Power grid equipment data display method and device, equipment and storage medium

Abstract

The invention provides a power grid equipment data display method and device, equipment and a storage medium, and the method comprises the steps: obtaining equipment data and geographic data, the equipment data comprises an equipment type, an equipment attribute and an equipment coordinate, and the geographic data comprises an element type and an element attribute; mapping the equipment type into an element type through a preset semantic mapping dictionary, and establishing a binding relationship between the equipment attribute and the element attribute to obtain association relationship data; performing precision grading on the equipment coordinates to obtain a plurality of precision grades, and calibrating the equipment coordinates according to different precision grades; fusing the association relationship data with the calibrated equipment coordinates to obtain fused data; determining a display detail level according to the resolution and the visual field range of the display terminal; and calling fusion data corresponding to the display detail level to carry out visual rendering, and displaying the fusion data on a display terminal. Uniform fusion data is generated, the intelligence and efficiency of visual display are improved, and the rendering fluency is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power grid data processing, and in particular relates to a power grid equipment data display method, device, equipment and storage medium. BACKGROUND

[0002] With the rapid development of smart grid technology, the scale of power systems is continuously expanding, and the topology structure is increasingly complex. Accurate and intuitive display of power grid information is crucial for the operation, maintenance and management of power systems. In this context, the visualization technology of power grid has evolved from traditional static drawings to electronic map mode based on geographic information system (GIS), but its limitations are increasingly highlighted in the face of modern power grid's massive dynamic data.

[0003] In the prior art, the common power grid visualization system presents obvious stage characteristics in data fusion and visualization rendering level:

[0004] In the data fusion level, a shallow fusion mode is mainly adopted. This mode directly superimposes the latitude and longitude coordinates in the CIM model data obtained from the power system (such as EMS) on the GIS map to realize the basic device mapping. This scheme does not process the internal differences of the two types of data, for example: the CIM model takes the logical relationship of power equipment as the core, while the GIS takes the geographical spatial position as the core, and there is data semantic heterogeneity between the two; at the same time, there is a mismatch between the precision of the geographical position data of part of the devices in the CIM (such as street level) and the meter-level precision requirement of the GIS map; in addition, there is a real-time difference between the second / minute-level real-time running data of the CIM and the month / quarter-level static geographical information of the GIS. SUMMARY

[0005] Therefore, the present application provides a power grid equipment data display method, device, equipment and storage medium to solve the problems existing in the prior art.

[0006] The first aspect of the present application provides a power grid equipment data display method, comprising:

[0007] Obtaining device data and geographical data, the device data including device type, device attribute, device coordinate, the geographical data including feature type, feature attribute;

[0008] Mapping the device type to the feature type through a preset semantic mapping dictionary, and establishing a binding relationship between the device attribute and the feature attribute to obtain association relationship data;

[0009] Classifying the device coordinate into multiple precision levels, and calibrating the device coordinate according to different precision levels;

[0010] Based on the association relationship data and the calibrated device coordinates, fusion data is obtained.

[0011] The display terminal resolution and field of view are used to determine the loaded display detail level.

[0012] The fusion data corresponding to the display detail level is called to perform visual rendering and display on the display terminal.

[0013] The second aspect of the present application provides a power grid device data display device, comprising:

[0014] A data acquisition and processing module is configured to acquire device data and geographic data, wherein the device data includes device type, device attribute and device coordinates, and the geographic data includes feature type and feature attribute.

[0015] A data fusion module is configured to map the device type to the feature type by using a preset semantic mapping dictionary, establish a binding relationship between the device attribute and the feature attribute, obtain association relationship data, perform accuracy grading on the device coordinates to obtain multiple accuracy levels, calibrate the device coordinates according to different accuracy levels, and fuse the association relationship data and the calibrated device coordinates to obtain fusion data.

[0016] A visualization module is configured to determine a display detail level according to the resolution and field of view of a display terminal, call the fusion data corresponding to the display detail level to perform visual rendering and display on the display terminal.

[0017] The third aspect of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize the power grid device data display method of the first aspect.

[0018] The fourth aspect of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the power grid device data display method of the first aspect.

[0019] The fifth aspect of the present application provides a computer program product, when the computer program product is executed on a computer, the computer program product makes the computer execute the power grid device data display method of the first aspect.

