A power station data full life cycle management system

Abstract

The application discloses a power station data full life cycle management system, and belongs to the technical field of power station intelligentization and data management. The system comprises a point checking unit, a quality evaluation unit, a deep diagnosis unit, and a globally unique identification inheritance module, a standardized interaction protocol module and a closed-loop self-optimization engine throughout the whole link. The globally unique identification realizes the full life cycle traceability of the measuring point data from access to archiving. The standardized interaction protocol realizes seamless cooperation of the three core units. The closed-loop self-optimization engine realizes the continuous iteration of the system performance. The application solves the industry pain points of the existing technology, such as the fragmentation of the data management link, the non-uniform benchmark, the high docking cost and the lack of closed-loop optimization, and builds a full-link management system of pre-admission, in-process control, post-diagnosis and closed-loop optimization. The application provides a high-reliability and full-compliance data base for the intelligent application of power stations, and can be widely applied to the data management scenes of various hydropower stations / pumped storage power stations.

Description

Technical Field

[0001] This invention belongs to the field of power plant intelligence and power system data governance technology, specifically involving a full life cycle governance system that adapts to power security zoning protection requirements and covers data from access access to quality control, and then to anomaly diagnosis and closed-loop optimization. Background Technology

[0002] Currently, power plant data governance in the industry mainly consists of three core stages: first, communication point verification before data access to resolve issues such as measurement point parsing errors and mapping misalignments; second, quality assessment after data access to address issues related to data integrity, timeliness, and accuracy control; and third, root cause diagnosis after data anomalies to distinguish between data anomalies and equipment status anomalies. Existing technologies offer single-point solutions for each of these three stages, but they suffer from the following core shortcomings: 1) The process is severely fragmented and the benchmark system is not unified. The three processes of communication point, quality assessment, and anomaly diagnosis are independent of each other, and each uses its own measurement point identifier and benchmark rules. This makes it easy for problems such as misalignment of measurement point mapping and inconsistency of benchmark thresholds to occur, resulting in misjudgment of quality and distortion of diagnosis, and making it impossible to achieve full-link traceability of measurement point data.

[0003] 2) Lack of standardized data interaction protocols leads to extremely high implementation costs. There are no unified interaction rules between the three stages, requiring customized development for data integration between systems and modules from different manufacturers. This results in long project implementation cycles, unclear boundaries of responsibility, and a high risk of blame-shifting between equipment manufacturers and system integrators.

[0004] 3) Lack of closed-loop self-optimization capability, resulting in long-term performance degradation. The results of anomaly diagnosis cannot be used to optimize the benchmark rules for point verification and the verification logic for quality assessment. The system cannot learn and iterate as the equipment operating status and data characteristics change, leading to a continuous decline in data governance accuracy after long-term operation.

[0005] 4) End-to-end compliance cannot be guaranteed. Existing fragmented solutions cannot achieve end-to-end compliance control of "verification first, assessment later, access later, and full archiving". There are compliance risks in the process of data transmission across security zones, which cannot meet the security audit requirements of the power industry.

[0006] To address the pain points of the aforementioned single-point links, the applicant filed three invention patents on the same day, respectively solving technical problems related to cross-security zone communication, automatic data quality assessment, and distinguishing between data anomalies and equipment status anomalies. However, all three patents are unit-level methodologies and do not involve system-level collaboration and end-to-end closed-loop governance of the three links, thus failing to resolve the core industry pain point of fragmented links. Therefore, there is an urgent need for a closed-loop governance system covering the entire lifecycle of power plant data to achieve organic collaboration, benchmark unification, and closed-loop optimization of the three core links. Summary of the Invention

[0007] This invention addresses the shortcomings of existing technologies by providing a power plant data lifecycle governance system. It solves the core pain points of existing technologies, such as fragmented data governance processes, inconsistent benchmarks, high integration costs, lack of closed-loop optimization, and insufficient compliance. The system constructs a full-link governance system of "pre-access, in-process control, post-diagnosis, and closed-loop optimization," providing a highly reliable and fully compliant data foundation for intelligent power plant applications.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A power plant data lifecycle governance system is provided. The system is adapted to the power security protection architecture of the power plant production control area and management information area. It includes a point verification unit, a quality assessment unit, a deep diagnosis unit, as well as a globally unique identifier inheritance module, a standardized interaction protocol module, and a closed-loop self-optimization engine that run through all units.

