Offshore converter station data management method and system

Abstract

The invention discloses an offshore converter station data management method and system, and solves the technical problem that the time cost of operation and maintenance personnel in data management work is greatly increased due to an existing offshore converter station data management method. The method comprises the steps of obtaining high-frequency data, low-frequency data and corresponding real-time meteorological data of converter station equipment, transmitting the high-frequency data and the low-frequency data by adopting an adaptive data transmission interaction protocol, and outputting high-frequency real-time data and low-frequency application data. And binding the two types of data with converter station equipment, determining three types of associated data including equipment components, equipment and equipment positions, and constructing an integrated storage library according to the three types of associated data in combination with high-frequency and low-frequency data. After a user data access request is received, interactive target data is extracted from the storage library, and a three-dimensional visual scene model of the converter station is constructed by adopting an algorithm based on grid block construction and an instantiation rendering technology and combining the data with real-time meteorological data.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a data management method and system for offshore converter stations. Background Technology

[0002] As the global energy structure shifts towards cleaner and smarter energy, DC transmission technology is widely used in cross-regional energy allocation and offshore wind power grid connection. Offshore converter platforms, as the core hub for offshore new energy grid connection, directly determine the stability and security of energy transmission through their digital and intelligent operation and maintenance levels. Against this industry backdrop, to achieve precise management and control of converter station equipment throughout its entire lifecycle, power equipment within converter stations is generally equipped with multi-dimensional sensing and monitoring systems. These systems also deploy various monitoring terminals, including high-definition cameras, infrared sensors, and power meteorological sensors. This results in massive amounts of multi-source heterogeneous data, including megahertz-level high-frequency equipment status data, static low-frequency equipment ledger drawings, and unstructured high-definition video image data. Furthermore, a high-fidelity full-station 3D scene model has become a core component of the converter station's digital platform, providing maintenance personnel with intuitive equipment and scene visualization support.

[0003] Focusing on the specific application scenario of offshore converter platforms, the management requirements for data and 3D resources are more stringent than those for onshore converter stations. On the one hand, offshore converter platforms have a compact equipment layout and complex operating conditions, requiring extremely high real-time and accuracy of equipment status data. On the other hand, the 3D scene model needs to take into account the dynamic linkage of equipment entities, building structures, and the marine environment to adapt to the special meteorological and operational conditions at sea. Although the industry has attempted to build a unified data interface for converter stations to achieve basic data access, a systematic architecture design and management method has not yet been formed for the specific characteristics of offshore converter platforms. Various types of data are mostly stored and retrieved in a stacked manner, without matching differentiated transmission protocols based on the frequency characteristics of the data, nor establishing a precise binding relationship between data and equipment components and 3D scene models.

[0004] Existing data management methods for offshore converter stations still employ traditional triangulation algorithms when constructing 3D scene models. When processing scattered 3D data points in the overall scene of the converter station, this algorithm requires global, undifferentiated triangulation calculations of all point cloud data across the entire area. Furthermore, when dealing with high-density point cloud data from multiple devices and large scenes on an offshore converter platform, a single round of triangulation requires a significant amount of computing power to complete the correlation calculations of millions or even tens of millions of data points, resulting in an exponential increase in computational load and ultimately leading to a substantial increase in the time cost for maintenance personnel in data management. Summary of the Invention

[0005] This invention provides a data management method and system for offshore converter stations, which solves the technical problem that existing offshore converter station data management methods lead to a significant increase in the time cost for operation and maintenance personnel in data management work.

[0006] The first aspect of this invention provides a data management method for an offshore converter station, comprising:

[0007] The system acquires high-frequency and low-frequency data and corresponding real-time meteorological data from the converter station equipment, and transmits the high-frequency and low-frequency data using a data transmission interaction protocol, outputting high-frequency real-time data and low-frequency application data.

[0008] The high-frequency real-time data is bound to the low-frequency application data and the converter station equipment to determine the converter station equipment component association data, converter station equipment association data, and converter station equipment location association data;

[0009] An integrated storage repository is constructed based on the converter station equipment component association data, the converter station equipment association data, the converter station equipment location association data, the high-frequency data, and the low-frequency data;

[0010] When a user initiates a data access request, interactive target data is extracted from the integrated repository based on the user's data access request.

[0011] A grid-based block construction algorithm and instantiation rendering technology are used to construct a three-dimensional visualization scene model of the converter station based on the interactive target data and the real-time meteorological data.

[0012] Optionally, the data transmission interaction protocol includes the Kafka message queue protocol and the Representational State Transfer Application Programming Interface protocol; the step of using the data transmission interaction protocol to transmit the high-frequency data and the low-frequency data, and outputting high-frequency real-time data and low-frequency application data, includes:

[0013] The high-frequency data is processed using the Kafka message queue protocol for low-latency, high-throughput transmission to obtain high-frequency real-time data.

[0014] The low-frequency data is processed using a descriptive state transfer application programming interface protocol to achieve lightweight transmission, thereby obtaining low-frequency application data.

[0015] Optionally, the step of binding the high-frequency real-time data with the low-frequency application data and the converter station equipment to determine converter station equipment component association data, converter station equipment association data, and converter station equipment location association data includes:

[0016] Based on the low-frequency application data, the equipment component ledger data, equipment component fault record data and equipment component loss data corresponding to the equipment components in the converter station equipment are extracted, and the equipment component ledger data, equipment component fault record data and equipment component loss data are bound to the corresponding equipment group components, and the converter station equipment component association data is output.

[0017] Extract the equipment operation data and equipment environment data corresponding to the converter station equipment from the low-frequency application data, and bind the equipment operation data, the equipment environment data and the high-frequency real-time data with the converter station equipment to output the converter station equipment association data;

[0018] Based on the physical location coordinates of the converter station equipment, a mapping relationship between the infrared camera and the converter station equipment is established, generating converter station equipment location association data.

[0019] Optionally, the step of constructing an integrated repository based on the converter station equipment component association data, the converter station equipment association data, the converter station equipment location association data, the high-frequency data, and the low-frequency data includes:

[0020] The low-frequency data is stored in a relational database format to obtain low-frequency data in relational database format;

[0021] The high-frequency data is stored in file format to obtain high-frequency data in file format.

[0022] The converter station equipment component association data, the converter station equipment location association data, and the high-frequency data in file format are hot-stored to obtain hot-stored data;

[0023] The converter station equipment association data and the low-frequency data in the relational database format are cold-stored to obtain cold-stored data.

[0024] Build an integrated repository based on hot-stored data and cold-stored data.

[0025] Optionally, when a user initiates a data access request, extracting interactive target data from the integrated repository based on the user-initiated data access request includes:

[0026] When a data access request initiated by the user is received, it is determined whether the data access request initiated by the user meets the preset application conditions;

[0027] If the conditions are met, then the user's data access request will be subject to permission verification.

[0028] If the verification is successful, an access token is generated and added to the user's data access request for identity verification.