[0020] Compared with the background art, the present application has the following beneficial effects:

[0021] The semantic mapping dictionary is used to establish the semantic level association between the device and the geographic element, and the device coordinates are calibrated based on the accuracy classification difference, so that the semantic heterogeneity and coordinate accuracy mismatch of the multi-source data are effectively solved, the high-quality unified fusion data is generated, and the intelligence and efficiency of the visual display are improved. Secondly, by determining the display detail level according to the display terminal resolution and the user field of view range, the intelligent distribution of the rendering load is realized, which makes the system can ensure the clear presentation of the fusion data while significantly improving the rendering fluency, and adapts to various devices from large display screens to mobile terminals, greatly improving the efficiency and intuitiveness of power grid operation monitoring and management. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a schematic diagram of a power grid device data display method provided by an embodiment of the present application;

[0024] Figure 2 is a structural schematic diagram of a power grid device data display device provided by an embodiment of the present application;

[0025] Figure 3 is a display effect diagram of LOD model data at different LOD levels provided by an embodiment of the present application;

[0026] Figure 4 is a schematic diagram of a power grid device data display device provided by an embodiment of the present application;

[0027] Figure 5 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] In the following description, specific details are set forth such as particular system architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments without these specific details. In other instances, well-known systems, devices, circuits, and methods have been omitted in order to not to obscure the description of the present application with unnecessary detail.

[0029] The technical solutions of the present application will be described below through specific embodiments.

[0030] Reference Figure 1, shows a schematic diagram of a power grid device data display method provided by an embodiment of the application, and can specifically include the following steps:

[0031] S101, acquire device data and geographic data, the device data including device type, device attribute, and device coordinate, and the geographic data including element type and element attribute.

[0032] This step is the input end of the system, which actively acquires information from two heterogeneous data sources. One is device data, which is derived from the business database (such as SCADA and EMS) of the power system, and includes device type (such as transformer and circuit breaker), device attribute (such as rated voltage and device ID), and device coordinate (usually longitude and latitude), which together define the function and logical position of the device in the power network. The other is geographic data, which is derived from the geographic information system (GIS), which describes the physical world, and the element type (such as point, line, and surface) and element attribute (such as geographic code and address) define the graphical representation and geographic information of spatial entities.

[0033] S102, map the device type to the element type through a preset semantic mapping dictionary, and establish a binding relationship between the device attribute and the element attribute to obtain association relationship data.

[0034] The preset semantic mapping dictionary is a pre-defined knowledge base, for example, it is specified that the Substation (substation) in CIM is mapped to the surface element in GIS. The binding relationship is a further association relationship compared with the mapping relationship, which can bind the device ID, voltage level, and other attributes of the device to the extended field of the GIS element. Through the above mapping and binding, the association relationship data is obtained, and the deep association between the power device and the geographic element is realized.

[0035] In an optional embodiment, the power grid device data display method further includes:

[0036] A configuration interface is provided, which is used to receive a configuration file submitted by a user to modify or expand the semantic mapping dictionary; wherein through the configuration file, a new mapping rule can be added for a device attribute that is not defined in the semantic mapping dictionary.

[0037] The user usually modifies the system configuration by uploading an XML, JSON, or special format configuration file, rather than directly replacing the entire dictionary file.

[0038] The types and attributes of devices in power systems can change constantly due to regions, manufacturers or introduction of new technologies. The preset dictionary cannot cover all cases completely. The configuration interface provides an official and system-level solution. Without modifying the system core code or waiting for version upgrade, the operation and maintenance personnel or users can adapt the system to new device data through configuration, greatly reducing the maintenance cost and prolonging the effective life cycle of the system.

[0039] S103, precision grading is performed on the device coordinates to obtain multiple precision levels, and the device coordinates are calibrated according to different precision levels.

[0040] The precision of the device coordinates may be caused by the difference of the device collecting the device coordinates, the error of the coordinate system and conversion, etc. For example, the precision of a civilian mobile phone or a common GPS device is usually 5-10 meters, and is easily affected by weather and building shielding. However, sub-meter or centimeter-level precision requires the use of more expensive differential GPS or RTK technology, which is costly in large-scale network-wide surveys. Many device coordinate data of power grids were entered many years ago, and the technical conditions at that time could not provide high-precision coordinates as today, but these data are still used in the system.

[0041] First, the coordinate quality is evaluated and classified (such as high, medium and low), which is an intelligent resource allocation strategy to avoid uniform processing.

[0042] In an optional embodiment, the precision grading of the device coordinates obtains multiple precision levels, including: determining the precision level according to the number of digits after the decimal point of the coordinate value of the device coordinates, and the precision level is proportional to the number of digits after the decimal point.

[0043] The number of digits after the decimal point directly reflects the resolution or degree of detail that can be achieved when measuring or recording the coordinate.

[0044] Example coordinate 1: (121.47, 31.23), which locates the device in a grid of about 1.1 km x 1.1 km (estimated near the equator). This usually corresponds to a rough location at the street level or regional level, which may come from early planning drawings or manual rough recording.