[0009] The point verification unit, as the system's reference source, is used to construct a unified measurement point reference library for the entire station, assign globally unique identifiers (GUIDs) to measurement points, complete same-partition source verification and cross-security partition end-to-end point verification, and output locked measurement point reference data with GUIDs; the internal point verification method of this unit.

[0010] The quality assessment unit, as the core of the system's in-process control, communicates with the point verification unit through a standardized interaction protocol module. It inherits the GUID and the measurement point benchmark library, performs data quality assessment with the GUID as the unique primary key, and outputs a list of abnormal measurement points bound by the GUID, quality score labels, and abnormal category location results; the internal quality assessment method of this unit.

[0011] The deep diagnostic unit, as the core of the system's post-diagnosis, inherits GUID, measurement point benchmark library, and full-cycle quality assessment data from the point verification unit and the quality assessment unit through a standardized interaction protocol module. It uses GUID as a unique index to distinguish between data anomalies and equipment status anomalies, and outputs diagnostic results with GUID binding. This unit also includes its internal anomaly differentiation method.

[0012] The globally unique identifier inheritance module is used to manage the rules of the entire lifecycle of GUID, ensuring that GUID, as the unique identity credential of the measurement point, is immutable, non-repeatable, and unchangeable throughout the entire process, and that all data processing uses GUID as the unique associated primary key.

[0013] The standardized interaction protocol module is used to define the benchmark synchronization interaction protocol between the point verification unit and the quality assessment unit, the abnormal data interaction protocol between the quality assessment unit and the deep diagnosis unit, and the closed-loop feedback interaction protocol between the deep diagnosis unit and the two preceding units.

[0014] The closed-loop self-optimization engine is used to match corresponding test points with GUIDs based on the diagnostic results output by the deep diagnostic unit, and to reverse-optimize the test point benchmark library and quality verification rules, thereby realizing system self-learning and closed-loop governance.

[0015] Furthermore, in the globally unique identifier inheritance module, the GUID adopts a 16-bit fixed-length hexadecimal encoding, with the encoding rules as follows: the 1st and 2nd bits are the security partition encoding, the 3rd and 4th bits are the manufacturer encoding, the 5th and 6th bits are the system encoding, the 7th and 8th bits are the device encoding, and the 9th to 16th bits are the measurement point serial number; the GUID remains unique and unchangeable throughout the entire lifecycle of measurement point access, parsing, transmission, processing, and archiving.

[0016] Furthermore, the globally unique identifier inheritance module is configured with first-level inheritance rules: all verification rules, threshold configurations, anomaly markers, and quality scores of the quality assessment unit are bound to the corresponding GUID. Measurement points that fail the point-to-point verification or do not have a valid GUID are automatically included in the quality assessment blacklist and are prohibited from accessing the formal business data channel, strictly implementing the compliance requirement of point-to-point verification before assessment.

[0017] Furthermore, the globally unique identifier inheritance module is configured with a two-level inheritance rule: the deep diagnostic unit retrieves the point-to-point verification benchmark information, full-cycle quality assessment data and real-time operation data of the corresponding measurement point through the GUID. All diagnostic processes and results are bound to the corresponding GUID. Measurement point data without a valid GUID will not be subject to diagnostic analysis, thus ensuring the reliability of the diagnostic results from the source.

[0018] Furthermore, in the standardized interaction protocol module, the benchmark synchronization interaction protocol supports full synchronization, incremental synchronization, and timed reconciliation synchronization. It adopts a three-segment fixed data packet structure, with the index segment and benchmark data segment sorted and associated with GUID as the core. The triggering mechanism includes: automatic full synchronization after the full verification of the point passes, real-time incremental synchronization when the measurement point configuration changes, and full reconciliation synchronization triggered at a fixed time every day.