[0029] If the identity verification is successful, the device component association data, high-frequency data in file format, and low-frequency data in relational database format are extracted from the integrated repository. The data interaction is carried out using a differentiated data adaptation and interaction method, and the interactive target data is output.

[0030] Optionally, the step of constructing a 3D visualization scene model of the converter station based on the grid-based block construction algorithm and instantiation rendering technology according to the interactive target data and the real-time meteorological data includes:

[0031] Extract the basic parameters of the overall 3D scattered data points of the converter station scene and the basic parameters of the same type of model components from the interactive target data;

[0032] The algorithm based on grid block construction is used to construct a model of the scattered 3D data points of the overall scene of the converter station, and outputs a basic triangular network model of the global 3D scene.

[0033] The instantiation rendering technology is used to draw in batches based on the basic parameters of the same type of model components, and the rendered same type of components are output.

[0034] The model is constructed based on the triangular mesh model of the global 3D scene, the rendered components of the same type, and the associated data of the device components in the interactive target data. The real-time meteorological data is then embedded to generate a 3D visualization scene model of the converter station.

[0035] A second aspect of the present invention provides a data management system for an offshore converter station, comprising:

[0036] The acquisition module is used to acquire high-frequency data and low-frequency data and corresponding real-time meteorological data of the converter station equipment, and to transmit the high-frequency data and the low-frequency data using a data transmission interaction protocol, and output high-frequency real-time data and low-frequency application data.

[0037] The binding module is used to bind the high-frequency real-time data with the low-frequency application data and the converter station equipment, and to determine the converter station equipment component association data, converter station equipment association data and converter station equipment location association data;

[0038] The database construction module is used to construct an integrated storage repository based on the converter station equipment component association data, the converter station equipment association data, the converter station equipment location association data, the high-frequency data, and the low-frequency data;

[0039] The receiving module is used to extract interactive target data from the integrated repository based on the user's data access request when it receives the user's data access request.

[0040] The model building module is used to construct a three-dimensional visualization scene model of the converter station based on the interactive target data and the real-time meteorological data, using a grid-based block construction algorithm and instantiation rendering technology.

[0041] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the offshore converter station data management method described above.

[0042] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the data management method for offshore converter stations as described above.

[0043] The fifth aspect of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the offshore converter station data management method described above.

[0044] As can be seen from the above technical solutions, the present invention has the following advantages:

[0045] The present invention provides a data management method for offshore converter stations, which acquires high-frequency and low-frequency data and corresponding real-time meteorological data from converter station equipment, and transmits the high-frequency and low-frequency data using a data transmission interaction protocol, outputting high-frequency real-time data and low-frequency application data; binds the high-frequency real-time data and low-frequency application data to the converter station equipment, determining the associated data of converter station equipment components, the associated data of converter station equipment, and the associated data of converter station equipment location; constructs an integrated storage repository based on the associated data of converter station equipment components, the associated data of converter station equipment, the associated data of converter station equipment location, the high-frequency data, and the low-frequency data; when a user initiates a data access request, interactive target data is extracted from the integrated storage repository based on the user's data access request; and constructs a three-dimensional visualization scene model of the converter station based on the interactive target data and real-time meteorological data using a grid-based block construction algorithm and instantiation rendering technology. Based on the above scheme, this invention combines a grid-based block construction algorithm and instantiation rendering technology to transform the original global exponential operation into multiple lightweight local operations, and avoids the repetitive operation of traditional one-by-one rendering, further compressing the rendering time of model components, thereby significantly reducing the time cost for operation and maintenance personnel in data management. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of a data management method for an offshore converter station according to Embodiment 1 of the present invention.

[0048] Figure 2 This is a schematic diagram of the data access architecture provided in Embodiment 1 of the present invention;

[0049] Figure 3 This is a schematic diagram of the real-time data interaction process provided in Embodiment 1 of the present invention;

[0050] Figure 4 This is a flowchart of the model rendering scheduling process provided in Embodiment 1 of the present invention;

[0051] Figure 5 This is a structural block diagram of a data management system for an offshore converter station provided in Embodiment 2 of the present invention. Detailed Implementation

[0052] This invention provides a data management method and system for offshore converter stations, which solves the technical problem that existing offshore converter station data management methods lead to a significant increase in the time cost for operation and maintenance personnel in data management work.

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0054] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a data management method for an offshore converter station provided in Embodiment 1 of the present invention.

[0055] This invention provides a data management method for offshore converter stations, comprising:

[0056] Step 101: Obtain high-frequency and low-frequency data and corresponding real-time meteorological data from the converter station equipment, and use a data transmission interaction protocol to transmit the high-frequency and low-frequency data, outputting high-frequency real-time data and low-frequency application data.

[0057] Low-frequency data includes equipment ledgers, equipment monitoring data, scenario model data, drawings, test reports, etc.

[0058] High-frequency data includes high-definition video, images, infrared data, and data from array sensors.

[0059] It should be noted that the process involves acquiring high-frequency and low-frequency data from the converter station equipment, along with corresponding real-time meteorological data. A data transmission protocol is used to transmit the high-frequency and low-frequency data, outputting high-frequency real-time data and low-frequency application data. Specifically, the high-frequency data from the converter station equipment is collected through real-time monitoring terminals such as array sensors, infrared thermal imagers, and high-definition industrial cameras mounted on the equipment itself. Low-frequency data is extracted from the converter station's operation and maintenance management system and the equipment's lifecycle platform. Real-time meteorological data is obtained from meteorological monitoring stations deployed at the converter station or from third-party meteorological services. During the acquisition of these three types of data, the data generation time, associated equipment identification, and acquisition location information are recorded simultaneously to ensure data traceability. During the data transmission phase, the data transmission interaction protocol will match the appropriate sub-protocol type according to the data characteristics. For the real-time requirements of high-frequency data, a high-throughput and low-latency transmission sub-protocol will be selected, while for the lightweight requirements of low-frequency data, a low-resource-consumption transmission sub-protocol will be selected. At the same time, real-time meteorological data can be synchronously processed according to the high-frequency data transmission standard and integrated as a supplementary dimension to the high-frequency real-time data. The final output high-frequency real-time data maintains millisecond-level update accuracy, while low-frequency application data undergoes format regularization and redundancy removal, providing a standardized data foundation for subsequent data and device binding operations.

[0060] Further, step 101 may include the following sub-steps:

[0061] S11. The Kafka message queue protocol is used to process high-frequency data with low latency and high throughput to obtain high-frequency real-time data.

[0062] S12. The low-frequency data is processed by a representational state transfer application programming interface protocol to obtain low-frequency application data.

[0063] Data transmission interaction protocols include the Kafka message queue protocol and the Representational State Transfer Application Programming Interface protocol.

[0064] Low latency and high throughput transmission processing refers to transmission optimization operations designed for the characteristics of high frequency data. "Low latency" means that the transmission time difference from the acquisition terminal to the receiving end is controlled at the millisecond level, and "high throughput" means that a large amount of data can be stably transmitted per unit time, thereby ensuring the timeliness and integrity of high frequency data.