[0045] Example coordinate 2: (121.4735, 31.2341), which locates the device in a grid of about 11 m x 11 m. This usually comes from civilian GPS devices or high-quality system data, and can locate to the approximate range of a building or a station.

[0046] Through the number of digits after the decimal point, the quality of the data source and the potential position error range can be quickly and effectively evaluated.

[0047] Different strategies are adopted for different levels. The precision levels include medium precision level and low precision level. The device coordinates are calibrated according to different precision levels, specifically including: for the device coordinates of the medium precision level, calling the address matching service of the GIS system for calibration; for the device coordinates of the low precision level, using the triangulation method or the interpolation algorithm based on the coordinates of the starting and ending points of the line to complete the coordinates.

[0048] The specific processing strategies are:

[0049] High precision level: the original coordinates are kept, and no calibration or only slight optimization is needed.

[0050] Medium precision level: the address matching API is called to try to correct the coordinates to a more accurate house number or feature location.

[0051] Low precision level: spatial geometric algorithms (such as triangulation, line interpolation) must be used to calculate the most likely accurate location using the topological relationship with the surrounding high-precision devices.

[0052] Through this step, the logical coordinates with precision errors can be converted into spatial coordinates that can be used by high-precision GIS maps, greatly improving the reliability of the visualization results.

[0053] S104, based on the association relationship data and the calibrated device coordinates, fusion is obtained.

[0054] This step integrates the processed multi-source data into a single, high-quality data entity. Data fusion is not simply splicing, but integrating the semantic association established in S102 with the high-precision coordinates obtained in S103 to generate a new fusion data record. Each record contains complete device business attributes, accurate geographic spatial information, and the binding relationship between them.

[0055] S105, according to the resolution and field of view of the display terminal, determine the display detail level to be loaded.

[0056] The purpose of this step is to intelligently determine the level of detail of the rendered content according to the display context, to balance the effect and performance, and to consider the resolution (physical hardware capability) and field of view (user's focus area). For example, when looking at the global on a high-definition large screen, a simplified model should be used; when looking at local details on a small screen of a mobile phone, a detailed model should be loaded. Determine a suitable display detail level, that is, whether to call a detailed model, a medium model, or a simplified model at this moment. The rendering resources are allocated on demand, ensuring that the most efficient visual experience can be obtained on any display device and at any zoom level.

[0057] S106, call the fusion data corresponding to the display detail level for visualization rendering and display on the display terminal.

[0058] According to the detail level decided in S105, model data of corresponding accuracy is called from the fusion data for rendering. The rendered power grid graphics (devices, lines, etc.) are combined with the GIS base map to intuitively show the user on the display terminal. The conversion from the original data to the user-friendly interface is realized, and a clear, smooth, and adaptive power grid visualization screen is provided for the user.

[0059] The embodiment of the present application establishes the semantic-level association between the device and the geographic element through the semantic mapping dictionary, and differentially calibrates the device coordinates based on the accuracy classification, effectively solving the semantic heterogeneity and coordinate accuracy mismatch of multi-source data, generating high-quality unified fusion data, and improving the intelligence and efficiency of the visualization display. Secondly, by determining the display detail level according to the display terminal resolution and the user's field of view, intelligent allocation of rendering load is realized, which enables the system to clearly present the key information in the fusion data while significantly improving the rendering fluency, and adapts to various devices from large display screens to mobile terminals, greatly improving the efficiency and intuitiveness of power grid operation monitoring and management.

[0060] In an optional embodiment, it further comprises:

[0061] The device data is divided into static attribute data and real-time running data; the geographic data, the static attribute data in the device data, and the association relationship data are stored in the first cache layer and are periodically updated; the real-time running data in the device data is stored in the second cache layer and is incrementally updated in real time; wherein the update frequency of the first cache layer is less than the update frequency of the second cache layer.

[0062] Static attribute data: refers to the inherent properties of the device that do not change with the real-time running state of the power grid. For example, device ID, device name, device model, rated voltage, calibrated coordinates, and the belonging station. The static attribute data has a very low change frequency, and is usually only updated when the device is newly added, replaced, or modified.

[0063] Real-time running data: refers to the measured values that describe the current dynamic running state of the power grid. For example, real-time active / reactive power, current, voltage, switch state (on / off), load rate, and fault signal. The real-time running data has a high change frequency and is continuously refreshed in seconds or minutes.

[0064] The storage content of the first cache layer (static / basic data layer): geographic data + static attribute data + association relationship data. It is periodically updated (for example, once a day, once a week, or once a month). Because its content is very stable and does not need to be frequently refreshed, this strategy can greatly reduce the load of the source database and network transmission.