[0019] Furthermore, in the standardized interaction protocol module, the abnormal data interaction protocol defines the triggering mechanism for abnormal data push and a three-segment fixed structure data packet format. The data packet consists of a header segment, an abnormal index segment, and an abnormal data segment. All abnormal measurement points in the data packet are uniquely identified and associated using GUIDs. The triggering mechanism includes: real-time push when the quality assessment unit detects an abnormal measurement point, on-demand push when the deep diagnosis unit initiates a diagnosis request, and push of full quality statistics data at a fixed time every day.

[0020] Furthermore, in the standardized interaction protocol module, the closed-loop feedback interaction protocol defines the triggering mechanism for diagnostic result feedback and a three-segment fixed structure data packet format. The data packet consists of a header segment, a feedback index segment, and a feedback data segment. The feedback content is accurately matched to the corresponding measurement point through GUID. The triggering mechanism includes: real-time feedback when the deep diagnostic unit generates the final result, confirmation feedback triggered after on-site verification and confirmation by maintenance personnel, and statistical result feedback triggered at a fixed time each month.

[0021] Furthermore, the closed-loop self-optimization engine is also used to collect the point-to-point verification history, full-cycle quality assessment records, anomaly diagnosis results, and on-site verification data of each GUID corresponding to the measurement point, construct a full life-cycle digital archive of the measurement point, and generate an end-to-end tamper-proof compliance audit report to meet the safety audit requirements of the power industry.

[0022] Furthermore, the point-to-point verification unit is deployed at the front-end acquisition device of each production control safety zone and the central node of the management information zone, and the quality assessment unit and deep diagnosis unit are deployed on the power plant-level data platform of the management information zone, which is fully adapted to the power safety protection requirements. Cross-zone data transmission is only achieved through forward / reverse safety isolation devices.

[0023] Correspondingly, the present invention also provides a method for the full lifecycle governance of power plant data, based on the above-mentioned system, comprising the following steps: S1. The point verification unit assigns a globally unique identifier (GUID) to each measurement point to be connected, completes source verification within the same partition and end-to-end point verification across partitions, and outputs locked measurement point reference data with GUID. S2. The quality assessment unit inherits the GUID and measurement point benchmark library output by the point verification unit. It performs a full-process data quality assessment with GUID as the unique primary key, and outputs a list of abnormal measurement points with GUID binding, quality score labels and abnormal category location results. S3, the deep diagnostic unit inherits the GUID of the corresponding measurement point, the measurement point benchmark library and the full-cycle quality assessment data. Using the GUID as the unique index, it accurately distinguishes between abnormal data and abnormal equipment status for abnormal measurement points, and outputs the final diagnostic result and confidence rating with GUID binding. S4. The closed-loop self-optimization engine matches the corresponding test points through GUID based on the diagnostic results, and reverse-optimizes the test point benchmark library of the test point verification unit and the verification rules of the quality assessment unit to complete the closed-loop governance of the entire life cycle.

[0024] The power plant data lifecycle governance system of the present invention is adapted to the power security protection architecture of the power plant production control area and management information area. The underlying layer is based on the redundant security isolation protection system of the inventor's application (application number CN117319013B) to build a cross-area primary and backup communication link, and based on the cross-security partition data access and transmission system of the inventor's application (application number CN114363096B) to realize the synchronous transmission of data and link status, forming a full-link governance system of pre-access, in-process control, post-diagnosis and closed-loop optimization.