[0065] It should be noted that the data access architecture diagram is as follows: Figure 2 As shown, the data is divided into high-frequency and low-frequency data. High-frequency data generally includes equipment status sensing systems, current transformers (CTs) and voltage transformers (VTs) in converter stations, and recorded data. Low-frequency data includes image data from high-definition cameras and infrared cameras. Low-frequency data is connected to the front-end application layer (the computer's .exe application receives and visualizes the data) using the REST API protocol (Representational State Transfer Application Programming Interface Protocol), while high-frequency data is connected to the front-end application layer using Kafka (Kafka Message Queuing Protocol) to ensure efficient data processing and meet the different real-time requirements of field operators.

[0066] Specifically, high-frequency data mainly originates from array vibration sensors, infrared thermal imagers, high-definition industrial cameras, and electrical quantity acquisition terminals mounted on the core equipment of the converter station. This includes dynamic monitoring data with an acquisition frequency of no less than 1Hz, such as real-time equipment vibration parameters, surface temperature flow, operating condition video streams, and instantaneous current and voltage values. When transmitting via the Kafka message queue protocol, the system first encapsulates the high-frequency data into data packets according to equipment function partitions and adds a unique device identifier and acquisition timestamp. Simultaneously, it activates a queue priority scheduling mechanism, assigning monitoring data from key equipment such as converter valves and main transformers to high-priority transmission queues, relying on the asynchronous batch processing capability of the protocol itself. The high-frequency transmission characteristics enable single data transmission latency to be controlled at the millisecond level, and a single link can carry a stable transmission of millions of data points per second. Low-frequency data includes static or quasi-static data with an update frequency of less than 1Hz, such as converter station equipment ledgers, factory test reports, design drawings, and monthly maintenance records. Before lightweight transmission through the expressive state transfer application programming interface protocol, the system first filters out invalid and redundant fields and performs structured format conversion on the low-frequency data, uniformly encapsulating it into the JSON standard format. At the same time, a local cache pool is established for low-frequency data with high-frequency queries, such as equipment ledgers, to avoid repeated cross-system retrieval and reduce the consumption of transmission link resources. This differentiated transmission processing method allows both high-frequency and low-frequency data to adapt to their own characteristics and achieve efficient flow. The final output of high-frequency real-time data fully retains the immediacy and traceability of the equipment status, while low-frequency application data achieves standardized format and reduced size. No maintenance personnel need to perform additional preprocessing work such as data format conversion, redundancy removal, and source verification. The data processing task after transmission, which originally required maintenance personnel to spend several hours to complete, can now be directly connected to the subsequent equipment data binding process. It can break through the disorderly data transmission dilemma under the traditional single transmission protocol architecture and eliminate the cumbersome preprocessing process caused by inconsistent data formats and redundant information accumulation. It resolves the technical pain point of excessive time and high time cost for maintenance personnel in the data processing stage from the source of transmission. In turn, it lays a solid foundation of standardized and timely data for the subsequent construction of an integrated storage repository and the efficient construction of a 3D visualization scene model of the converter station.

[0067] Step 102: Bind the high-frequency real-time data with the low-frequency application data and the converter station equipment to determine the converter station equipment component association data, converter station equipment association data, and converter station equipment location association data.

[0068] Converter station equipment component association data refers to the association data formed by binding the component-level monitoring data in high-frequency real-time data with the specific parts of the converter station equipment, which can accurately reflect the operating status of the core components of the equipment.

[0069] Converter station equipment associated data refers to the associated data formed by integrating the high-frequency real-time operating data of the entire equipment with the low-frequency basic ledger data, realizing the integrated association of the dynamic status and static attributes of the equipment.

[0070] Converter station equipment location association data refers to the association data formed by mapping various monitoring data with the physical installation points of converter station equipment, which can realize the accurate correspondence between monitoring data and the actual spatial location of equipment.

[0071] It should be noted that this invention assigns a unique identification code to all equipment and components in the converter station, establishing a hierarchical association system from equipment components (such as converter valve thyristors and transformer bushings) to the equipment itself (such as converter valve groups and main transformers) and then to the physical installation location of the equipment. Subsequently, it extracts the acquisition terminal identifier, equipment code, and timestamp information carried in the high-frequency real-time data, as well as the equipment ledger number, component model, and other information recorded in the low-frequency application data. Through a coding matching mechanism, it accurately binds the high-frequency real-time data with the real-time operating status of the equipment and associates the low-frequency application data with the basic attribute information of the equipment; [This ensures...] When defining the three types of associated data, component-level monitoring data (such as thyristor temperature data) from high-frequency real-time data will be bound to the corresponding equipment components to generate converter station equipment component associated data. The integrated real-time data of the entire equipment will be merged with the basic ledger data to generate converter station equipment associated data. At the same time, the rotational degree of freedom parameters of monitoring terminals such as infrared cameras and the physical location coordinates of the equipment will be combined to map various monitoring data to the actual installation points of the equipment to generate converter station equipment location associated data. The entire binding process will synchronously generate data and equipment association verification logs to ensure the accuracy and traceability of the binding relationship.

[0072] Furthermore, step 102 may include the following sub-steps:

[0073] S21. Based on low-frequency application data, extract the equipment component ledger data, equipment component fault record data and equipment component loss data corresponding to the equipment components in the converter station equipment, and bind the equipment component ledger data, equipment component fault record data and equipment component loss data with the corresponding equipment group components, and output the converter station equipment component association data;

[0074] S22. Extract the equipment operation data and equipment environment data corresponding to the converter station equipment from the low-frequency application data, and bind the equipment operation data, equipment environment data and high-frequency real-time data with the converter station equipment to output the converter station equipment associated data;

[0075] S23. Based on the physical location coordinates of the converter station equipment, establish a mapping relationship between the infrared camera and the converter station equipment, and generate converter station equipment location association data.

[0076] An infrared camera is a monitoring terminal deployed in a converter station to monitor the temperature status of equipment. It has an adjustable field of view with a degree of rotational freedom and can collect infrared thermal imaging data of the equipment.

[0077] It should be noted that in the scenario model built within the converter station, the data is associated with the scenario model in a hierarchical manner. At the equipment level, the data is directly associated with the equipment components. For example, for a transformer, its components include bushings, tap changers, windings, and cores. When a bushing is selected in the front-end application, relevant data such as bushing ledgers, faults, and losses are exported and visualized in real time (in the past, for transformers, all relevant data such as bushings, tap changers, windings, and cores were visualized without classification, and the transformer was not broken down into lower-level components).

