subscription and maintenance of multiple users; the modeling function of a power grid primary equipment model of the conventional relay protection setting computing system cannot meet the requirement of cloud service on the system operation technology, and the conventional setting computing system graphical modeling method needs to be controlled by manual locking and unlocking in the aspects of model combination and collaborative maintenance, fails to provide a mechanism for multi-user collaborative modeling and automatic model combination, and is difficult to realize multi-user collaborative application in business; the high efficiency of the overall operation of the system is limited to a certain extent, and a mechanism of one canvas and manual locking (unlocking) is technically difficult to meet a cloud service mode and does not meet the requirement of the cloud service on the overall operation mode of multiple users.
Based on the basis, the embodiment of the invention provides a cloud service-oriented power grid primary model collaborative modeling method and device.
The cloud service-oriented power grid primary model collaborative modeling method and device provided by the embodiment of the invention are described below with reference to the accompanying drawings, and the cloud service-oriented power grid primary model collaborative modeling method provided by the embodiment of the invention will be described with reference to the accompanying drawings.
Fig. 1 is a flowchart of a cloud service-oriented power grid primary model collaborative modeling method according to an embodiment of the present invention.
As shown in fig. 1, the cloud service-oriented power grid primary model collaborative modeling method includes the following steps:
in step S101, the attention range of the user-selected model is determined, the desired model is subscribed, and the subscription range is determined.
It will be appreciated that, as shown in FIG. 2, first at step S1, the user subscribes to a model scope: the user selects the attention range of the model as required, subscribes to the required model, and after determining the subscription range, the process proceeds to step S1.
In step S102, a derivative canvas is generated according to the subscription scope, and a relationship between the derivative canvas, the main canvas, and the user is determined.
As shown in fig. 2, next step S2, a derivative canvas is generated: generating a derivative canvas according to the subscription range, and storing the relation among the derivative canvas, the main canvas and the user in a library for recording; and (4) performing the operation of the step 3 according to the application requirements of the system.
In an embodiment of the present invention, generating a derivative canvas according to a subscription scope includes: deriving and deriving a power grid model canvas, deriving a virtual canvas facing a user by taking station elements and equipment elements in the model canvas as a unit according to subscription requirements of the user, and forming a connection with a main canvas of a power grid equipment model through IDs of stations and equipment and connection relations of the equipment.
Specifically, as shown in fig. 3, in order to meet the requirements of a tuning calculation system in a cloud service mode for multiple subscriptions and model maintenance of a user, a graph canvas of a power grid equipment model is subjected to hierarchical processing. Deriving and deriving a power grid model canvas, and deriving a virtual canvas facing a specific user, namely a derived canvas, from station elements and equipment elements in the model canvas by taking the user as a unit according to the subscription requirement of the user; the device and the main canvas of the power grid device model form a connection through the ID of the plant station and the device and the connection relation of each device.
And forming the derivative canvas by taking the graphic layout, the station attribute, the equipment attribute and the equipment connection relation of the main canvas power grid equipment model as data bases and identifying the subscription range of a specific user to form the model range of the derivative canvas. When a user logs in the system, the user defaults to enter a derived canvas interface, and the overall display form and functions of the user are consistent with those of the main canvas except the range of equipment.
For different schedules, especially schedules with an upper-lower level relation, all derived canvases are subjected to hierarchical processing through the scheduling levels of users to which the derived canvases belong, so that the derived canvases with different levels are formed (the level of the derived canvas of a user 3 is higher than that of the derived canvas of the user 1 and that of the derived canvas of the user 2 shown in the figure), and when data are stored and recorded, hierarchical identification is recorded. The hierarchical processing mechanism of the canvas lays a technical foundation for the overall implementation process of the collaborative modeling and the combination method of the models, and is a basic guarantee for the implementation of the collaborative modeling.
In step S103, when the user logs in the system application, a corresponding derivative canvas microservice is automatically generated according to user information of different users, so as to perform model maintenance through the canvas microservice, and update data of the derivative canvas to the main canvas.