[0065] The second cache layer (dynamic / real-time data layer) stores content: real-time running data. Its update strategy is real-time incremental update. It guarantees low latency and ensures the timeliness of data display.

[0066] Low-frequency update is adopted for the static layer, and incremental update is adopted for the dynamic layer, which greatly reduces unnecessary data transmission and saves network bandwidth. The second cache layer focuses on processing real-time data that changes rapidly, making the refresh speed of key information such as power flow and load faster and the user experience smoother.

[0067] Incremental update is the key to performance optimization. Instead of transmitting and updating the entire data set every time the data changes, the system only synchronizes a small part of the data that has changed. This maximizes the saving of computing resources and network bandwidth.

[0068] By separating stable static data from changing dynamic data, the system avoids repeatedly loading and processing a large set of unchanged base data every time the real-time data is refreshed, significantly reducing system I / O and computing overhead.

[0069] In an optional embodiment, the power grid device data display method further comprises:

[0070] For each piece of fused data, record the generation time of the data source used to generate the fused data and the processing completion time of the fusion processing; when the data source interruption is monitored, automatically fill in the latest historical fused data, and add a warning mark to the filled data.

[0071] The data source generation time is the timestamp of the original data (such as SCADA measurement, GIS feature update) generated in the respective source system, which represents the "birth certificate" of the data and indicates the timeliness of the data. For example, the generation time of a device's current value is accurate to the second, which directly relates to the accuracy of operation monitoring.

[0072] The processing completion time of the completed fusion processing refers to the completion time of the final fused data generated after the original data has undergone a series of processes such as mapping, calibration, and fusion. It represents the processing completion time of the data in the system. At the same time, it reflects the efficiency and delay of the system's data processing pipeline.

[0073] By comparing the generation time and the processing completion time, the total time consumption of data processing can be calculated for performance monitoring and bottleneck analysis.

[0074] The user or system can clearly know the real-time of seeing this data. If the generation time is 10 minutes ago and the processing completion time is 5 seconds ago, it means that the data itself has a certain delay, but the system processing is fast. When the data is in doubt, it can be traced back to whether the data source itself is not "real-time" enough, or whether the system's fusion processing flow has been blocked. This is crucial for debugging and operation. Providing reliable time dimension information for visual results gives users a precise grasp of the real-time of the information on the screen.

[0075] In an optional embodiment, the display detail level loaded is determined according to the resolution and field of view of the display terminal, including:

[0076] The display detail level is determined to be a first detail level according to the resolution of the display terminal; and the display detail level is dynamically increased or decreased based on the area of the field of view of the display terminal and the first detail level as a reference.

[0077] The resolution (usually PPI, pixel density) of the display terminal is a physical property inherent to the display terminal, which determines the information density of the screen. A high-resolution screen (such as a 4K professional display) has more pixels per unit area, so it has the ability to render and present more complex and fine model details without appearing crowded or blurred. Conversely, a low-resolution screen (such as a normal notebook or mobile phone) cannot reflect its fine details if it forces to load high-detail models, and will cause visual confusion and waste a lot of GPU resources.

[0078] Therefore, determining a "first detail level" according to the resolution is equivalent to customizing an initial detail ceiling or floor for the current hardware device, providing a reasonable baseline for subsequent adjustment.

[0079] In an optional embodiment, before determining the display detail level to be a first detail level according to the resolution of the display terminal, it further includes:

[0080] Identifying the current business scenario type; when in a fault positioning or inspection navigation scenario, determining the display detail level loaded to be a second detail level, the resolution corresponding to the second detail level being greater than the resolution corresponding to the first detail level.

[0081] Specifically, when in a fault positioning or inspection navigation scenario, the display detail level is forcibly set to a preset high-detail level regardless of the current resolution and field of view. When in a fault positioning or inspection navigation scenario, the display detail level is forcibly set to a preset high-detail level regardless of the current resolution and field of view.

[0082] In an optional embodiment, it further includes:

[0083] In response to the zooming or dragging operation of the user, the field of view range is updated, and the step of determining the display detail level according to the resolution of the display terminal and the field of view range is performed again.

[0084] Any view change of the user can immediately obtain corresponding adjustment of the detail level, avoiding manual refreshing or resetting, and providing a smooth interactive experience. System resources are always used for optimal rendering of the current visible area, ensuring the coherence of the user experience and the self-adjustment ability of the system.

[0085] In an optional embodiment, after the device data corresponding to the display detail level is called and visualized and displayed, the method further comprises:

[0086] The current map movement trajectory of the user and the current display detail level are analyzed, the geographical area that the user is likely to browse next is predicted, and the device data of the geographical area corresponding to a display detail level of a higher level than the current display detail level is preloaded in the background.