[0078] At the converter station level, each device is associated with data such as operating conditions, environmental conditions, and high-definition camera data. Users can directly select devices (device name, number) via drop-down menus or specify target locations within the substation using input methods such as touchscreen clicks or keyboard input, thus mapping the data. If the device name or number is entered, the relevant data is directly retrieved. For data from visualized images such as infrared cameras, issues arise regarding lens rotation (one lens corresponds to multiple converter station-level devices in different locations). The mapping is achieved by mapping these locations to corresponding physical coordinates using a pre-established substation device location database, and simultaneously constructing rotational degree-of-freedom parameters for the corresponding infrared cameras.

[0079] Specifically, when extracting and binding data related to equipment components, the system first filters out equipment component ledger data (including basic information such as component model, factory technical parameters, and installation time) from the structured database of low-frequency application data, equipment component fault record data (including historical fault occurrence time, fault cause, maintenance plan, and recovery status), and equipment component loss data (including loss values, loss trends, and lifespan estimates from periodic maintenance and inspection). Using the unique identification code of the equipment component as a matching identifier, the system accurately binds these three types of data to corresponding equipment components such as converter valve thyristors and transformer bushings. Simultaneously, a binding relationship verification code is generated to ensure that data association is error-free. In the converter station equipment association data stage, equipment operation data (including equipment rated power, daily start-up and shutdown records, maintenance cycles, etc.) and equipment environmental data (including temperature and humidity of the equipment installation area, salt spray level, electromagnetic interference intensity, etc.) are further extracted from low-frequency application data. Then, combined with dynamic monitoring information such as real-time current and voltage, surface temperature, and vibration parameters of the equipment in high-frequency real-time data, the cross-type binding of multi-source data is completed with the unique code of the entire converter station equipment as the core identifier. During the binding process, the timestamps of high-frequency data and low-frequency data are aligned simultaneously to ensure the consistency of data time sequence. When constructing equipment location association data, the physical location coordinates of the equipment in the converter station's whole-station GIS (Geographic Information System) spatial coordinate system are retrieved first. Then, combined with the installation point coordinates and rotational degree of freedom parameters of the infrared cameras, a spatial coordinate matching algorithm is used to establish the mapping relationship between the camera monitoring view and the physical location of the equipment. The equipment range that each camera can cover and the spatial ownership of the corresponding monitoring data are clarified. Finally, converter station equipment location association data containing equipment location information and the association relationship of monitoring terminals is generated. This multi-dimensional data binding process enables end-to-end association from device components to the entire machine, from static basic data to dynamic monitoring data, and from data attributes to spatial location. It avoids the tedious operation of maintenance personnel manually searching and matching corresponding device information in multi-source data. The device data association and organization work that originally required maintenance personnel to spend several hours to complete can now be completed automatically and accurately by the system. It overcomes the technical bottleneck of high time cost of maintenance data management caused by weak data association and low matching efficiency in the traditional model. It not only greatly improves the efficiency of data integration, but also lays a standardized and traceable data foundation for the subsequent construction of an integrated repository, laying a solid foundation for the efficient advancement of end-to-end data management.

[0080] Step 103: Construct an integrated repository based on converter station equipment component association data, converter station equipment association data, converter station equipment location association data, high-frequency data, and low-frequency data.

[0081] It should be noted that this invention first establishes a layered storage architecture of "hot data - warm data - cold data" for the integrated repository based on data type, access frequency, and usage scenario. High-frequency data and converter station equipment-related data with high real-time requirements are placed in the hot data layer, stored using an in-memory database to ensure millisecond-level retrieval response. Converter station equipment component-related data and equipment location-related data are placed in the warm data layer, stored using a distributed database to balance storage costs and retrieval efficiency. Low-frequency historical archive data is placed in the cold data layer, retained long-term using low-cost cloud storage media. During the data entry process, multi-dimensional correlation indexes are established for various types of data, using unique equipment codes, component codes, and location coordinates as core index fields. Simultaneously, data traceability tags are embedded, synchronously recording the entire data chain information such as the data acquisition terminal, transmission protocol, and binding time. A real-time data verification mechanism is also activated to automatically verify the completeness and correlation of the entered data. If data loss or misalignment is detected, an alert is triggered and automatic correction is performed to ensure the accuracy and consistency of data within the repository. The establishment of this integrated repository enables centralized, hierarchical, and correlated storage of multi-source heterogeneous data from converter stations. This completely changes the chaotic situation in traditional data management where various types of data are scattered across different systems without a unified index. Maintenance personnel no longer need to search across multiple databases one by one and manually integrate equipment-related data. The cross-database data query and integration work that originally took several hours can now be accurately retrieved in seconds through multi-dimensional indexing.

[0082] Furthermore, step 103 may include the following sub-steps:

[0083] S31. Store the low-frequency data in a relational database format to obtain low-frequency data in relational database format;

[0084] S32. Store the high-frequency data in file format to obtain high-frequency data in file format;

[0085] S33. Perform hot storage on the converter station equipment component association data, converter station equipment location association data, and high-frequency data in file format to obtain hot storage data;

[0086] S34. Cold storage is used to process the converter station equipment association data and low-frequency data in relational database format to obtain cold storage data;

[0087] S35. Build an integrated repository based on hot storage data and cold storage data.

[0088] Low-frequency data in relational database format refers to low-frequency data that has been structured and stored in the form of relational database table structure. It has the characteristics of clear field definition, clear data association constraints, and strong transaction consistency, and is suitable for long-term management of static and periodically updated data.

[0089] High-frequency data in file format refers to the storage form of high-frequency data after it has been converted into the corresponding file format according to its data attributes. The device and time identifiers are embedded in the file name, which is suitable for efficient access to unstructured or semi-structured high-frequency monitoring data.

[0090] Hot storage refers to a storage method designed for data with high access frequency and high real-time requirements. It typically uses high-speed storage media such as SSD solid-state drives and system caches, with the core objective of ensuring millisecond-level data retrieval response.

[0091] Hot storage data refers to the collection of stored data formed after the converter station equipment component association data, equipment location association data, and high-frequency data in file form are processed by a high-speed hot storage architecture, which has the characteristics of real-time retrieval and fast response.

[0092] Cold storage refers to a storage method designed for data that is accessed infrequently and needs to be archived and retained for a long time. It uses low-cost storage media and its core goal is to achieve long-term and complete data retention while controlling storage costs.

[0093] Cold storage data refers to the collection of stored data formed after the converter station equipment-related data and low-frequency data in relational database format are processed by a cold storage solution. It has the characteristics of low storage cost and orderly archiving.

[0094] An integrated storage repository refers to a centralized data storage system that integrates hot and cold storage architectures and achieves full-domain data association through a unified data management middleware, enabling efficient data retrieval, orderly archiving, and full lifecycle management.

[0095] It should be noted that equipment ledgers, equipment monitoring data, scene model data, drawings, and test reports are stored in a relational database, while high-definition video, images, infrared, and array sensor data in the converter station are stored in file format.