As shown in fig. 2, further to step S3, multiple users simultaneously maintain and update the model: when a user logs in the system application, the cloud service automatically generates derivative canvas micro-service according to different user information; each user maintains the model through the derivative canvas microserver, updates the derivative canvas data to the main canvas, and enters the step S4 when the data is updated.
In step S104, it is detected whether there is a conflict in the data of the derivative canvas updated by each user.
As shown in fig. 2, further step S4: whether there is a data collision: and the system judges whether the updated data of each user has conflict or not according to the incidence relation and the unique identification of each device.
In step S105, if there is a conflict, dynamically merging conflict data according to a preset model dynamic merging method, otherwise, further determining whether the updated model has an error.
As shown in fig. 2, further to step S5, the conflict data are dynamically merged: performing dynamic merging of the conflict data according to a model dynamic merging method; in addition, step S6, the model correctness checks whether it passes: and judging whether the updated model has errors or not, if not, entering the step S7, if so, feeding error data back to the derivative canvas service corresponding to the data, namely, returning to the step S3, and maintaining the data again.
In step S106, if there is no error, the main canvas model is updated, and the updated main canvas model is issued to the corresponding derivative canvas microservice, otherwise, the error data is fed back to the corresponding derivative canvas service for re-maintenance.
As shown in FIG. 2, in a further step S7, the master canvas model is dynamically updated: and the main canvas model updates the main body model according to the verified model data and issues the updated model to the corresponding derivative canvas microservice, so that a user can see the updated data information in time.
Further, in one embodiment of the present invention, the method further comprises: and after the main canvas model is successfully updated, quitting the collaborative modeling process, otherwise, sending an abnormal data prompt, ending the collaborative modeling process, and controlling the related derivative canvas microservice to suspend data modification.
It will be appreciated that step 8 is entered after the update is complete, as shown in figure 2. Finally, step S8, end exit: and after the model is successfully updated, the whole process is carried out, the system administrator is informed of abnormal data processing when the model is unsuccessfully updated, the whole process is ended, and relevant derivative canvas microservice is informed of suspending data modification.
In summary, as shown in fig. 2, the embodiment of the present invention establishes a canvas hierarchical processing mechanism for a primary device model, defines an application logic of multi-user collaborative modeling and a dynamic model merging method of multi-user collaborative modeling, provides a reliable development scheme for a multi-user to model data multi-subscription application mode of a cloud service-based tuning computing system, provides a set of multi-user collaborative modeling methods for the cloud service mode tuning computing system, defines a hierarchical design mechanism of a graphical canvas of a primary device, provides technical guidance for a graphical processing method of collaborative modeling based on a patent, provides a dynamic merging method of a multi-user collaborative maintenance model and a basic flow of collaborative modeling, and provides a basic implementation flow for a primary device model system modeling program design and development of a power grid based on the patent.
In an embodiment of the present invention, dynamically merging conflict data according to a preset model dynamic merging method includes: judging whether the equipment parameter attribute in the data of the derived canvas meets the requirement of the service specification; if the data model does not meet the requirements of the service specification, the data model which does not meet the service specification eliminates the data set of the dynamic merging method and records the data set as final feedback abnormal data; if the data information meets the requirements of the service specification, further judging whether the data information in each derivative canvas has data corresponding to the consistent equipment ID; if the data corresponding to the consistent equipment ID exist, accepting or rejecting the model data for the data information with conflict according to the priority, and preliminarily combining the model information; if the data corresponding to the consistent equipment ID does not exist, mapping the graph modification information of the derivative canvas to the main canvas, and judging whether the coordinate layout mapped to different derivative canvases has an overlapping area; if the overlapped area exists, judging whether the equipment ID connected with the equipment corresponding to the graph of the overlapped area is repeated, if so, performing graph selection on the data with repeated equipment connection relation in the overlapped area according to a priority rule to form new model data of the derivative canvas; and if the overlapping area does not exist or the overlapping area does not exist, irregular data records exist and are fed back to the system log and the corresponding derivative canvas microservices, the graphs of the derivative canvases are merged to the main canvas, the relative position of coordinates is calculated, a brand-new main canvas model is formed, and the dynamic model merging process is ended.