[0025] The embodiments of the present invention bring the following beneficial effects: This invention, through the organic collaboration of three core units, achieves full lifecycle governance of power plant data, from access approval to quality control, anomaly diagnosis, and closed-loop optimization, addressing the core industry pain points of fragmented processes and inconsistent benchmarks in existing technologies. By using unique identifiers throughout the entire chain, this invention fundamentally avoids misjudgments caused by misaligned measurement point mapping and inconsistent benchmarks, ensuring full traceability of each measurement point's data from generation to archiving, while clearly defining the responsibility boundaries of each stage. This invention establishes a standardized data interaction protocol between the three core units, enabling seamless integration of systems from different manufacturers and significantly reducing project implementation and maintenance costs. Through reverse feedback of diagnostic results, this invention achieves system self-learning and self-optimization, improving the accuracy of distinguishing between data anomalies and equipment status anomalies. This invention strictly adheres to power safety protection standards, achieving full-chain compliance control through verification, evaluation, access, and full archiving. The generated tamper-proof audit report fully meets the power industry's safety audit requirements, with no compliance risks. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is an overall architecture diagram of the system described in an embodiment of the present invention; Figure 2 This is a schematic diagram of the GUID full-link inheritance mechanism described in an embodiment of the present invention; Figure 3 This is a timing diagram of the standardized interaction protocol of the three main units described in the embodiments of the present invention; Figure 4 This is an overall flowchart of the closed-loop governance method for the entire life cycle described in this embodiment of the invention. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to specific embodiments. This embodiment takes a 300MW pumped storage power station as the application scenario. The power station strictly follows the power safety protection requirements and is divided into a production control zone (Safety Zone I and Safety Zone II) and a management information zone (Safety Zone III). Safety Zone I is equipped with production control-related systems such as computer monitoring systems, speed regulation systems, and excitation systems. Safety Zone II is equipped with non-production control-related systems such as unit status monitoring systems, high-voltage equipment status monitoring systems, and hydrological monitoring systems. The management information zone is equipped with management systems such as production management systems and material management systems, as well as a power plant-level data platform and intelligent operation and maintenance system. The entire station is equipped with GPS / BeiDou dual-mode time synchronization devices, and the time synchronization accuracy of all systems and equipment is not less than 1ms. This embodiment is fully compatible with the implementation scenarios of the four invention patents filed by the applicant on the same day. Those skilled in the art can fully implement the solution of the present invention based on the content disclosed in this embodiment.

[0029] I. Overall System Deployment and Core Unit Implementation like Figure 1 As shown, the power plant data lifecycle governance system in this embodiment adopts a partitioned deployment, unidirectional transmission, and centralized aggregation architecture, which is fully adapted to power security protection requirements, including: 1. Point-to-point verification unit: Deployed in two parts, with the front-end acquisition devices deployed in Security Zone I and Security Zone II on the front end, and the central side deployed in the power plant-level data platform in the management information area; responsible for building a unified measurement point benchmark library for the entire station, generating GUIDs, and completing source verification within the same zone and end-to-end point-to-point verification across zones.

[0030] 2. Quality Assessment Unit: Deployed in the power plant-level data platform of the management information area, it is connected to the central side of the point verification unit through intranet communication; based on the inherited GUID and measurement point benchmark library, it completes batch integrity verification, single measurement point basic verification, time series correlation analysis, anomaly category location and quality scoring.

[0031] 3. Deep Diagnostic Unit: The intelligent operation and maintenance system deployed in the management information area is connected to the point verification unit and the quality assessment unit through intranet communication; based on the inherited GUID and full-link data, it can accurately distinguish between data anomalies and equipment status anomalies at abnormal measurement points.

[0032] 4. Globally Unique Identifier Inheritance Module: Distributed across three core units, it uniformly manages the entire lifecycle rules for GUID generation, inheritance, and updating.

[0033] 5. Standardized Interaction Protocol Module: Distributed across the three core units, it unifies the interaction rules and data packet formats between management units.

[0034] 6. Closed-loop self-optimization engine: Deployed on the management information regional data platform, responsible for closed-loop optimization, full-link file management, and compliance audit report generation.

[0035] II. Implementation of the Globally Unique Identifier (GUID) Full-Link Inheritance Mechanism like Figure 2 As shown, the GUID full-link inheritance mechanism in this embodiment is specifically implemented as follows: 1. GUID generation rules: 16-bit fixed-length hexadecimal encoding is used. The encoding rules and examples are as follows:

[0036] A complete GUID example: 0102010100000001. This identifier serves as a unique identity credential for the measurement point and remains unchanged throughout its entire lifecycle.