[0096] Meanwhile, equipment monitoring data, scene model data, high-definition video, images, infrared data, and array sensor data are stored using hot storage in SSDs (Solid State Drives) or system cache areas for real-time retrieval and historical review. Equipment ledger data, drawings, test reports, and other data from the converter station's infrastructure construction phase are stored using cold storage. In the hot storage method, a data service is created to convert hot-stored data to cold-stored data by setting a time threshold (one month or several weeks, depending on the user), releasing related storage resources; alternatively, the conversion can be skipped and the data can be overwritten.

[0097] When cold data needs to be accessed, the data service generates a data extraction task and notifies the cold data aggregation service to extract and cache the data through a message mechanism.

[0098] Furthermore, when converting low-frequency data into a relational database format, the system uses the unique identification code of the converter station equipment and components as the primary key, and constructs a data table structure according to the hierarchy of "equipment unit - equipment component - data category". Low-frequency data such as equipment ledgers, maintenance records, and fault history are accurately filled into the corresponding fields. At the same time, the system establishes association constraints between fields to ensure data integrity. Finally, a MySQL relational database is selected for storage, forming a well-structured relational database format for low-frequency data. For high-frequency data, an appropriate file format is generated according to its data type. For example, infrared thermal imaging data is stored in TIFF (Tagged Image File Format), equipment operation video streams are stored in MP4 format, and sensor real-time monitoring data is stored in binary file format. All file names embed the equipment code, acquisition timestamp, and data type identifier to achieve rapid data identification and classification, thereby obtaining high-frequency data in file format. In the hot storage stage, a high-speed storage architecture combining SSDs and system cache is adopted. High-frequency data in file format, requiring millisecond-level response times, is directly stored in the system cache. Data related to converter station components and device locations is stored on SSDs. A memory index based on device codes is created for all three types of data to ensure efficient real-time retrieval, ultimately forming the hot storage data. For cold storage, a low-cost solution combining distributed disk arrays and cloud archive storage is used. Before storage, low-frequency data related to converter station devices and relational database formats are losslessly compressed and archived according to the logic of "year-device region-device type," resulting in cold storage data. When building the integrated repository, a unified data management middleware is established to create cross-storage-level relational indexes for hot and cold storage data, enabling full-domain data linkage queries based on device codes. A built-in data lifecycle management module automatically migrates data from hot to cold storage based on data retrieval frequency, ensuring uninterrupted data association after migration. The construction of this integrated repository completely breaks the drawbacks of the traditional data storage model, which involves the mixed storage of high-frequency and low-frequency data and the lack of differentiated management of hot and cold data. Operation and maintenance personnel no longer need to manually retrieve and integrate device-related data across multiple storage media. They can simultaneously retrieve real-time monitoring data from hot storage and historical basic data from cold storage simply by inputting the device code through a unified data interface. What used to require several hours of cross-source data query work by operation and maintenance personnel can now be completed in tens of seconds. This overcomes the time-consuming problem of operation and maintenance data management caused by the scattered data storage and inefficient retrieval in the traditional model. It not only significantly reduces the data retrieval and integration time for operation and maintenance personnel, but also lays the foundation for a unified data support platform that is fully interconnected, efficient and reliable for subsequent data access verification processes and visualization modeling work.

[0099] Step 104: When a user initiates a data access request, extract interactive target data from the integrated repository based on the user's data access request.

[0100] It should be noted that, as Figure 3 As shown, by obtaining application service authorization, point-to-point data connection is provided, and real-time data interaction is achieved using REST APIs, file transfer, Kafka message queues, or other methods. The specific process is as follows:

[0101] 1) When a user initiates a request, it is determined whether the user has applied for the corresponding data application; if not, the application creation step is initiated, and the service access is applied for through manual review, and the key is downloaded and saved for the user.

[0102] 2) If you have already applied, proceed to step 2 to apply for data access. Applying for access to x-token, calling RESTful services, and adding it to the request header are all standard methods of data-user authentication. When using X-Token for authentication, the client needs to add a field named X-Token to the request header, and the value of the field is the user's identity token.

[0103] 3) Verify identity information and determine if there are any anomalies;

[0104] 4) Complete the verification by using REST API, file transfer, KAFKA message queue, or other methods to achieve data interaction.

[0105] Specifically, step 104 may include the following sub-steps:

[0106] S41. When a user initiates a data access request, determine whether the user's data access request meets the preset application conditions.

[0107] S42. If the conditions are met, then perform permission verification on the user's data access request.

[0108] S43. If the verification is successful, an access token is generated and added to the user's data access request for identity verification.

[0109] S44. If the identity verification is successful, extract the device component association data, high-frequency data in file format, and low-frequency data in relational database format from the integrated repository, and use a differentiated data adaptation and interaction method to perform data interaction and output interactive target data.

[0110] It should be noted that when a user initiates a data access request, the system will first conduct an access verification based on preset application conditions. These preset application conditions specifically include three core dimensions: first, the user has completed the filing application for the corresponding data application; second, the user has a compliant key credential for service access; and third, the user's data access operation must comply with the system's established authentication standards and specifications. Based on these preset application conditions, the system first determines whether the user has applied for the corresponding data application. If not, the core requirement of "data application filing" in the preset application conditions is not met, and the user needs to proceed to the application creation step. The user needs to submit application materials such as the application name, actual usage scenario, and the data type and scope to be retrieved. After that, the user applies for service access through manual review. Once the review is approved, the filing of the corresponding data application is completed. At the same time, a unique key is generated and issued to the user, and the user is guided to complete the local download and secure storage of the key, thereby fulfilling the access requirement of "compliant key certificate" in the preset application conditions. If the user has applied for the corresponding data application, the requirement of "data application filing" in the preset application conditions is met, and the user directly enters the data access application stage. Applying for access to X-Token, calling RESTful services, and adding X-Token (i.e., access token) to the request header are all standard data-user authentication methods specified in the "authentication standard specification" in the preset application conditions. When using X-Token for identity verification, the client needs to strictly follow the specification and add a field named X-Token to the request header. The value of the field is the identity token obtained by the user, thus completing the standardized submission of authentication information. After the access application is completed, the system proceeds to the identity verification stage. Based on preset application conditions, the system focuses on comparing the validity and timeliness of the X-Token, the matching degree between the key and the user's identity, and the consistency between the requested data range and the registered application. This is to determine if there are any anomalies such as expired tokens, mismatched keys, or data access exceeding the scope. Once verification is completed and no anomalies are confirmed (i.e., all requirements of the preset application conditions are fully met), data interaction is achieved using REST API, file transfer, or Kafka message queues. This accurately extracts device component-related data, high-frequency data in file format, and low-frequency data in relational database format from the integrated repository, completing compliant and efficient data transmission and outputting interactive target data. This data access processing flow uses preset application conditions as the end-to-end operational guideline, achieving standardized control over data access applications, identity verification, and data interaction. It replaces the tedious operation of manual verification of request compliance and fragmented processing of application registration and identity verification by maintenance personnel in the traditional model. What used to require 1-2 hours for maintenance personnel to complete application access review and data access verification can now be efficiently completed within tens of minutes using a standardized process.