Specifically, all modified data of the derivative canvas are fed back to the main canvas through the IDs of the plant station and the equipment; when the derivative canvas model is fed back to the main canvas, the data priority of each derivative canvas depends on the scheduling level of a corresponding user, and the main canvas model is updated on the basis of the priority of the high-priority person; and the overlapping degree of the graphs is identified by combining the fuzzy matching technology of the graphs and the accurate comparison of the coordinates of the graphs, the layout is automatically adjusted, and all conflict judgment and processing of the model are automatically completed through the system without manual intervention. As shown in fig. 4, the model dynamic merging method flow of the embodiment of the present invention includes:
step S11: acquiring update data of the derivative canvas model: and acquiring data to be updated of the derived canvas model from the data processing process of the collaborative modeling basic flow, and entering the step S12.
Step S12: whether each data attribute meets the service specification: and judging whether the equipment parameter attributes in the model data meet the service specification requirements, if so, entering step S14, and if not, entering step S13.
Step S13: and discarding the non-standard data source: and discarding the data set of the dynamic merging method from the data model which does not meet the service specification, recording, and providing recording information for finally feeding back the abnormal data.
Step S14: whether there is a data collision with the device ID: and judging whether the data corresponding to the consistent equipment ID exists in the data information of each derivative canvas, if so, entering step S15, and if not, entering step 6.
Step S15: retention of higher level derivative canvas data: accepting or rejecting model data for the data information with conflict according to a rule of 'high-priority', and preliminarily combining the model information; after the data processing is completed, the flow proceeds to step S16.
Step S16: whether there is an overlapping pattern in the co-ordinate position: and mapping the graphic modification information of the derivative canvas to the main canvas, and judging whether an overlapping area exists in the coordinate layout mapped by different derivative canvases, if so, entering the step S17, and if not, entering the step S19.
Step S17: comparing whether the graph connection relation is repeated: and judging whether the connected device ID of the device corresponding to the overlapping area graph is repeated, if so, repeatedly entering the step S18, and if not, repeatedly entering the step S19.
Step S18: preserving high-level derivative canvas graphics: and carrying out graph selection and selection on the data with repeated equipment connection relation in the overlapping area according to a rule of 'high-priority', and forming the model data of a new derivative canvas.
Step S19: feeding back abnormal data to update the model: feeding back the data records with irregularities in the steps to a system log and a corresponding derivative canvas microservice; and merging the graphs of the derived canvas to the main canvas, calculating the relative position of coordinates, avoiding the graph overlapping, forming a brand new main canvas model, and ending the dynamic model merging process.
In summary, the embodiment of the invention defines a canvas hierarchical processing mechanism, a multi-user collaborative modeling implementation process and a dynamic merging implementation method of data after model maintenance, which are relied on by the primary equipment model viewing and editing, defines a design direction and a main process of program implementation for the design and development of the multi-user collaborative modeling function of the power grid primary equipment model, and establishes a basic technical route of power grid model modeling of 'user multi-subscription, platform dynamic merging' facing to a cloud service mode; the defect that the modeling technology of the existing setting system cannot meet the application requirement of a cloud service mode is overcome, and a good technical basis is laid for the cloud-based transformation design and development of the setting computing system.
Specifically, (1) the canvas grading processing mechanism of the embodiment of the invention provides a good processing method for multi-user collaborative maintenance and application service development of a power grid primary equipment model, aiming at the problem that multi-user collaborative application automatic model updating is difficult to realize in the current situation that an original power system models one picture at a time; based on the mechanism, a multi-center cooperative application method can be evolved, and a new idea is provided for cooperative application in a cloud service mode.
(2) The canvas grading processing mechanism of the embodiment of the invention solves the problem that a user multi-subscription application data model has no good solution under a cloud service mode, and fills in the gap of a cloud service-oriented power grid primary equipment model user multi-subscription data model service development technology.