[0037] 2. GUID Generation and Locking: The point verification unit assigns GUIDs to each of the more than 20,000 measurement points across the entire site and pre-builds a measurement point benchmark library; after completing the source verification within the same partition and the end-to-end point verification across partitions, the GUID and the benchmark information of the corresponding measurement point are locked and written to a read-only configuration file, which cannot be modified without an approval process.

[0038] 3. First-level inheritance (point-to-point verification unit → quality assessment unit): The quality assessment unit fully inherits the GUIDs and benchmarks of all measurement points. All data processing steps use the GUID as the unique primary key and prohibit using the measurement point name or address as the core identifier. The batch affiliation, value range verification rules, rate of change threshold, anomaly marker, quality score, and historical records of the measurement points are all bound to the corresponding GUID; Measurement points that fail the point verification or lack a valid GUID are automatically included in the quality assessment blacklist, prohibited from accessing the formal business data channel, and stored only in an isolated database for post-analysis.

[0039] 4. Secondary inheritance (point-to-point verification unit + quality assessment unit → deep diagnostic unit): The deep diagnostic unit fully inherits the GUIDs of all measurement points, and can retrieve three types of core data for the corresponding measurement point with one click through the GUID: ① baseline information, source verification records, and full life cycle health records of the point verification unit; ② full life cycle quality score, anomaly history, and time-series correlation data of the quality assessment unit; ③ real-time operation data. The mechanism rule matching, anomaly judgment, mechanism linkage verification, cross-validation, diagnostic results, and handling suggestions of the measurement points are all bound to the corresponding GUIDs. Measurement point data without a valid GUID or that has not undergone quality assessment will not be used for diagnostic analysis by the deep diagnostic unit, thus ensuring the reliability of diagnostic results from the source.

[0040] 5. Closed-loop inheritance (deep diagnostic unit → two preceding units): The final results of the deep diagnostic unit are precisely bound to the corresponding measurement points through GUIDs and then synchronized in reverse to the point verification unit and the quality assessment unit. Based on the feedback results, the point verification unit updates the full life cycle health record, degradation characteristics, and benchmark threshold of the corresponding GUID measurement point. Based on the feedback results, the quality assessment unit optimizes the verification rules, anomaly detection logic, and quality scoring weights for the corresponding GUIDs, achieving closed-loop optimization across the entire chain.

[0041] III. Specific Implementation of the Standardized Interaction Protocol Module The standardized interaction protocol module in this embodiment defines three core interaction protocols. All protocols strictly comply with power safety protection requirements. Cross-zone transmission is achieved only through forward / reverse security isolation devices, with no unauthorized bidirectional interaction. The timing of the standardized interaction protocols between the three units in this embodiment is as follows: Figure 3 As shown.

[0042] 1. Reference synchronization and interaction protocol (point-to-point verification unit → quality assessment unit) Triggering mechanisms: ① Full baseline data synchronization is automatically triggered after 100% of the full measurement points pass the point-to-point verification; ② Incremental synchronization is triggered in real time after measurement points are added, deleted, or their configurations are modified; ③ Full baseline data reconciliation and synchronization is triggered at 2:00 AM every day to ensure that the baselines at both ends are completely consistent.

[0043] The data packet has a fixed format: it adopts a three-segment structure, with GUIDs bound throughout, and is compatible with both TCP transmission and file transfer modes for cross-partition transmission. The packet header is fixed at 32 bytes: Unique batch number (4 bytes), synchronization type (1 byte): 0x01 full / 0x02 incremental, transmission time stamp (8 bytes), full-station synchronization clock, total number of measurement points (4 bytes), full-message CRC32 checksum (4 bytes), reserved bytes (11 bytes); Each measurement point in the index segment corresponds to 16 bytes: The measurement point GUID is 8 bytes, the data offset is 4 bytes, the data length is 2 bytes, and the CRC16 check value for a single measurement point is 2 bytes. The index segments are arranged in ascending order by GUID, and the receiving end can quickly retrieve the corresponding measurement point data by GUID.