[0111] Interactive target data refers to the final data set extracted from the integrated repository and standardized after passing the full compliance review of the application conditions and identity verification process. This data is achieved through differentiated interaction methods such as RESTAPI (low-frequency structured data), file transfer (high-frequency large file video data), and Kafka message queues (high-frequency real-time monitoring data). The data is clearly divided into three categories according to the user's actual use: First, high-frequency monitoring data for device status early warning, which originates from high-frequency data in file format and real-time dynamic fields in device component-related data, used to support real-time early warning and status analysis of abnormal device operation. The data is categorized into three types: first, low-frequency static data for equipment records, which is derived from basic attribute information in low-frequency data and equipment-related data in relational database formats, suitable for record management scenarios such as equipment ledger retrieval, historical fault tracing, and maintenance record verification; second, scene model visualization data, which integrates high-frequency video image data (such as equipment operation videos and infrared thermal images) with low-frequency scene model-related data (such as equipment physical location coordinates and scene structure model data), used for applications such as converter station 3D visualization modeling and scene simulation. All three types of data are accompanied by data traceability tags and permission identifiers, which can directly meet the diverse business needs of users.

[0112] Step 105: Using a grid-based block construction algorithm and instantiation rendering technology, construct a 3D visualization scene model of the converter station based on interactive target data and real-time meteorological data.

[0113] Spatial Block Algorithm Optimization (Mesh Block Construction Algorithm): In model spatial block algorithms, Delaunay triangulation (DT) is commonly used. However, this method is computationally intensive, has complex data structures, and consumes significant computer storage, making it difficult to implement. Therefore, this paper proposes a mesh block construction algorithm that requires less storage space and whose execution time is approximately linearly related to the number of nodes. The mesh block algorithm groups given 3D scattered data points into square grids. This algorithm uses a quadtree structure to process the data, dividing it level by level to ensure that the number of nodes in each grid is not significantly different from the number of triangles at the junction of adjacent grids. Triangular meshes are constructed within each of the divided grids, and finally, these grouped meshes are connected to form a whole, generating the final triangular mesh model of the entire point set.

[0114] Instantiated rendering technology is a "batch processing" method performed by the GPU (Graphics Processing Unit) through the invocation of a "special" rendering interface. Its biggest difference from traditional rendering methods lies in the fact that when invoking the rendering command, the GPU needs to be informed of the number of times to render (N objects). When the GPU receives this command, it will continuously render N objects onto our screen, which is far more efficient than continuously invoking the traditional rendering command N times and rendering one object at a time.

[0115] The 3D visualization scene model of the converter station includes the equipment and buildings inside the converter station, as well as the surrounding environmental information. It can also embed a model with pre-set weather information (fog, rain, snow, daytime, nighttime, etc.) and link it with meteorological data.

[0116] Furthermore, step 105 may include the following sub-steps:

[0117] S51. Extract the basic parameters of the 3D scattered data points of the overall scene of the converter station and the basic parameters of the same type of model components from the interactive target data.

[0118] S52. An algorithm based on grid block construction is used to build a model of the scattered 3D data points of the overall scene of the converter station, and outputs a basic triangular network model of the full-domain 3D scene.

[0119] S53. Use instantiation rendering technology to draw in batches based on the basic parameters of similar model components, and output the rendered components of the same type.

[0120] S54. Based on the triangular network model of the full-domain 3D scene, the rendered components of the same type, and the associated data of the equipment components in the interactive target data, a model is constructed, and real-time meteorological data is embedded to generate a 3D visualization scene model of the converter station.

[0121] 3D scattered data points refer to the set of discrete coordinate points in the converter station scene space extracted from interactive target data. They cover spatial information such as site topography, equipment installation points, and building outlines, and are the basic data for building 3D scene models.

[0122] The basic parameters of similar model components refer to the core data describing the geometric and attribute characteristics of similar equipment components in the converter station, including component model, geometric dimensions, material properties, installation reference coordinates and connection relationships, etc., which are used for batch rendering and modeling.

[0123] The global 3D scene basic triangular mesh model refers to the basic model of the converter station scene formed after processing by the mesh block construction algorithm. It is composed of non-overlapping triangular meshes, reflecting the spatial structure and contour features of the scene.

[0124] The rendered components of the same type refer to device component models that have been batch-drawn according to basic parameters and given materials, textures and lighting effects based on instantiation rendering technology, and can be directly used for global scene integration.

[0125] The converter station 3D visualization scene model refers to an integrated visualization model that combines the basic triangular network of the full-domain 3D scene, rendering components, equipment component association data and real-time meteorological data. It supports spatial interaction and data query, and intuitively presents the converter station scene and equipment status.

[0126] It should be noted that, as Figure 4As shown, the 3D scattered data points of the converter station scene extracted from the interactive target data cover spatial data such as the converter station site terrain coordinates, equipment foundation installation points, and building outline feature points. The basic parameters of the same type of model components include core information such as component model, geometric dimensions, material properties, installation reference coordinates, and connection relationships. When using a grid-based block construction algorithm, the 3D scattered data points are first divided into blocks according to the converter station's functional areas (such as the converter valve area, main transformer area, and auxiliary facility area). For the data points in each block, a local triangulation algorithm is used to construct a local triangulation network. Then, by matching and stitching the data points at the edge of the blocks, a basic triangulation network model covering the entire converter station area is formed, ensuring the spatial accuracy and structural integrity of the model. When using instantiation rendering technology, a unified template model is first created based on the basic parameters of the same type of model components. Then, the GPU batch processing capability is used to batch copy the instantiated objects, automatically matching the parameters of each instance. By installing coordinate and dimensional parameters and simultaneously assigning preset material textures and lighting effects, efficient batch rendering of similar components such as converter valve assemblies and transformer bushings is achieved, avoiding efficiency losses caused by repetitive modeling. In the integration and construction phase, the spatial position of the basic triangular mesh model of the full-domain 3D scene, the rendered similar components, and the associated data of the equipment components in the interactive target data (such as component fault records, loss status, and real-time operating parameters) are accurately bound through the unique equipment code. This allows model components to be associated with and query the full life cycle data of the corresponding components. At the same time, real-time meteorological data such as temperature, humidity, wind speed, and salt spray level are accessed through the real-time data interface, and the meteorological distribution is dynamically mapped in the model according to spatial coordinates, such as using color gradients to mark the temperature of different areas and using arrows to indicate wind speed and direction. The final generated 3D visualization scene model of the converter station realizes the integrated fusion of spatial structure, equipment components, and multi-source data, and supports interactive operations such as scene zooming, roaming, and component click query data. This model building process, through automated block modeling, batch rendering, and automatic data association, replaces the tedious operations of manual scene division, modeling, and data binding by maintenance personnel in the traditional model. The work of converter station scene modeling and data integration, which originally took several days or even weeks to complete, can now be completed efficiently in a short time. Maintenance personnel no longer need to search for data across platforms or manually match equipment and parameters. They can intuitively obtain information such as equipment spatial location, real-time status, historical faults, and surrounding meteorological environment through the visual model. This visual modeling process completely overcomes the time-consuming problems of operation and maintenance data management and scene analysis caused by the lengthy modeling process and inefficient data association in the traditional model. It not only significantly reduces the ineffective working time of maintenance personnel, but also builds an intuitive and integrated visual support base, providing efficient and accurate technical support for operation and maintenance decision-making, rapid fault location, and daily inspection.