(3) The basic flow of the multi-person collaborative modeling of the embodiment of the invention defines how to develop the multi-person collaborative modeling service design based on the canvas hierarchical processing mechanism; and the model dynamic combination method of the patent is combined to form a complete cloud service-oriented power grid primary equipment model collaborative modeling service, the whole data is processed abnormally, combined and updated without manual participation, and the existing collaborative method of manually limiting a model editing mechanism in the industry is abandoned, so that the collaborative modeling process is smoother, and the application requirements of actual users are better met.
According to the cloud service-oriented power grid primary model collaborative modeling method, a setting computing system development technical route under the cloud service is exploited, the primary equipment model modeling of the setting computing system is ensured to realize multi-user collaboration and dynamic combination of basic models, the technical route requirement of the cloud service on the system is met, the problem of cloud service operation requirement is effectively met, and the cloud service-oriented power grid primary equipment model collaborative modeling can be realized, so that the rapid, accurate and efficient collaborative modeling of multiple users can be supported under the cloud service mode, and a good technical foundation is laid for the cloud formation of a relay protection setting computing professional system.
Next, a cloud service-oriented power grid primary model collaborative modeling apparatus proposed according to an embodiment of the present invention is described with reference to the drawings.
Fig. 5 is a schematic block diagram of a cloud service-oriented power grid primary model collaborative modeling apparatus according to an embodiment of the present invention.
As shown in fig. 5, the cloud-service-oriented power grid primary model collaborative modeling apparatus 10 includes: a determination module 100, a generation module 200, a maintenance module 300, a detection module 400, a merging module 500, and an update module 600.
The determining module 100 is configured to determine an attention range of a model selected by a user, subscribe to a desired model, and determine a subscription range.
And the generating module 200 is configured to generate a derivative canvas according to the subscription range, and determine a relationship between the derivative canvas, the main canvas, and the user.
The maintenance module 300 is configured to, when a user logs in the system application, automatically generate a corresponding derivative canvas microservice according to user information of different users, perform model maintenance through the canvas microservice, and update data of the derivative canvas to the main canvas.
The detection module 400 is configured to detect whether there is a conflict in the data of the derivative canvas updated by each user.
And a merging module 500, configured to merge the conflict data dynamically according to a preset model dynamic merging method when a conflict exists, and otherwise, further determine whether the updated model has an error.
And the updating module 600 is configured to update the main canvas model when there is no error, and publish the updated main canvas model to the corresponding derivative canvas microservice, otherwise, feed back error data to the corresponding derivative canvas service for re-maintenance.
In one embodiment of the present invention, the merging module 500 includes: the device comprises a first judgment unit, a processing unit, a second judgment unit, a first merging unit, a mapping unit, a third judgment unit and a second merging unit.
And the first judging unit is used for judging whether the equipment parameter attribute in the data of the derived canvas meets the requirement of the service specification.
And the processing unit is used for discarding the data set of the dynamic merging method from the data model which does not meet the service specification when the data model does not meet the service specification requirement, and recording the data set as final feedback abnormal data.
And the second judging unit is used for further judging whether the data information in each derivative canvas has data corresponding to the consistent equipment ID when the service specification requirement is met.
And the first merging unit is used for accepting or rejecting the model data of the data information with conflict according to the priority when the data corresponding to the consistent equipment ID exists, and preliminarily merging the model information.
And the mapping unit is used for mapping the graphic modification information of the derivative canvas to the main canvas when the data corresponding to the consistent equipment ID does not exist, and judging whether the coordinate layout mapped to different derivative canvases has an overlapping area.
And the third judging unit is used for judging whether the equipment IDs of the equipment connections corresponding to the images of the overlapping areas are repeated or not when the overlapping areas exist, if so, performing image rejection on the data of the equipment connections of the overlapping areas which are repeated according to the rules of priority, and forming new model data of the derivative canvas.
And the second merging unit is used for feeding back the data records with irregularities to the system log and the corresponding derivative canvas microservices when no overlapping area exists or the data records are not repeated, merging the graphs of the derivative canvases to the main canvases, calculating the relative position of coordinates, forming a brand-new main canvas model and ending the dynamic model merging process.