[0087] By preloading the finer data before the user realizes it, when the user actually drags or zooms to the target area, the high-detail model is ready and can be displayed immediately. This completely eliminates the loading delay and realizes a smooth interactive experience with zero waiting. Instead of being a passive response to user operations, the system can actively predict the user's intention (movement trajectory) and intelligently allocate network and computing resources (only preload the area and details that are most likely needed), balancing system efficiency while improving the experience.

[0088] The power grid device data display method of the application realizes high-definition and high-performance visualization in multiple terminals and multiple scenarios by deeply coupling the three-layer fusion architecture of CIM and GIS data, using the pre-constructed CIM model level of detail (LOD) hierarchical system, and performing adaptive rendering based on a "double-factor dynamic matching mechanism". The method of the application is applied to a corresponding power grid device data display device, Figure 2 A structural schematic diagram of a power grid device data display device is shown, as Figure 2 As shown, the device includes four core functional modules, namely a data acquisition and processing module, a data fusion module, a visualization module, and a user interaction module.

[0089] Data acquisition and processing module: As the starting point of the data processing process, it is responsible for acquiring CIM model data from various data sources of the power system (such as SCADA, EMS systems), and acquiring geographic information data from the GIS database. This module performs cleaning, integration, and preprocessing on the raw data obtained to ensure the accuracy and consistency of the data, providing a high-quality data foundation for subsequent deep fusion.

[0090] Data fusion module: as the data base core of the application, it is responsible for the deep fusion of pre-processed CIM model data and GIS geographic information data. The module is built-in one of the core technical points of the application, namely the "semantic mapping-precision calibration-time and space synchronization" three-layer fusion architecture. Specifically, the module solves the data heterogeneity through semantic mapping, solves the problem of accurate positioning of equipment geographic location through precision calibration, and guarantees the timeliness of the fused data through time and space synchronization, and finally generates a fused, high-precision power grid geographic information model.

[0091] Visualization module: as the rendering and decision-making core of the application, it is responsible for automatically adjusting the visualization content according to the display context.

[0092] The module carries another core technical point of the application, namely the "CIM model LOD hierarchical construction + resolution-LOD dynamic matching" technical path. As shown in Table 1, the module first manages a pre-constructed five-level detail (LOD) system from LOD0 to LOD4; then, through "double-factor trigger logic", it detects the resolution and user field of view of the display device in real time, and automatically decides and calls the most suitable LOD level model for rendering in combination with specific business scenarios, ensuring that the best balance between information density and system performance is achieved in all scenarios.

[0093] Table 1. LOD hierarchical system table.

[0094]

[0095] User interaction module: as the connection bridge between the application and the user, it is responsible for providing a friendly graphical user interface. The module receives various types of user interaction operations (such as zooming, panning, clicking queries), and feeds these operations (especially operations that change the field of view) back to the adaptive visualization module as input to trigger dynamic adjustment of LOD. Finally, the module presents the adaptively adjusted visualization results to the user intuitively, and supports providing consistent and optimized visualization experience on various display terminals (such as PC, large screen, tablet).

[0096] In order to clearly illustrate the power grid device data display method of the application, the following examples are combined to illustrate the method, which comprises the following steps:

[0097] First step: data acquisition and processing (performed by the data acquisition and processing module).

[0098] Step 1, the system periodically acquires CIM model data from SCADA, EMS and other data sources of the power system, and at the same time, acquires the latest geographic information data from the GIS server.

[0099] Step 2: The module cleans, integrates, and pre-processes the raw data obtained to ensure data accuracy and consistency, providing a high-quality data foundation for subsequent deep fusion.

[0100] Second link: CIM-GIS data fusion (processed by the data fusion module).

[0101] Step 1: The CIM-GIS fusion module calls the built-in CIM-GIS semantic mapping dictionary, as shown in Table 2, to map device types such as "Substation" and "PowerTransformer" in CIM to "point features" and "surface features" in GIS, respectively, and establish binding relationships between CIM device core attributes (such as device ID and voltage level) and GIS element extended attribute fields. At the same time, the system provides a dynamic mapping interface to support users manually adding mapping rules for non-standardized attributes through configuration files.

[0102] Table 2. CIM-GIS semantic mapping dictionary.