[0044] Baseline data segment: Each GUID corresponds to complete benchmark information for a measurement point, including: GUID, Chinese name of the measurement point, dimensions, data type, byte length, endianness, fixed offset, measurement range, original protocol type, point verification result, and health record summary. All entries are arranged in the order of the index segment. Verification rules: After receiving the data packet, the receiving end first verifies the CRC32 checksum of the entire message, then checks the total number of test points and the number of index segments, and finally verifies the CRC16 checksum of a single test point. Only after all verifications pass can the local benchmark library be updated to ensure that the benchmark data transmission is error-free.

[0045] 2. Abnormal Data Interaction Protocol (Quality Assessment Unit → In-depth Diagnosis Unit) Triggering mechanisms: ① When the quality assessment unit detects that the measurement point verification fails and triggers an anomaly flag, it will push the abnormal data in real time; ② When the deep diagnosis unit initiates a diagnosis request, it will push the full-cycle quality assessment data of the corresponding GUID measurement point as needed; ③ The 24-hour quality statistics data of all measurement points will be pushed at 3:00 AM every day.

[0046] The data packet has a fixed format: it adopts a three-segment structure, with GUIDs as the core identifier throughout. The packet header is fixed at 28 bytes: The abnormal batch number is 4 bytes, the trigger time stamp is 8 bytes, the abnormal type code is 2 bytes, the total number of associated GUIDs is 4 bytes, the full message CRC32 checksum is 4 bytes, and the reserved bytes are 6 bytes. Each abnormal test point in the abnormal index segment corresponds to 12 bytes: The measurement point GUID is 8 bytes, the exception type code is 1 byte, the exception occurrence timestamp is 4 bytes, and the data offset is 3 bytes. The exception index segments are sorted in descending order of the time the exception occurred.

[0047] Abnormal data segment: Each GUID corresponds to complete data for an abnormal measurement point, including: GUID, anomaly type, anomaly occurrence time, quality score, anomaly category location result, associated time series data, point verification benchmark summary, and quality status of associated measurement points in the same batch.

[0048] 3. Closed-loop feedback interaction protocol (deep diagnostic unit → point-to-point verification unit + quality assessment unit) Triggering mechanisms: ① Real-time feedback is triggered when the deep diagnostic unit generates the final diagnostic results; ② Confirmation feedback is triggered after maintenance personnel verify and confirm the results on-site; ③ Feedback on the diagnostic statistics and optimization suggestions of the previous month is triggered on the 1st of each month.

[0049] The data packet has a fixed format: it adopts a three-segment structure, and uses GUIDs to accurately match the corresponding measurement points; reverse transmission is only achieved through the file transfer mode of the reverse security isolation device, transmitting only plain text statistical data without any control commands. The packet header is fixed at 24 bytes: Feedback batch number (4 bytes), generation timestamp (8 bytes), feedback type (1 byte): 0x01 real-time / 0x02 confirmation / 0x03 statistics, total number of associated GUIDs (4 bytes), full message CRC32 checksum (4 bytes), reserved bytes (3 bytes); Each measurement point in the feedback index segment corresponds to 10 bytes: The measurement point GUID is 8 bytes, the discrimination result encoding is 1 byte, and the confidence rating is 1 byte. Feedback data segment: The complete feedback content for each measurement point corresponding to a GUID includes: GUID, final judgment result, confidence rating, fault location, handling suggestions, benchmark threshold optimization parameters, verification rule optimization suggestions, and quality score weight adjustment parameters.

[0050] IV. Specific Implementation of the Closed-Loop Self-Optimization Engine The closed-loop self-optimization engine in this embodiment is implemented as follows: 1. Full-link data collection: Automatically collects the point-to-point verification history, full-cycle quality assessment records, anomaly diagnosis results, and on-site verification confirmation data for each GUID corresponding to the measuring point, constructs a digital archive of the measuring point's entire lifecycle, stores it in an immutable archive storage, and retains it for no less than 6 years to meet the compliance audit requirements of the power industry.

