A digital submount system

Abstract

The present disclosure provides a digital base system applied to a source network and cargo integrated platform, comprising: a data access and standardization module for obtaining original operation data of a plurality of business objects in the source network and cargo integrated platform and forming a unified record containing a unified identifier; an object and topology modeling module for constructing a unified object model and a topology relationship between the business objects based on the unified record, and binding each topology node in the topology relationship with the unified identifier; a lake warehouse integration and index module comprising a plurality of databases for constructing a unified index of the plurality of databases; and an algorithm and application interface module comprising a unified interface for determining a result of a request carrying the unified index obtained based on the unified interface, and writing the result back to the lake warehouse integration and index module and returning to the source network and cargo integrated platform. The present disclosure realizes data flow and function linkage of each module through the unified identifier, and supports the platform to efficiently obtain reliable business results.

Description

Technical Field

[0001] This disclosure relates to the field of power system data management technology, specifically to a digital base system. Background Technology

[0002] The integrated power generation, grid, load and energy storage platform is an important dispatch and management carrier that meets the development needs of new power systems. It consists of a power source system, a grid system, a load system and an energy storage system. It achieves power supply and demand balance and efficient utilization through the collaboration of multiple systems.

[0003] However, existing technologies typically require the construction of separate digital bases or data middleware for each business system within the integrated source-grid-load-storage platform. Due to the complexity of cross-system data interoperability, this results in poor platform scheduling performance and unreliable business outcomes. Summary of the Invention

[0004] This disclosure addresses the problems existing in the prior art by providing a digital foundation system that can solve the problems of poor platform scheduling and unreliable business results caused by the complexity of cross-system data interoperability in the prior art.

[0005] To achieve the above objectives, the technical solution adopted in this disclosure is as follows:

[0006] This disclosure provides a digital infrastructure system applied to an integrated source-grid-load-storage platform, comprising: a data access and standardization module for acquiring raw operational data of multiple business objects in the integrated source-grid-load-storage platform and forming unified records; the unified records include unified identifiers; an object and topology modeling module for constructing a unified object model and topological relationships between various business objects in the unified object model based on the unified records; and binding each topological node in the topological relationship to a unified identifier; the unified identifier includes at least one of a unique device identifier and a unique measurement point identifier; a lake-warehouse integration and indexing module, comprising multiple databases for constructing a unified index for the multiple databases, the unified index including a unified identifier; the multiple databases include a time-series database storing unified records and a graph database storing topological relationships; and an algorithm and application interface module, comprising a unified interface for obtaining requests carrying the unified index from the integrated source-grid-load-storage platform based on the unified interface, determining the result of the request; and writing the result back to the lake-warehouse integration and indexing module and returning it to the integrated source-grid-load-storage platform.

[0007] In some embodiments of this disclosure, the digital base system further includes: a feature service module, including a feature operator library, for generating and returning corresponding feature data based on the unified identifier in the unified index and the feature operator library; an algorithm and application interface module, further used to call the feature service module; and an algorithm result for determining the request based on the feature data returned by the feature service module.

[0008] In some embodiments of this disclosure, the algorithm and application interface module further includes a registered algorithm service; when the registered algorithm service is triggered, it is used to determine the algorithm result of the request based on the feature data returned by the feature service module.

[0009] In some embodiments of this disclosure, the feature service module further includes a feature service interface; the feature service module returns feature data through the feature service interface; and the algorithm and application interface module calls the feature service module through the feature service interface.

[0010] In some embodiments of this disclosure, the digital base system further includes: a metadata and data lineage management module, used to record the full-link lineage of data corresponding to each business object and generate corresponding lineage association records; transmitting the lineage association records to the lake warehouse integration and index module for storage; the full-link lineage includes data access and data standardization of the data access and standardization module, data storage of the lake warehouse integration and index module, feature processing of the feature service module, and algorithm processing and data write-back of the algorithm and application interface module; a data quality and tag management module, used to perform quality verification on the unified records of each business object based on preset general rules and electrical constraints, and generate quality tags; and write-back the quality tags to the unified records of the lake warehouse integration and index module.

[0011] In some embodiments of this disclosure, the feature service module is further configured to determine the corresponding target business object based on the unified identifier in the unified index, and pull relevant data of the target business object from the corresponding database of the lake warehouse integration and index module; call the corresponding operator in the feature operator library to generate feature data of the target business object based on the relevant data of the target business object; and return the feature data to the algorithm and application interface module.

[0012] In some embodiments of this disclosure, the data access and standardization module includes a protocol adaptation and data access module and a data standardization and unified encoding module. The protocol adaptation and data access module is used to interface with multiple protocols of multiple business objects to obtain the original operating data of multiple business objects, and after completing protocol decoding, encapsulate the original operating data into access layer standard records; and transmit the access layer standard records to the lake warehouse integration and index module for storage. The access layer standard records include timestamps, original device identifiers, original measurement point identifiers, original values, original quality codes, source channels, and protocol names. The data standardization and unified encoding module is used to map the original device identifiers and original measurement point identifiers of the access layer standard records to unique device identifiers and unique measurement point identifiers respectively through a unified encoding system to form unified records; and write the unified records back to the lake warehouse integration and index module. The unified records include unified timestamps, unique device identifiers, unique measurement point identifiers, standardized values, quality identifiers, and source identifiers.