[0103]

[0104] Step 2: This module uses a "multi-level calibration + dynamic completion" strategy. First, according to the number of decimal places of the CIM device geographic coordinates, the precision is divided into high, medium, and low levels. Then, for medium-precision data, the "address matching API" of the GIS system is called for calibration; for low-precision data, the "triangulation method" or line interpolation based on start and end point coordinates is used to complete the accurate coordinates combined with surrounding high-precision devices. After calibration, the system automatically generates a "device precision calibration report" for user verification.

[0105] Step 3: This module uses a "double buffer + incremental update" mechanism for data synchronization, storing GIS geographic information and CIM static attributes in the static buffer layer (updated monthly), and storing CIM real-time running data in the dynamic buffer layer (refreshed at a second level). The dynamic layer uses an "incremental push" mode, updating only when the data change exceeds the preset threshold (such as a load rate fluctuation of more than 5%). To ensure timeliness and traceability, the system adds "double time and space stamps" (data generation and fusion time) to each piece of fusion data, and enables "historical data filling + warning marking" functions when the data source is interrupted.

[0106] Third link: adaptive visualization rendering (executed by the visualization module).

[0107] Step 1: The adaptive visualization module detects the physical resolution and pixel density (PPI) of the display terminal in real time when the system starts or the user interacts, and calculates the user's current GIS map field of view range in real time (through map zoom level and latitude and longitude span calculation).

[0108] Step 2: Based on the detected resolution and field of view, this module first determines an initial LOD level according to the "Resolution-LOD Basic Mapping Table." Then, based on the basic LOD, it dynamically increases or decreases the LOD level according to the "Field of View-LOD Adjustment Rules" (e.g., LOD0 is forcibly called if the field of view > 10,000 square kilometers). Simultaneously, for specific scenarios such as fault location and inspection navigation, the system will activate scenario-based LOD priority rules, forcibly calling the specified LOD level to ensure core business needs.

[0109] Step 3, as follows Figure 3 As shown, Figure 3 This module displays LOD model data at different LOD levels. Based on decision-making results, it calls the corresponding LOD model data and employs an optimization mechanism to smoothly present the visualization to the user. When switching from low to high LOD levels, a "progressive model rendering" logic is used: the core model is loaded first to ensure fast display, and then secondary details are loaded asynchronously. Simultaneously, based on user operating habits, the system uses a "view movement trajectory prediction" algorithm to preload higher-level LOD models of the next area that might be viewed in the background, achieving a zero-latency response during switching.

[0110] The fourth stage: User interaction (executed by the user interaction module).

[0111] Step 1: This module receives the visualization results processed by the adaptive visualization rendering layer and displays them intuitively on a user-friendly interface.

[0112] Step 2: This module responds to various user interactions in real time, including device clicks via mouse, keyboard, or touchscreen (for querying detailed information), map dragging (for browsing), and map zooming (for changing the field of view).

[0113] Step 3: This module parses the user's interactive operations (especially zooming and dragging) into new field of view parameters and transmits them as feedback information to Step 1 (context awareness) of the logic processing unit 3 (adaptive visualization rendering layer) in real time, thereby triggering a new round of LOD dynamic matching and rendering, forming a complete closed loop of interaction-feedback-adaptive display.

[0114] The beneficial effects of this invention include:

[0115] 1) The internal technical contradictions of CIM and GIS data fusion are solved, and the accuracy and intuitiveness of visual display are improved. Through the "three-layer fusion architecture", the present application can intuitively display the relationship between power grid equipment and geographical environment, so that the operator can more clearly understand the overall layout and operation of the power grid. This scheme fundamentally solves the "spatial and temporal dislocation" problem caused by semantic heterogeneity, precision mismatch and real-time difference of data, ensuring the accuracy of the fused data and providing strong support for the operation, maintenance and management of the power system.

[0116] 2) Through the adaptive display mechanism, the visual experience and system performance in multiple terminals and multiple scenarios are significantly optimized. The present application can automatically adjust the visualization parameters according to the resolution of the display device, ensuring good visualization effect on different devices. Through LOD layering, the interface is simple and clear in low-resolution scenarios, without "pixel congestion", and the details are rich in high-resolution scenarios, without resource waste. Compared with the traditional full-load scheme, the low-LOD level only loads 10%-20% of the CIM model data, the memory occupation is reduced by more than 70%, the rendering frame rate on old devices (such as operation and maintenance tablets) is increased from 15fps to 60fps, and the lag is avoided.

[0117] 3) The business scenario adaptation capability is enhanced, and the efficiency of power system management and operation is improved. The friendly user interface facilitates user queries, analysis, decision-making and other operations. The scene-based LOD rules such as fault location and inspection navigation ensure the information density and response speed in core business scenarios. The system automatically completes LOD adaptation, which can reduce more than 80% of manual interface operations (such as manually hiding secondary equipment and adjusting the display scale), which is especially suitable for grassroots operation and maintenance personnel to quickly get started. Accurate and intuitive visual display and efficient user interaction functions help managers to quickly find problems and make decisions, improving the reliability of the power system and the management level.