Further, in one embodiment of the present invention, the apparatus 10 of the embodiment of the present invention further comprises: and a reminding module.
And the reminding module is used for quitting the collaborative modeling process after the main canvas model is successfully updated, otherwise, sending an abnormal data reminding, finishing the collaborative modeling process and controlling related derivative canvas microservice to suspend data modification.
Further, in an embodiment of the present invention, the generation module 200 is further configured to derive a power grid model canvas, derive a virtual canvas facing a user from plant station elements and device elements in the model canvas by using the user as a unit according to subscription requirements of the user, and form a connection with a main canvas of the power grid device model through an ID of the plant station, the device, and a connection relationship between the main canvas and each device.
It should be noted that the explanation of the foregoing embodiment of the cloud service-oriented power grid primary model collaborative modeling method is also applicable to the cloud service-oriented power grid primary model collaborative modeling apparatus of this embodiment, and details are not repeated here.
According to the cloud service-oriented power grid primary model collaborative modeling device provided by the embodiment of the invention, a setting computing system development technical route under a cloud service is exploited, the primary equipment model modeling of the setting computing system is ensured to realize multi-user collaboration and dynamic combination of basic models, the requirement of the cloud service on the technical route of the system is adapted, the problem of cloud service operation requirement is effectively met, and the cloud service-oriented power grid primary equipment model collaborative modeling can be realized, so that the rapid, accurate and efficient collaborative modeling of multiple users can be supported under the cloud service mode, and a good technical foundation is laid for the cloud formation of a relay protection setting computing professional system.
In order to implement the above embodiments, the present invention further provides an electronic device, including: at least one processor and a memory. Wherein the memory is in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the cloud service-oriented power grid primary model collaborative modeling method of the embodiment
In order to implement the foregoing embodiment, the present invention further provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the cloud service-oriented power grid primary model collaborative modeling method of the foregoing embodiment.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A cloud service-oriented power grid primary model collaborative modeling method is characterized by comprising the following steps:
determining the attention range of a user selected model, subscribing the required model and determining the subscription range;
generating a derivative canvas according to the subscription range, and determining the relationship among the derivative canvas, the main canvas and the user;
when a user logs in a system application, automatically generating corresponding derivative canvas micro-service according to user information of different users, so as to perform model maintenance through the canvas micro-service, and updating data of the derivative canvas to the main canvas;
detecting whether the data of the derivative canvas updated by each user has conflict or not;
if the conflict exists, dynamically combining conflict data according to a preset model dynamic combination method, and otherwise, further judging whether the updated model has errors; and
and if no error exists, updating the main canvas model, and issuing the updated main canvas model to the corresponding derivative canvas micro-service, otherwise, feeding error data back to the corresponding derivative canvas service for re-maintenance.
2. The method according to claim 1, wherein the dynamically merging conflict data according to a preset model comprises:
judging whether the equipment parameter attribute in the data of the derived canvas meets the requirement of service specification;
if the data model does not meet the requirement of the service specification, the data model which does not meet the service specification eliminates the data set of the dynamic merging method and records the data set as final feedback abnormal data;
if the data information meets the requirements of the service specification, further judging whether the data information in each derivative canvas has data corresponding to the consistent equipment ID;
if the data corresponding to the consistent equipment ID exists, accepting or rejecting the model data for the data information with conflict according to the priority, and preliminarily combining the model information;
if the data corresponding to the consistent equipment ID does not exist, mapping the graph modification information of the derivative canvas to the main canvas, and judging whether the coordinate layout mapped to different derivative canvases has an overlapping area;
if the overlapped area exists, judging whether the equipment IDs of the equipment connections corresponding to the images of the overlapped area are repeated, if so, performing image accepting or rejecting on the data of the equipment connections of the overlapped area, which are repeated, according to a priority rule to form new model data of the derivative canvas;
and if the overlapped area does not exist or the overlapped area does not exist, irregular data records are fed back to the system log and the corresponding derivative canvas microserver, the graphs of the derivative canvases are merged to the main canvas, the relative position of coordinates is calculated, a brand new main canvas model is formed, and the dynamic model merging process is ended.