[0013] In some embodiments of this disclosure, the digital base system further includes: a security audit and multi-tenant operation and maintenance module, used to log and audit the interface call operations, data query operations and result write-back operations associated with each business object; and to monitor the operating status of each module and issue anomaly alarms.

[0014] In some embodiments of this disclosure, the lake-warehouse integration and indexing module is also used to perform write operations on any database and synchronously update the unified index; the multiple databases also include a relational database that stores static attribute data of each business object and an object storage database that stores log documents of each business object; the unified index also includes topology node identifiers, business tags and time windows to allow the source-grid-load-storage integrated platform to query data according to device, region, scenario or time.

[0015] In some embodiments of this disclosure, multiple business objects include power supply units, grid equipment, load units, and energy storage units; power supply units include at least one of photovoltaic inverters, wind turbine generators, and coal-fired generators; grid equipment includes at least one of transmission lines, distribution feeders, and switchgear; load units include at least one of data center servers, industrial motors, and household appliances; and energy storage units include at least one of electrochemical energy storage battery packs, energy storage converters, and pumped storage power stations.

[0016] Compared with the prior art, this disclosure has the following beneficial effects:

[0017] The digital base system provided in this disclosure achieves data access and data unification through a data access and standardization module, realizes the semantic association of "any measurement point can be traced to the device and topology location" through an object and topology modeling module, shields the upper layer from the differences in the underlying storage through the lake-warehouse integration and index module, realizes consistent access according to a unified index, and realizes unified closed-loop management of "sensing-computing-decision-execution-feedback" in the source-network-load-storage business through an algorithm and application interface module. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a digital docking station system provided in Embodiment 1 of this disclosure;

[0019] Figure 2 This is a schematic diagram of a unified object model and topological relationship provided in Embodiment 1 of this disclosure;

[0020] Figure 3 This is a schematic diagram of a digital docking station system provided in Embodiment 2 of this disclosure;

[0021] Figure 4 This is a schematic diagram of a digital docking station system provided in Embodiment 3 of this disclosure;

[0022] Figure 5 This is a schematic diagram of a digital docking station system provided in Embodiment 4 of this disclosure;

[0023] Figure 6 This is a schematic diagram of a protocol access process provided in Embodiment 4 of this disclosure;

[0024] Figure 7 This is a schematic diagram of the overall architecture of a digital docking station system provided in an embodiment of this disclosure;

[0025] Figure 8 This is a schematic diagram of a lake warehouse integration and feature service closed loop provided by an embodiment of this disclosure. Detailed Implementation

[0026] The present disclosure will now be further described with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present disclosure and should not be construed as limiting the scope of protection of the present disclosure. It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application.

[0027] The acquisition, transmission, storage, use, and processing of data in this disclosed technical solution comply with relevant national laws and regulations. In the embodiments of this disclosure, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this disclosure, and do not imply that the applicant has already used or necessarily used such solutions.

[0028] All terms used in this disclosure have the same meaning as understood by one of ordinary skill in the art to which this disclosure pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as idealized or highly formalized, unless expressly defined herein.

[0029] With the development of high-proportion renewable energy access, multi-energy complementarity, integrated generation, grid, load and storage, and virtual power plants, the power system faces problems on the data and application side, such as difficulty in associating multi-source heterogeneous data, information silos and semantic fragmentation, redundant construction of intelligent applications, and lack of closed-loop and traceability mechanisms.

[0030] While existing technologies include solutions such as "digital base system", "source-grid-load-storage platform" and "data middleware", they mostly focus on infrastructure zoning, security isolation, 3D display or general data integration, and cannot solve the above problems.

[0031] Based on this, Embodiment 1 of this disclosure provides a digital base system applied to an integrated power generation, grid, load, and storage platform, such as... Figure 1As shown, it includes a data access and standardization module 1, an object and topology modeling module 2, a lake warehouse integration and indexing module 3, and an algorithm and application interface module 4.

[0032] The data access and standardization module in this embodiment is used to obtain the original operating data of multiple business objects in the integrated source-grid-load-storage platform and form a unified record; the unified record includes a unified identifier.

[0033] In one possible implementation, the unified record is as follows: Where t is the unified timestamp, DevID is the unique identifier of the device, PointID is the unique identifier of the measurement point, Val is the standardized value, Q is the quality identifier (also known as the quality label or tag), and Src is the source identifier.

[0034] In some embodiments of this disclosure, multiple business objects include power supply units, grid equipment, load units, and energy storage units. The power supply unit may include at least one of the following: a photovoltaic inverter, a wind turbine generator, or a coal-fired power generator. The grid equipment may include at least one of the following: transmission lines, distribution feeders, or switchgear. The load unit may include at least one of the following: a data center server, an industrial motor, or a household appliance. The energy storage unit includes at least one of the following: an electrochemical energy storage battery pack, an energy storage converter, or a pumped-storage hydroelectric power station.