[0127] For comparison of technical effects, existing technologies can be used as a reference. With the development of energy and power digitalization, the power equipment operating in DC converter stations is equipped with a large number of sensing systems for multi-dimensional monitoring of equipment status information. Most of the equipment sensors use high-frequency, high-speed acquisition, characterized by high signal frequency and large data acquisition volume, mostly megahertz data. At the same time, high-definition cameras, infrared sensors, power meteorological sensors, etc., are also widely deployed in converter stations, resulting in large-scale data access, including images, single-point measurement values, array values, etc. Especially considering the data from high-definition equipment, its transmission and visualization lead to a large amount of multi-source heterogeneous data, making data management within the converter station complex and consuming a lot of hardware resources for data resource management. In addition, to strengthen the data control of converter stations and facilitate the use of the converter station's digital platform system by operators during data reading, management, and operation, converter stations are often equipped with high-fidelity full-site scene models, including a 3D geometric model of the entire converter station valve hall, a 3D geometric model of the building, an external model of the equipment, a model of the internal components of the equipment, and a model of the surrounding environment, etc., which also presents the problem of complex data resource management for equipment, design, and operation.

[0128] Currently, there is a lack of systematic architecture design and management methods for the data and 3D resources of offshore converter platforms. Data retrieval, storage, and application are mostly handled through unified data interfaces, using a fixed format for stacked storage and retrieval, without categorized or hierarchical data control. This results in poor usability for station operators using the digital converter station platform. Due to the large number of devices, intuitive access to device data is complex, and some data involves confidential information. Directly searching documents in databases is not feasible; data mining within documents is required, making rapid and targeted data retrieval difficult. Existing technologies often directly use database integration without establishing a link between 3D resources and physical data management, leading to complex data retrieval, insufficient information mining, and time-consuming and labor-intensive data processing for operators, thus hindering the development and improvement of new productivity.

[0129] Based on the above, the shortcomings of existing technical solutions have severely hampered the operation and maintenance efficiency and digital transformation process of offshore converter platforms: First, they fail to adopt compatible transmission and interaction protocols for high-frequency and low-frequency data. High-frequency equipment monitoring data suffers from insufficient real-time performance due to transmission link congestion, while low-frequency ledger and drawing data suffer from resource waste due to redundant transmission methods, failing to meet the transmission needs of different types of data. Second, the binding of data and converter station equipment is limited to the overall equipment level, failing to achieve refined association of equipment components. Furthermore, the parameters of monitoring terminals such as infrared cameras are not accurately mapped to the physical location of the equipment, resulting in a misalignment between monitoring data and the actual equipment, making it difficult to support accurate equipment status assessment. Third, various types of data and related data are not integrated. The storage system suffers from several shortcomings. First, the lack of hierarchical separation between hot and cold data retrieval requires searching across multiple systems, which is not only time-consuming and labor-intensive but also poses a security risk of sensitive data leakage. Second, user data access lacks standardized permission and identity verification processes, making it difficult to balance the convenience and security of data retrieval. Third, the construction of 3D scene models still relies on traditional triangulation algorithms, resulting in high computational and storage requirements. Furthermore, efficient batch rendering technology is not used for similar components. Additionally, real-time data from models and equipment, as well as meteorological data, are fragmented, hindering dynamic and interconnected visual operation and maintenance. Ultimately, this leads to cumbersome and inefficient operations for maintenance personnel during data processing and scenario application, failing to unleash the empowering value of digital technology for new productivity.

[0130] To address the aforementioned issues, this invention proposes a data management method for offshore converter stations. Firstly, addressing the problem of insufficient data transmission adaptability, this invention employs the Kafka message queue protocol for low-latency transmission of high-frequency data and the REST API protocol for lightweight interaction of low-frequency data. This ensures the real-time nature of high-frequency equipment monitoring data to support immediate status awareness while avoiding the waste of transmission resources for low-frequency ledger and drawing data, accurately matching the transmission needs of different data types. Secondly, addressing the deficiency of coarse data binding, a hierarchical association system from the component level to the whole machine level is established through unique equipment and component codes. Combined with a spatial coordinate matching algorithm, precise mapping between infrared cameras and the physical location of equipment is achieved, completely solving the problem of misalignment between monitoring data and equipment entities, and providing data association support for accurate equipment status assessment. Thirdly, a hierarchical integrated storage repository of "hot data - warm data - cold data" is constructed, based on equipment... The system establishes a multi-dimensional correlation index with coding as the core, allowing operations and maintenance personnel to quickly retrieve data across multiple systems through a unified index. This significantly shortens data retrieval time and reduces the risk of sensitive data leakage through access control. Fourth, it establishes a standardized access process of "application filing - permission verification - token authentication," which automates access requests while standardizing permission and identity verification, balancing data retrieval convenience and security, and reducing the cost of manual review. Fifth, it optimizes triangulation modeling efficiency using a grid-based block construction algorithm, combines GPU-driven instantiation rendering technology to achieve batch rendering of similar components, and deeply integrates 3D scene models with equipment component association data and real-time meteorological data to construct a dynamically linked visual scene. Operations and maintenance personnel can intuitively obtain equipment spatial location, real-time status, historical data, and environmental parameters through scene interaction without manually integrating multi-source information.

[0131] In this embodiment of the invention, a data management method for offshore converter stations is provided. The method acquires high-frequency and low-frequency data from the converter station equipment and corresponding real-time meteorological data. A data transmission interaction protocol is used to transmit the high-frequency and low-frequency data, outputting high-frequency real-time data and low-frequency application data. The high-frequency real-time data and low-frequency application data are bound to the converter station equipment to determine the associated data of converter station equipment components, the associated data of converter station equipment, and the associated data of converter station equipment location. An integrated storage repository is constructed based on the associated data of converter station equipment components, the associated data of converter station equipment, the associated data of converter station equipment location, the high-frequency data, and the low-frequency data. When a user initiates a data access request, interactive target data is extracted from the integrated storage repository based on the user's data access request. A three-dimensional visualization scene model of the converter station is constructed using a grid-based block construction algorithm and instantiation rendering technology based on the interactive target data and real-time meteorological data. Based on the above scheme, this invention combines a grid-based block construction algorithm and instantiation rendering technology to transform the original global exponential operation into multiple lightweight local operations, and avoids the repetitive operation of traditional one-by-one rendering, further compressing the rendering time of model components, thereby significantly reducing the time cost for operation and maintenance personnel in data management.