3. The method of claim 1, further comprising:
and after the main canvas model is successfully updated, quitting the collaborative modeling process, otherwise, sending an abnormal data prompt, ending the collaborative modeling process, and controlling related derivative canvas microservice to suspend data modification.
4. The method of claim 1, wherein generating the derivative canvas according to the subscription scope comprises:
deriving and deriving a power grid model canvas, deriving a virtual canvas facing a user by taking station elements and equipment elements in the model canvas as a unit according to subscription requirements of the user, and forming a connection with a main canvas of a power grid equipment model through IDs of stations and equipment and connection relations of the equipment.
5. A cloud service-oriented power grid primary model collaborative modeling device is characterized by comprising:
the determining module is used for determining the attention range of the model selected by the user, subscribing the required model and determining the subscription range;
the generating module is used for generating a derivative canvas according to the subscription range and determining the relationship among the derivative canvas, the main canvas and the user;
the maintenance module is used for automatically generating corresponding derivative canvas microservices according to user information of different users when a user logs in the system application, so as to carry out model maintenance through the canvas microservices and update data of the derivative canvas to the main canvas;
the detection module is used for detecting whether the data of the derivative canvas updated by each user conflict or not;
the merging module is used for dynamically merging conflict data according to a preset model dynamic merging method when a conflict exists, and otherwise, further judging whether the updated model has errors; and
and the updating module is used for updating the main canvas model when no error exists, issuing the updated main canvas model to the corresponding derivative canvas microservice, and otherwise, feeding error data back to the corresponding derivative canvas service for re-maintenance.
6. The method of claim 5, wherein the merging module comprises:
the first judging unit is used for judging whether the equipment parameter attribute in the data of the derived canvas meets the requirement of the service specification;
the processing unit is used for discarding the data set of the dynamic merging method from the data model which does not meet the service specification when the data model does not meet the service specification requirement, and recording the data set as final feedback abnormal data;
the second judging unit is used for further judging whether the data information in each derivative canvas has data corresponding to the consistent equipment ID when the service specification requirement is met;
the first merging unit is used for accepting or rejecting model data of the data information with conflict according to priority when the data corresponding to the consistent equipment ID exists, and preliminarily merging the model information;
the mapping unit is used for mapping the graph modification information of the derivative canvas to the main canvas when the data corresponding to the consistent equipment ID does not exist, and judging whether the coordinate layout mapped to different derivative canvases has an overlapping area or not;
a third judging unit, configured to judge whether the device IDs of the device connections corresponding to the images in the overlapping area are repeated when the overlapping area exists, where if so, the data that the device connection relationship in the overlapping area is repeated is subjected to image selection according to a priority rule, so as to form new model data of the derivative canvas;
and the second merging unit is used for feeding back the abnormal data records to the system log and the corresponding derivative canvas microservices when the overlapping area does not exist or the overlapping area is not repeated, merging the graphs of the derivative canvases to the main canvas, calculating the relative position of coordinates, forming a brand-new main canvas model and ending the dynamic model merging process.
7. The method of claim 6, wherein the apparatus further comprises:
and the reminding module is used for quitting the collaborative modeling process after the main canvas model is successfully updated, otherwise, sending an abnormal data reminding, finishing the collaborative modeling process and controlling related derivative canvas microservice to suspend data modification.
8. The method of claim 5, wherein the generating module is further configured to derive a power grid model canvas, derive a virtual canvas facing the user from plant station elements and device elements in the model canvas by using the user as a unit according to subscription requirements of the user, and form a connection with a main canvas of the power grid device model through the plant station, the ID of the device and a connection relationship between the devices.
9. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the cloud service oriented grid primary model collaborative modeling method of any of the above claims 1-5.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the cloud service oriented power grid primary model collaborative modeling method of any one of claims 1-5.
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