[0035] The object and topology modeling module in this embodiment is used to construct a unified object model and the topological relationships between various business objects in the unified object model based on a unified record; and to bind each topology node in the topology relationship to a unified identifier; the unified identifier includes at least one of a unique device identifier and a unique measurement point identifier.

[0036] In one possible implementation, a unified object model for source-grid-load-storage is constructed at the levels of region or platform, station or park, power unit, line and feeder, load unit, and energy storage unit. Topological relationships between these objects are established, including node-branch relationships, operating modes, and energy flow directions. Unique device identifiers (DevID) and unique measurement point identifiers (PointID) are bound to the corresponding objects and topological nodes, enabling bidirectional queries from measurement points to service objects. In a specific implementation, the constructed unified object model and topological relationships are as follows: Figure 2 As shown, hierarchical objects and measurement points are bound to labels, nodes-branches, and energy flow directions.

[0037] In one possible implementation, the unified identifier includes the device unique identifier DevID and the measurement point unique identifier PointID.

[0038] It should be noted that the purpose of object and topology modeling is to map power supply units, grid equipment, load units, energy storage units and their measurement points to a unified object model and electrical / energy flow topology, so as to achieve the semantic association that "any measurement point can be traced to the equipment and topology location", providing basic support for cross-system aggregation and topology analysis.

[0039] The lake-warehouse integration and indexing module in this embodiment includes multiple databases for constructing a unified index for the multiple databases. The unified index includes a unified identifier. The multiple databases include a time-series database that stores unified records and a graph database that stores topological relationships.

[0040] In some embodiments of this disclosure, the multiple databases also include a relational database that stores static attribute data of each business object and an object storage database that stores log documents of each business object.

[0041] In some embodiments of this disclosure, the lake warehouse integration and indexing module is also used to perform write operations on any database and synchronously update the unified index. In one possible implementation, runtime data is written to a time-series database, business configuration and asset information are written to a relational database, documents and logs are written to an object storage database, and topology relationships are written to a graph database.

[0042] In some embodiments of this disclosure, the unified index further includes topology node identifiers, service tags, and time windows to allow the integrated source-grid-load-storage platform to query data by device, region, scenario, or time. The topology node identifier is used to locate the position of the target service object in the topology relationship, the service tag is used to label the type attribute of the target service object, and the time window is used to limit the time range of the data.

[0043] In one possible implementation, a unified index is established based on the device unique identifier DevID, the measurement point unique identifier PointID, the topology node identifier, and the service tag.

[0044] In some embodiments of this disclosure, the lake warehouse integration and indexing module is also used to construct a semantic logical view, so that when the upper layer queries by "device / region / scene / time", the base automatically decomposes it into joint access to multiple storage engines and aggregates the results.

[0045] It should be noted that in some of the above embodiments, multiple engines such as time-series databases, relational databases, object storage databases and graph databases are used in collaboration. By using a unified index and logical view with objects and topology as primary keys, the differences in underlying storage are shielded from the upper layer. This enables consistent access based on device, region, scenario, or time dimension, which is different from the traditional data middle platform that is only organized by database tables.

[0046] The algorithm and application interface module in this embodiment includes a unified interface, used to determine the result of a request carrying a unified index sent by the source-grid-load-storage integrated platform based on the unified interface; and to write the result back to the lake-warehouse integration and index module and return it to the source-grid-load-storage integrated platform.

[0047] It should be noted that, in one possible implementation, the algorithm and application interface module uniformly obtains features and data from the base station, and writes back the predicted values, optimization schemes and control instructions to the base station, establishing a lineage relationship with the input data and model version, thereby realizing unified closed-loop management of "perception-computation-decision-execution-feedback" in the source-grid-load-storage business.

[0048] The above modules can communicate decoupled through a service bus or message middleware to form a distributed and scalable architecture.

[0049] The digital base system provided in Embodiment 1 of this disclosure can achieve standardized data processing, topological association and unified storage through the above modules, thereby efficiently outputting business results and solving the problems of data fragmentation, inefficient scheduling and unreliable results in the traditional multi-base mode.

[0050] Based on this, Embodiment 2 of this disclosure provides a digital base system applied to an integrated power generation, grid, load, and storage platform, such as... Figure 3 As shown, in addition to the data access and standardization module 1, object and topology modeling module 2, lake warehouse integration and indexing module 3, and algorithm and application interface module 4, it also includes a feature service module 5.

[0051] The feature service module in this embodiment includes a feature operator library, which is used to generate and return corresponding feature data based on the unified identifier in the unified index and the feature operator library.

[0052] In one possible implementation, window statistics, aggregation calculations, and scene feature extraction (such as feeder load rate, new energy output penetration rate, energy storage status indicators, etc.) are performed on the base data; a configurable feature operator library and feature view are formed and stored or cached in a time series database.