[0118] It should be noted that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0119] Referring to Figure 2 , a schematic diagram of a power grid equipment data display device provided by an embodiment of the present application is shown, which can specifically include the following modules:

[0120] The data acquisition and processing module 401 is used to acquire device data and geographical data, wherein the device data includes device type, device attribute and device coordinate, and the geographical data includes feature type and feature attribute.

[0121] The data fusion module 402 includes:

[0122] an association submodule, configured to map the device type to the feature type by using a preset semantic mapping dictionary, and establish a binding relationship between the device attribute and the feature attribute, to obtain association relationship data;

[0123] a calibration submodule, configured to perform precision grading on the device coordinates to obtain multiple precision levels, and calibrate the device coordinates according to different precision levels;

[0124] a fusion submodule, configured to fuse the association relationship data and the calibrated device coordinates, to obtain fusion data;

[0125] a visualization module 403, configured to determine a display detail level according to a resolution and a field of view of a display terminal, call fusion data corresponding to the display detail level, perform visual rendering, and display on the display terminal.

[0126] Optionally, the precision levels include a medium precision level and a low precision level, and the calibration submodule is configured to:

[0127] for the device coordinates of the medium precision level, call an address matching service of a GIS system to perform calibration;

[0128] for the device coordinates of the low precision level, use a triangulation method or an interpolation algorithm based on route start and end point coordinates to complete the coordinates.

[0129] Optionally, the data acquisition and processing module 401 is further configured to

[0130] divide the device data into static attribute data and real-time running data;

[0131] store the geographic data, the static attribute data in the device data, and the association relationship data in a first cache layer, and perform periodic updating;

[0132] store the real-time running data in the device data in a second cache layer, and perform real-time incremental updating;

[0133] The updating frequency of the first cache layer is less than the updating frequency of the second cache layer.

[0134] Optionally, the data acquisition and processing module 401 is further configured to

[0135] for each piece of the fusion data, record a generation time of a data source used to generate the fusion data and a processing completion time of the fusion processing;

[0136] when a data source interruption is monitored, automatically use historical fusion data of a latest time to fill in, and add a warning mark to the filled data.

[0137] Optionally, the visualization module 403 is configured to:

[0138] determine the display detail level as a first display detail level according to the resolution of the display terminal;

[0139] dynamically increase or decrease the display detail level according to the area of the field of view of the display terminal, and take the first display detail level as a reference.

[0140] Optionally, the visualization module 403 is further configured to:

[0141] identify the current service scenario type before determining the display detail level as the first display detail level according to the resolution of the display terminal; and determine that the loaded display detail level is a second display detail level when in a fault location or inspection navigation scenario, wherein the second display detail level corresponds to a resolution that is greater than the resolution corresponding to the first display detail level.

[0142] Optionally, as shown in Figure 4 the power grid equipment data display device further comprises a user interaction module 404,

[0143] the user interaction module 404 is configured to:

[0144] analyze the current map moving track of the user and the current display detail level;

[0145] predict the geographical area that the user is likely to browse next;

[0146] pre-load the fusion data of the display detail level corresponding to the geographical area and having a higher level than the current display detail level in the background.

[0147] It should be noted that Figure 4 each module in the power grid equipment data display device corresponds to each module in the power grid equipment data display method. Figure 2 The specific correspondence can be determined according to the name of the module.

[0148] The power grid equipment data display device provided by the embodiment of the present application can realize each step in each power grid equipment data display method embodiment.

[0149] It should be noted that the division of the modules in the various power grid equipment data display devices provided in the above embodiments is illustrative, and is merely a logical functional division. Actual implementation can also have another division manner. In addition, each functional module in each embodiment of the present application can be integrated in one processor, or can be a separate physical existence, or can be integrated into one module in two or more modules. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0150] The integrated module, if realized in the form of a software functional module and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the technical solutions of the embodiments of the present application can be embodied in the form of a computer program product stored in a computer storage medium, including a plurality of instructions for causing an electronic device or a processor to execute all or part of the steps of the method in each embodiment of the present application. The foregoing computer storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0151] In addition, the power grid equipment data display device and the power grid equipment data display method provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0152] Referring to Figure 5 , a schematic diagram of an electronic device provided by an embodiment of the present application is shown. As Figure 5 shown, the electronic device in the embodiment of the present application includes a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the steps in the power grid equipment data display method embodiments. Alternatively, the processor executes the computer program to implement the functions of each module in the power grid equipment data display device embodiments.