[0132] Please see Figure 5 , Figure 5 This is a structural block diagram of a data management system for an offshore converter station provided in Embodiment 2 of the present invention.

[0133] This invention provides a data management system for offshore converter stations, comprising:

[0134] The acquisition module 501 is used to acquire high-frequency data and low-frequency data and corresponding real-time meteorological data of the converter station equipment, and to transmit the high-frequency data and low-frequency data using a data transmission interaction protocol, and output high-frequency real-time data and low-frequency application data.

[0135] The binding module 502 is used to bind high-frequency real-time data with low-frequency application data and converter station equipment, and to determine the associated data of converter station equipment components, the associated data of converter station equipment, and the associated data of converter station equipment location;

[0136] The database construction module 503 is used to build an integrated storage repository based on converter station equipment component association data, converter station equipment association data, converter station equipment location association data, high-frequency data, and low-frequency data;

[0137] The receiving module 504 is used to extract interactive target data from the integrated repository based on the user's data access request when it receives a data access request initiated by the user.

[0138] The model building module 505 is used to construct a 3D visualization scene model of the converter station based on interactive target data and real-time meteorological data using a grid-based block construction algorithm and instantiation rendering technology.

[0139] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0140] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the offshore converter station data management method as described in the above embodiments.

[0141] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the offshore converter station data management method as described in the above embodiments.

[0142] This invention also provides a computer program product, including a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the offshore converter station data management method as described in the above embodiments.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data management method for an offshore converter station, characterized in that, include: The system acquires high-frequency and low-frequency data and corresponding real-time meteorological data from the converter station equipment, and transmits the high-frequency and low-frequency data using a data transmission interaction protocol, outputting high-frequency real-time data and low-frequency application data. The high-frequency real-time data is bound to the low-frequency application data and the converter station equipment to determine the converter station equipment component association data, converter station equipment association data, and converter station equipment location association data; An integrated storage repository is constructed based on the converter station equipment component association data, the converter station equipment association data, the converter station equipment location association data, the high-frequency data, and the low-frequency data; When a user initiates a data access request, interactive target data is extracted from the integrated repository based on the user's data access request. A grid-based block construction algorithm and instantiation rendering technology are used to construct a three-dimensional visualization scene model of the converter station based on the interactive target data and the real-time meteorological data.

2. The data management method for offshore converter stations according to claim 1, characterized in that, The data transmission interaction protocol includes the Kafka message queue protocol and the expressive state transfer application programming interface protocol. The process of transmitting the high-frequency data and the low-frequency data using a data transmission interaction protocol, and outputting high-frequency real-time data and low-frequency application data, includes: The high-frequency data is processed using the Kafka message queue protocol for low-latency, high-throughput transmission to obtain high-frequency real-time data. The low-frequency data is processed using a descriptive state transfer application programming interface protocol to achieve lightweight transmission, thereby obtaining low-frequency application data.

3. The data management method for offshore converter stations according to claim 1, characterized in that, The step of binding the high-frequency real-time data with the low-frequency application data and the converter station equipment to determine the converter station equipment component association data, converter station equipment association data, and converter station equipment location association data includes: Based on the low-frequency application data, the equipment component ledger data, equipment component fault record data and equipment component loss data corresponding to the equipment components in the converter station equipment are extracted, and the equipment component ledger data, equipment component fault record data and equipment component loss data are bound to the corresponding equipment group components, and the converter station equipment component association data is output. Extract the equipment operation data and equipment environment data corresponding to the converter station equipment from the low-frequency application data, and bind the equipment operation data, the equipment environment data and the high-frequency real-time data with the converter station equipment to output the converter station equipment association data; Based on the physical location coordinates of the converter station equipment, a mapping relationship between the infrared camera and the converter station equipment is established, generating converter station equipment location association data.

4. The data management method for offshore converter stations according to claim 1, characterized in that, The step of constructing an integrated repository based on the converter station equipment component association data, the converter station equipment association data, the converter station equipment location association data, the high-frequency data, and the low-frequency data includes: The low-frequency data is stored in a relational database format to obtain low-frequency data in relational database format; The high-frequency data is stored in file format to obtain high-frequency data in file format. The converter station equipment component association data, the converter station equipment location association data, and the high-frequency data in file format are hot-stored to obtain hot-stored data; The converter station equipment association data and the low-frequency data in the relational database format are cold-stored to obtain cold-stored data. Build an integrated repository based on hot-stored data and cold-stored data.

5. The data management method for offshore converter stations according to claim 1, characterized in that, When a user initiates a data access request, the step of extracting interactive target data from the integrated repository based on the user-initiated data access request includes: When a data access request initiated by the user is received, it is determined whether the data access request initiated by the user meets the preset application conditions; If the conditions are met, then the user's data access request will be subject to permission verification. If the verification is successful, an access token is generated and added to the data access request initiated by the user for identity verification. If the identity verification is successful, the device component association data, high-frequency data in file format, and low-frequency data in relational database format are extracted from the integrated repository. The data interaction is carried out using a differentiated data adaptation and interaction method, and the interactive target data is output.

6. The data management method for offshore converter stations according to claim 1, characterized in that, The algorithm based on grid-based segmentation and instantiation rendering technology constructs a 3D visualization scene model of the converter station based on the interactive target data and the real-time meteorological data, including: Extract the basic parameters of the overall 3D scattered data points of the converter station scene and the basic parameters of the same type of model components from the interactive target data; The algorithm based on grid block construction is used to construct a model of the 3D scattered data points of the overall scene of the converter station, and outputs a basic triangular network model of the global 3D scene. The instantiation rendering technology is used to draw in batches based on the basic parameters of the same type of model components, and the rendered same type of components are output. The model is constructed based on the triangular mesh model of the global 3D scene, the rendered components of the same type, and the associated data of the device components in the interactive target data. The real-time meteorological data is then embedded to generate a 3D visualization scene model of the converter station.

7. A data management system for an offshore converter station, characterized in that, include: The acquisition module is used to acquire high-frequency data and low-frequency data and corresponding real-time meteorological data of the converter station equipment, and to transmit the high-frequency data and the low-frequency data using a data transmission interaction protocol, and output high-frequency real-time data and low-frequency application data. The binding module is used to bind the high-frequency real-time data with the low-frequency application data and the converter station equipment, and to determine the converter station equipment component association data, converter station equipment association data and converter station equipment location association data; The database construction module is used to construct an integrated storage repository based on the converter station equipment component association data, the converter station equipment association data, the converter station equipment location association data, the high-frequency data, and the low-frequency data; The receiving module is used to extract interactive target data from the integrated repository based on the user's data access request when it receives the user's data access request. The model building module is used to construct a three-dimensional visualization scene model of the converter station based on the interactive target data and the real-time meteorological data, using a grid-based block construction algorithm and instantiation rendering technology.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the offshore converter station data management method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the data management method for offshore converter stations as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the offshore converter station data management method as described in any one of claims 1-6.

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