[0053] In some embodiments of this disclosure, the requested result may include an algorithm result. The algorithm and application interface module is further configured to invoke the feature service module and determine the requested algorithm result based on the feature data returned by the feature service module. In one possible implementation, the algorithm result may include a prediction result, an optimization strategy, or a control instruction. In another possible implementation, the requested result may also include a query result.

[0054] In some embodiments of this disclosure, the algorithm and application interface module may further include a registered algorithm service, which, when triggered, is used to determine the algorithm result of the request based on the feature data returned by the feature service module.

[0055] In one possible implementation, the unified interface supports the registration of algorithm services such as load forecasting, output forecasting, source-load coordination, energy storage optimization, and virtual power plant control in the underlying architecture.

[0056] In some embodiments of this disclosure, the feature service module may further include a feature service interface. The feature service module returns feature data through this feature service interface, and the algorithm and application interface module calls the feature service module through this feature service interface. By providing a feature service interface, applications or algorithms can directly obtain feature data through a unified identifier and time window, without the need to repeatedly develop data processing logic.

[0057] In one possible implementation, the algorithm service pulls data from the feature service module through the feature service interface, and the output prediction results, optimization strategies or control instructions are uniformly written back to the base.

[0058] In some embodiments of this disclosure, the feature service module is further configured to determine the corresponding target business object based on the unified identifier in the unified index, and pull relevant data of the target business object from the corresponding database of the lake warehouse integration and index module; call the corresponding operator in the feature operator library to generate feature data of the target business object based on the relevant data of the target business object; and return the feature data to the algorithm and application interface module.

[0059] It should be noted that in some of the above embodiments, by building a feature operator library and feature view inside the base, typical power business features (rolling load, output statistics, penetration rate, load factor, etc.) are standardized and encapsulated, and provided to various prediction and optimization algorithms through an online feature service interface, which can realize "data and features are processed once and reused in multiple applications".

[0060] The digital base system provided in Embodiment 2 of this disclosure adds a feature service module, which can extract and process features from standardized data. Combined with topological association data and a global index, this provides more accurate feature data support for the business results output by the algorithm and application interface modules. Compared to Embodiment 1, this module combination can further reduce the feature processing time of the algorithm and application interface modules and improve the accuracy of business results.

[0061] Based on this, Embodiment 3 of this disclosure provides a digital base system applied to an integrated power generation, grid, load, and storage platform, such as... Figure 4As shown, in addition to the data access and standardization module 1, object and topology modeling module 2, lake warehouse integration and indexing module 3, algorithm and application interface module 4, and feature service module 5, it also includes metadata and data lineage management module 6 and data quality and tag management module 7.

[0062] The metadata and data lineage management module in this embodiment is used to record the full-link lineage of data corresponding to each business object and generate corresponding lineage association records; the lineage association records are transmitted to the lake warehouse integration and index module for storage; the full-link lineage includes data access and data standardization of the data access and standardization module, data storage of the lake warehouse integration and index module, feature processing of the feature service module, and algorithm processing and data write-back of the algorithm and application interface module.

[0063] In one possible implementation, the data processing chain is recorded, from acquisition, standardization, and storage to feature calculation and algorithm invocation.

[0064] It should be noted that generating the corresponding lineage record refers to associating the feature view and algorithm output with the corresponding source data version, which supports subsequent tracking and auditing.

[0065] The data quality and tag management module in this embodiment is used to perform quality verification on the unified records of each business object based on preset general rules and electrical constraints, and generate quality tags; and write the quality tags back to the unified records of the lake warehouse integration and index module.

[0066] In one possible implementation, the data quality is assessed based on general rules such as range checks, required fields, and time continuity, as well as electrical constraints such as topology and power balance. The assessment results are appended to the records or time periods in the form of quality identifiers. An algorithm plugin interface is provided to allow external anomaly detection or repair modules to write back quality identifiers and suggested values. This disclosure does not limit the specific algorithm implementation.

[0067] The digital infrastructure system provided in Embodiment 3 of this disclosure adds a metadata and data lineage management module and a data quality and tag management module, further improving the collaborative system of "data governance-business support". The new modules can trace the source and flow of data throughout the entire chain, verify data quality and classify and label it, providing high-quality data for the feature service module; in collaboration with existing modules, it makes stored data more reliable and traceable, providing accurate support for business result output. Compared with Embodiment 2, it improves the credibility and interpretability of results, reduces the risk of scheduling decision deviations, and aligns with the data management and high-precision business needs of the source-network-load-storage platform.

[0068] Based on this, Embodiment 4 of this disclosure provides a digital base system applied to an integrated source-grid-load-storage platform, such as... Figure 5As shown, in addition to the data access and standardization module 1, object and topology modeling module 2, lake warehouse integration and indexing module 3, algorithm and application interface module 4, feature service module 5, metadata and data lineage management module 6, and data quality and tag management module 7, it also includes a security audit and multi-tenant operation and maintenance module 8. The data access and standardization module can specifically include a protocol adaptation and data access module 11 and a data standardization and unified coding module 12.