[0153] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which can be used to describe the execution process of the computer program in the electronic device.

[0154] The electronic device can be a desktop computer, a cloud server, or the like computing device. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that Figure 5 The electronic device is only an example and does not constitute a limitation on the electronic device, and can include more or fewer components than shown, or combine certain components, or include different components, for example, the electronic device can also include an input / output device, a network access device, a bus, and the like.

[0155] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0156] The memory can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. Further, the memory can include both the internal storage unit and the external storage device of the electronic device. The memory is used to store the computer program and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output.

[0157] The embodiment of the present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the power grid device data display method as described in the foregoing embodiments.

[0158] The embodiment of the present application also discloses a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the power grid device data display method as described in the foregoing embodiments.

[0159] The embodiment of the present application further discloses a computer program product, which enables a computer to execute the power grid equipment data display method described in the foregoing embodiments when the computer program product runs on the computer.

[0160] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application. Although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A power grid equipment data display method characterized by comprising: The method comprises the following steps: obtaining device data and geographic data, wherein the device data comprises device type, device attribute and device coordinates, and the geographic data comprises feature type and feature attribute; mapping the device type to the feature type by using a preset semantic mapping dictionary, and establishing a binding relationship between the device attribute and the feature attribute to obtain association relationship data; grading the device coordinates according to accuracy to obtain multiple accuracy levels, and calibrating the device coordinates according to different accuracy levels; fusing the association relationship data and the calibrated device coordinates to obtain fused data; determining display detail levels according to the resolution and field of view of a display terminal; calling the fused data corresponding to the display detail levels for visual rendering and displaying on the display terminal.

2. The method of claim 1, wherein, The accuracy levels comprise medium accuracy level and low accuracy level, and the calibration of the device coordinates according to different accuracy levels comprises: for the device coordinates of the medium accuracy level, calling an address matching service of a GIS system for calibration; for the device coordinates of the low accuracy level, using a triangulation method or an interpolation algorithm based on route start and end point coordinates for coordinate completion.

3. The method of claim 1, wherein, The method further comprises the following steps: dividing the device data into static attribute data and real-time running data; storing the geographic data, the static attribute data in the device data and the association relationship data into a first cache layer, and performing periodic updating; storing the real-time running data in the device data into a second cache layer, and performing real-time incremental updating; wherein the updating frequency of the first cache layer is less than the updating frequency of the second cache layer.

4. The method of claim 1, wherein, The method further comprises the following steps: for each piece of the fused data, recording the generation time of the data source used for generating the fused data and the processing completion time of the fusion processing; when a data source interruption is monitored, using the fused data of the latest time history for filling, and adding a warning mark to the filled data.

5. The method of claim 1, wherein, The determination of the loaded display detail levels according to the resolution and field of view of the display terminal comprises the following steps: determining the display detail levels as first detail levels according to the resolution of the display terminal; dynamically increasing or decreasing the display detail levels based on the first detail levels according to the area of the field of view of the display terminal.

6. The method of claim 5, wherein, Before the determination of the display detail levels as first detail levels according to the resolution of the display terminal, the method further comprises the following steps: identifying the current business scenario type; when in a fault positioning or inspection navigation scenario, determining the loaded display detail levels as second detail levels, wherein the resolution corresponding to the second detail levels is greater than the resolution corresponding to the first detail levels.

7. The method according to any one of claims 1 to 6, characterized in that, After the calling of the fused data corresponding to the display detail levels for visual rendering and displaying, the method further comprises the following steps: analyzing the current map moving track of the user and the current display detail levels; predicting the geographical area that the user is likely to browse next; preloading the fused data of the geographical area corresponding to the display detail levels of a higher level than the current display detail levels in the background.

8. A power grid equipment data display device, characterized by comprising: The method comprises the following steps: A data acquisition and processing module is configured to acquire device data and geographic data, wherein the device data includes device type, device attribute, and device coordinate, and the geographic data includes feature type and feature attribute; A data fusion module is configured to map the device type to the feature type by using a preset semantic mapping dictionary, to establish a binding relationship between the device attribute and the feature attribute, and to obtain correlation relationship data; to perform precision grading on the device coordinate to obtain multiple precision levels, and to calibrate the device coordinate according to different precision levels; Fusion data is obtained based on the correlation relationship data and the calibrated device coordinate. A visualization module is configured to determine a display detail level according to the resolution and field of view of a display terminal, to call the fusion data corresponding to the display detail level to perform visual rendering, and to display the visual rendering on the display terminal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the power grid device data display method of any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the power grid device data display method of any one of claims 1-7.

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