[0069] The protocol adaptation and data access module in this embodiment is used to interface with multiple protocols of multiple business objects to obtain the original operation data of multiple business objects, and after completing the protocol decoding, encapsulate the original operation data into access layer standard records; and transmit the access layer standard records to the lake warehouse integration and index module for storage.

[0070] In one possible implementation, various protocols include the Modbus protocol in the field of industrial serial communication, the IEC 60870-5-104 standard protocol in the field of power telecontrol, the IEC 61850 standard protocol in the field of power system automation, the OPC UA (OPC Unified Architecture) protocol in the field of cross-platform interaction of industrial data, the MQTT (Message Queuing Telemetry Transport) protocol in the field of lightweight transmission of the Internet of Things, and custom protocols.

[0071] In some embodiments of this disclosure, the access layer standard record includes a timestamp, original device identifier, original measurement point identifier, original value, original quality code, source channel, and protocol name.

[0072] In one possible implementation, the access layer standard is recorded as follows:

[0073] Where t is the timestamp, RawDev is the original device identifier, RawPoint is the original measurement point identifier, RawVal is the raw value, RawQ is the raw quality code, Src is the source channel, and Proto is the protocol name.

[0074] The data standardization and unified coding module in this embodiment is used to map the original device identifier and the original measurement point identifier of the access layer standard record to the unique device identifier and the unique measurement point identifier respectively through a unified coding system, forming a unified record; and to write the unified record back to the lake warehouse integration and indexing module.

[0075] In some embodiments of this disclosure, the unified record includes a unified timestamp, a unique device identifier, a unique measurement point identifier, a standardized numerical value, a quality identifier, and a source identifier.

[0076] In one possible implementation, the unified record is as follows: Where t is the unified timestamp, DevID is the unique identifier of the device, PointID is the unique identifier of the measurement point, Val is the standardized value, Q is the quality identifier, and Src is the source identifier.

[0077] In one specific implementation, the protocol access process is as follows: Figure 6 As shown, the protocol access process includes: device and external platform, multi-protocol adapter, acquisition control and buffering, Unified packaging, data standardization and unified coding Unified recording and access to the bus or message channel.

[0078] The security audit and multi-tenant operation and maintenance module in this embodiment is used to log and audit the interface call operations, data query operations and result write-back operations associated with each business object; and to monitor the running status of each module and issue anomaly alarms.

[0079] In one possible implementation, the functions of the security audit and multi-tenant operation and maintenance module include: adopting tenant-level logical isolation and access control for different projects, parks or virtual power plants; performing security audit and logging on interface calls, data queries and result write-back; and monitoring and alarming the operating status of each module to ensure the stable operation of the base.

[0080] The digital infrastructure system provided in Embodiment 4 of this disclosure adds a security audit and multi-tenant operation and maintenance module, and further subdivides the data access and standardization module into protocol adaptation and data access, and data standardization and unified encoding modules, forming a more comprehensive collaborative system. The new module enables security auditing and multi-tenant isolated operation and maintenance, and collaborates with existing modules to enhance the accuracy and security of data access while meeting the operation and maintenance needs of multiple stakeholders. Compared to Embodiment 3, this further improves system security and operational flexibility.

[0081] The following combination Figure 7 The overall architecture of the digital base system provided in the embodiments of this disclosure will be described.

[0082] like Figure 7As shown, the overall architecture process starts with meteorological data sources from the source, grid, load, and storage systems. Multi-source heterogeneous data first enters the protocol adaptation and data access stage. Then, after data standardization and unified encoding, associations are built through unified object models and topology modeling. Subsequently, the multi-source heterogeneous data is stored in the lake-warehouse integrated time-series database, relational database, object storage, and graph database, respectively. Then, it is processed by semantic unified indexing and logical views (using DevID, PointID, TopoNode, scene, and time window as keys), and then output to the outside through the real-time feature service API (Application Programming Interface). The output data can flow to the algorithm service for prediction and optimization, or flow to the business system scheduling, energy management, and virtual power plants for scheduling. The algorithm results and scheduling results are all summarized in the result write-back and lineage association stage to perform write-back and lineage association operations. At the same time, the security audit and multi-tenant operation and maintenance modules cover and control the protocol adaptation and data access, lake-warehouse integrated storage, and real-time feature service API stages.

[0083] The following combination Figure 8 The lake-warehouse integration and feature service closed-loop process in the embodiments of this disclosure are described.

[0084] like Figure 8 As shown, the data is stored in multiple types, namely, different types of data are stored in time series databases, relational databases, object storage, and graph databases respectively; then it is processed by a unified index and logical view (categorized by device, region, scene, and time), and then output to the outside world through real-time computing and feature service API (including feature operator library and feature view); the output data flows to algorithm service prediction, optimization, control and business application virtual power plant, scheduling, and energy management respectively, and the results of both are summarized to the result write-back stage (recording prediction values, optimization schemes, and control instructions); the data of the final result write-back is associated with metadata and data lineage, and the multi-type storage also forms a closed loop linkage with metadata and data lineage.

[0085] The following provides a detailed description of the construction method of the digital base system provided in the embodiments of this disclosure, which specifically includes the following steps S1 to S9.

[0086] Step S1: Protocol adaptation and data access;

[0087] Interacts with Modbus, IEC 60870-5-104, IEC 61850, OPC UA, MQTT, and custom protocols; completes protocol decoding and minimal unified encapsulation, generating access layer standard records.

[0088] ,

[0089] Where t is the timestamp (unified to the same time zone and precision), RawDev is the device identifier in the original system (unstandardized), RawPoint is the identifier of the measurement point / register / IOA in the original system (unstandardized), RawVal is the raw value (without unit / proportional conversion), RawQ is the raw quality code / status (such as QDS in IEC104), Src is the source channel / system name (such as SCADA_IEC104, GATEWAY_MODBUS), and Proto is the protocol name (such as IEC104, Modbus, OPCUA, MQTT).

[0090] Step S2: Data standardization and unified coding;

[0091] Align units / dimensions, naming conventions, and sampling step sizes; establish a unified coding system to map RawDev / RawPoint to DevID / PointID; and create a unified base record.

[0092] ,

[0093] Where t is the unified timestamp; DevID is the unique identifier of the device; PointID is the unique identifier of the measurement point; Val is the standardized data value; Q is the quality identifier; and Src is the source identifier.

[0094] It should be noted that 't' refers to the unified time when the measurement data was generated or entered into the database, using a unified time zone and precision (e.g., milliseconds or seconds) as the time base for all data alignment and window calculations. DevID refers to a globally unique code assigned to each physical or logical device within the digital base, masking differences in original numbers across different plants and systems, used to identify which device / unit the data originated from, such as a specific inverter, switch, transformer, or BMS cabinet. PointID refers to the unified code for a specific measurement point or status point under the corresponding device, such as "phase A current," "active power," "SOC," or "switch position," used to uniquely locate a specific measurement quantity within the base. Val refers to the actual value corresponding to the device and measurement point at that timestamp, after necessary unit, precision, and format standardization (e.g., unified to kW, kV, A, etc.). Q refers to the quality code for the validity of the data value, which may include whether the acquisition was successful, whether it exceeded limits, whether interpolation was used, whether missing data was filled, or whether it was marked as suspicious by rules or algorithms, used for subsequent calculations and algorithmic data selection based on quality. Src refers to the system or channel from which the data originally came, such as "SCADA", "Park Energy Management", "Metering System", "Gateway Number", etc., used for tracing and comparison with multiple systems.

[0095] The following is an example in R:

[0096] R (Modbus inverter active power) = (DevID="PV-ARRAY-01:INV-07", PointID="P_ac_kW", Val=1225.3, Q="valid", Src="GATEWAY_MODBUS").

[0097] R(MQTT BMS SOC) = (DevID="ESS-01:BMS", PointID="SOC_pct", Val=67.8, Q="valid", Src="MQTT_BMS");

[0098] R (OPC UA PCS grid-connected power) = (DevID="ESS-01:PCS-03", PointID="P_grid_kW", Val=-350.0, Q="valid", Src="OPCUA");

[0099] R (IEC104 switch position) = (DevID="STN-0001:Breaker-21", PointID="SW_pos", Val=1, Q="valid", Src="SCADA_IEC104").

[0100] The following is a typical mapping example of raw data → R_raw → R:

[0101] The raw data acquired by the IEC104 voltage protocol includes: asdu=13, ioa=10001, val=231.4, qds=0x00;

[0102] The mapped R_raw includes: RawDev="STN-0001", RawPoint="IOA:10001", RawVal=231.4, RawQ=0x00, Src="SCADA_IEC104";

[0103] The mapped R includes: DevID="STN-0001:Feeder-01", PointID="Uab_kV", Val=0.2314, Q="valid", Src="SCADA_IEC104".

[0104] Step S3: Unify object model and topology modeling;

[0105] Establish object models at levels such as region, station / park, power unit, line / feeder, load unit, and energy storage unit; construct topological relationships such as node-branch, operation mode, and energy flow direction; bind DevID / PointID to objects and topological nodes to achieve bidirectional traceability from measurement points to business objects.

[0106] Step S4: Lakewarehousing integration and semantic unified indexing;

[0107] High-frequency operational data is written to TSDB (Time Series Database); assets / configurations are written to RDB (Relational Database); documents / logs are written to object storage; topological relationships are written to GDB (Graph Database); and semantic unified indexes are built on top of these.

[0108] Index key set: {DevID, PointID, TopoNodeID, BizTag, TimeRange}

[0109] TopoNodeID (Topology Node Identifier) ​​refers to the node ID in the electrical / energy topology diagram, such as "busbar B1", "feeder-12", "low-voltage side node of transformer TR-03", etc. Its function is to serve as the primary key when aggregating or tracing topology data (e.g., "give me the load rate of all equipment under this feeder at a certain time period").

[0110] BizTag (business tag) is an extensible tag oriented towards business dimensions, used for quick filtering or aggregation. Typical tags include: region, station / park, professional domain (source / network / load / storage), voltage level, asset category, scenario (supply guarantee / maintenance / summer season), tenant ID, etc. Function: Allows for combined queries based on business criteria without modifying the physical database table structure.

[0111] TimeRange (time window) is a unified time interval [start, end] (including step size / alignment granularity information) that serves as a time constraint for all time-series accesses and window feature calculations. Its function is to drive time filtering, window aggregation, and alignment and merging in the TSDB view layer.

[0112] Routing and Aggregation: Query keys are parsed into sub-plans (e.g., DevID + time window → TSDB; TopoNode → GDB; asset attributes → RDB), and the logical view layer is aligned and aggregated according to time / object dimensions and returned; a single view or unified API is presented above.

[0113] Pseudo-SQL example (querying the overall load ratio and PV penetration rate of a feeder within a time window): SELECT t, load_ratio, pv_penetration FROM VIEW_FEEDER_FEATURES WHERE topo_node_id = 'Feeder-12'

[0114] The query query, `AND t BETWEEN '2025-11-21T00:00:00Z' AND '2025-11-21T06:00:00Z'`, retrieves data from the view named `VIEW_FEEDER_FEATURES`. The filtering criteria are: the topology node ID equals 'Feeder-12', and the time field `t` falls within the range of 00:00:00 (UTC time) to 06:00:00 (UTC time) on November 21, 2025. The returned fields include the time field `t`, the load ratio field `load_ratio`, and the photovoltaic penetration rate field `pv_penetration`.

[0115] Step S5: Metadata and Data Lineage Management;

[0116] Record the entire lineage from data collection to standardization, storage, features, algorithms, and write-back; bind the input data range / version, feature operator version, model version, and output result into a ternary structure to support traceability and auditing.

[0117] Step S6: Data Quality and Tag Management;

[0118] Based on general rules such as range / jump / time continuity and electrical constraints such as topology consistency, power balance, and energy conservation of storage SOC, quality labels (such as valid, suspect, invalid, interpolated_ok, etc.) are generated; algorithm plugins are provided to accept label / suggested values ​​from external anomaly detection / repair algorithms (this disclosure does not limit the algorithm implementation); the labels are bound to R records or time periods and affect the filtering / weighting strategies for feature calculation.

[0119] Step S7: Real-time computation and feature services;

[0120] Built-in feature operator library (rolling average / extreme value, load factor, penetration rate, topology aggregation metrics, etc.) and feature view (cacheable), providing an online feature service API (unified interface):

[0121] Request key: Object identifier (DevID / TopoNodeID / Business object ID), feature name or operator ID, time window / step size, quality filter, as_of time point, feature version, etc.;

[0122] Returns: Feature sequence or time point vector + caliber / version / window / quality summary / lineage pointer;

[0123] Consistency and performance: Incremental window updates, cached TTL, batch fetching, and concurrent rate limiting ensure consistency between training and deployment.

[0124] Example (example from the manual, not intended as a protocol limitation): Send a POST request to the / features / query interface, querying the device with device ID "INV-07" (DevID: INV-07) and the feeder with topology ID "Feeder-12" (Topo: Feeder-12). Specifically, request data within the range of 00:00:00 (UTC) on November 21, 2025 to 06:00:00 (UTC) on November 21, 2025, with a quality status of "valid" or "interpolated_ok". For device INV-07, query the "load_ratio" feature (calculated) for version "v1". The calculation window is 30 minutes. For Feeder-12, the query is for the "pv_penetration" feature with version "v1" (calculation window is 15 minutes), and the latest data version is specified as November 21, 2025, 06:00:10 (UTC time). The interface response returns two sets of core data: the load_ratio feature data of device INV-07 (including specific data points and meta-information such as version "load_ratio@1.3") and the pv_penetration feature data of Feeder-12 (including specific data points and meta-information such as version "pv_penetration@2.1"), along with traceability information such as data version and feature plan ID.

[0125] Step S8: Algorithm enablement and application interface (closed loop);

[0126] The algorithm service is registered in the base as a "managed module" and obtains data from the feature service in a unified manner. The output prediction / optimization / control results are written back to the base (with object set, time window, version and quality description), and the model version and input data version are attached in the lineage. Business systems (dispatch master station, energy management, virtual power plant, etc.) retrieve data and results through the unified interface of the base to achieve closed loop.

[0127] Step S9: Security Audit and Multi-tenant Operation and Maintenance;

[0128] Implement tenant-level isolation and access control; audit and alert on interface calls, data queries, and result write-back; monitor the operating status of each module to ensure the stability of the platform.

[0129] It should be noted that DevID / PointID in this embodiment is used throughout the entire construction process. Specifically, in step S2, the unified coding system maps RawDev / RawPoint to DevID / PointID. In steps S3, S4, S7, and S8, DevID / PointID is used as the primary key for object binding, index key, feature acquisition, and result write-back, respectively.

[0130] It should also be noted that words such as "include" or "contain" mean that the element preceding the word covers the element listed after the word, and do not exclude the possibility that it may also cover other elements.

[0131] Although operations are described in a specific order in the accompanying drawings in this disclosure, it should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the shown operations to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0132] Finally, it should be noted that the above content is only used to illustrate the technical solution of this disclosure, and is not intended to limit the scope of protection of this disclosure. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of this disclosure do not depart from the substance and scope of the technical solution of this disclosure.

Claims

1. A digital docking station system, characterized in that, Applications include: Integrated power generation, grid, load, and storage platforms The data access and standardization module is used to acquire the original operational data of multiple business objects in the integrated source-grid-load-storage platform and form a unified record; the unified record includes a unified identifier. The object and topology modeling module is used to construct a unified object model and the topological relationships between the business objects in the unified object model based on the unified record; and to bind each topology node in the topological relationship to the unified identifier; the unified identifier includes a unique device identifier and a unique measurement point identifier. The lake-warehouse integration and indexing module includes multiple databases for constructing a unified index for the multiple databases. The unified index includes the unified identifier. The multiple databases include a time-series database storing the unified records and a graph database storing the topological relationships. The algorithm and application interface module includes a unified interface, which is used to obtain a request carrying the unified index sent by the source-grid-load-storage integrated platform based on the unified interface, determine the result of the request, and write the result back to the lake-warehouse integration and index module and return it to the source-grid-load-storage integrated platform. The feature service module includes a feature operator library, used to generate and return corresponding feature data based on the unified identifier in the unified index and the feature operator library; the algorithm and application interface module is also used to call the feature service module; and to determine the algorithm result of the request based on the feature data returned by the feature service module; The metadata and data lineage management module is used to record the full-link lineage of the data corresponding to each of the aforementioned business objects and generate corresponding lineage association records; the lineage association records are transmitted to the lake warehouse integration and indexing module for storage; the full-link lineage includes data access and data standardization of the data access and standardization module, data storage of the lake warehouse integration and indexing module, feature processing of the feature service module, and algorithm processing and data write-back of the algorithm and application interface module; The data quality and tag management module is used to perform quality verification on the unified records of each of the aforementioned business objects based on preset general rules and electrical constraints, and generate quality identifiers; and to write the quality identifiers back to the unified records of the lake warehouse integration and index module; The data access and standardization module includes a protocol adaptation and data access module and a data standardization and unified encoding module. The protocol adaptation and data access module is used to interface with multiple protocols of the multiple business objects to obtain the original operating data of the multiple business objects, and after completing the protocol decoding, encapsulate the original operating data into an access layer standard record; and transmit the access layer standard record to the lake warehouse integration and index module for storage; the access layer standard record includes a timestamp, original device identifier, original measurement point identifier, original value, original quality code, source channel and protocol name; The data standardization and unified coding module is used to map the original device identifier and the original measurement point identifier of the access layer standard record to the unique device identifier and the unique measurement point identifier respectively through a unified coding system, forming a unified record; and to write the unified record back to the lake warehouse integration and index module; the unified record includes a unified timestamp, a unique device identifier, a unique measurement point identifier, a standardized value, a quality identifier, and a source identifier.

2. The digital base system according to claim 1, characterized in that, The algorithm and application interface module also includes registered algorithm services; when the registered algorithm services are triggered, they are used to determine the algorithm result of the request based on the feature data returned by the feature service module.

3. The digital base system according to claim 1, characterized in that, The feature service module also includes a feature service interface; The feature service module returns the feature data through the feature service interface; the algorithm and application interface module calls the feature service module through the feature service interface.

4. The digital docking station system according to any one of claims 1-3, characterized in that, The feature service module is further configured to determine the corresponding target business object based on the unified identifier in the unified index, and pull relevant data of the target business object from the corresponding database of the lake warehouse integration and index module; call the corresponding operator in the feature operator library to generate feature data of the target business object based on the relevant data of the target business object; and return the feature data to the algorithm and application interface module.

5. The digital docking station system according to any one of claims 1-3, characterized in that, Also includes: The security audit and multi-tenant operation and maintenance module is used to log and audit the interface call operations, data query operations and result write-back operations associated with each of the aforementioned business objects; It also monitors the operating status of each module and issues anomaly alerts.

6. The digital docking station system according to any one of claims 1-3, characterized in that, The lake warehouse integration and indexing module is also used to perform write operations on any database and synchronously update the unified index; the multiple databases also include a relational database that stores the static attribute data of each business object and an object storage database that stores the log documents of each business object; The unified index also includes topology node identifiers, business tags, and time windows to allow the integrated source-grid-load-storage platform to query data by device, region, scenario, or time.

7. The digital docking station system according to any one of claims 1-3, characterized in that, The multiple business objects include power supply units, grid equipment, load units, and energy storage units; The power supply unit includes at least one of the following: a photovoltaic inverter, a wind turbine generator set, and a coal-fired generator set; the grid equipment includes at least one of the following: a transmission line, a distribution feeder, and a switchgear; the load unit includes at least one of the following: a data center server, an industrial motor, and a household appliance; and the energy storage unit includes at least one of the following: an electrochemical energy storage battery pack, an energy storage converter, and a pumped storage power station.

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