[0051] 2. Automatic optimization execution: For measurement points whose diagnostic results indicate abnormal equipment status and are confirmed by on-site verification, the quality assessment threshold and abnormal judgment logic corresponding to the GUID are automatically optimized, and the fluctuation range of normal operating conditions of the equipment is included in the benchmark threshold to avoid misjudgment. For measurement points whose diagnostic results indicate sensor drift / failure and are confirmed by on-site verification, the full lifecycle health record of the measurement point corresponding to the GUID is automatically updated, sensor degradation characteristics are marked, and the benchmark rules for point calibration and the calibration sensitivity of quality assessment are optimized simultaneously. Based on machine learning algorithms, the system automatically calibrates the benchmark thresholds, verification rules, and mechanism linkage rules for measurement points under different working conditions, continuously improving the system's accuracy.

[0052] 3. Compliance Audit Report Generation: A full-chain compliance audit report for site data governance is automatically generated monthly. The report includes: test point access status, point verification results, quality assessment statistics, anomaly diagnosis statistics, rule change records, and full-chain operation logs. After the report is generated, it is automatically stored in an immutable archive for regulatory audit and operation and maintenance traceability.

[0053] V. Complete Implementation Process of the Closed-Loop Governance Method Throughout the Entire Life Cycle like Figure 4 As shown, the complete execution steps of the full lifecycle closed-loop governance method in this embodiment are as follows: Step S1: Point verification and benchmark locking S11. The management information regional data center constructs a unified measurement point benchmark library for the entire station, assigns a GUID to each measurement point to be connected, and synchronizes the benchmark library to the front-end acquisition devices of each security zone. S12. The front-end acquisition device in the security partition performs the same partition source benchmark verification, completes the specification compliance verification and parsing consistency verification, and generates structured benchmark data with GUID and locks the cache after the verification is passed; S13. Based on the power station's safety isolation configuration, select either a pure one-way verification mode or a two-way closed-loop verification mode to complete the end-to-end point-to-point verification across safety zones; S14. After the full measurement point-to-point verification is 100% successful, the locked measurement point benchmark data with GUID is synchronized to the quality assessment unit through the benchmark synchronization interaction protocol, and the formal business data channel is opened.

[0054] Step S2: Full-process data quality assessment S21. The quality assessment unit inherits the GUID and measurement point benchmark library output by the point verification unit, and performs data batch integrity verification with the GUID as the unique primary key. S22. Perform single-point basic quality verification for each measurement point, including value range verification, format verification, logical constraint verification, and timing continuity verification. All verification results are bound to the corresponding GUID. S23. Based on unified time information, perform causal time series matching analysis, cross-system time scale consistency verification, and device status and analog quantity linkage analysis, and bind the analysis results to the corresponding GUID; S24. Based on the comprehensive verification results, complete the identification of the major categories of anomalies, calculate the comprehensive quality score for each measuring point, and generate a list of anomaly measuring points with GUID binding, quality score labels, and anomaly category identification results; S25. Synchronize abnormal measurement point data to the deep diagnostic unit through the abnormal data interaction protocol.

[0055] Step S3: In-depth diagnosis of the root cause of the anomaly The S31 deep diagnostic unit inherits the GUID of the corresponding measurement point, the measurement point benchmark library and the full-cycle quality assessment data. It uses the GUID as the unique index to retrieve the full-link historical data of the corresponding measurement point. S32. Based on the full lifecycle health records of measurement points, perform pre-link verification to filter out abnormal data related to link faults; S33. Match the current subdivided working condition scenario, lock the corresponding benchmark threshold, and shield the interference of transient fluctuations in the working condition; S34. Based on the index type of the measuring point, perform a preliminary differential anomaly judgment to screen out single-point data anomalies and transient interference-type data anomalies; S35. Perform cross-parameter strong correlation mechanism linkage verification to determine whether the anomaly conforms to the physical evolution law of the equipment; S36. Perform multi-model cross-validation, generate the final discrimination result and three-level confidence rating, and output the diagnostic results and treatment recommendations with GUID binding; S37. The diagnostic results are back-synchronized to the point-to-point verification unit, the quality assessment unit, and the closed-loop self-optimization engine through a closed-loop feedback interaction protocol.

[0056] Step S4: Closed-loop self-optimization S41. The closed-loop self-optimization engine receives diagnostic results, matches corresponding test points through GUIDs, collects historical data across the entire link, and constructs a digital archive of the test point's entire lifecycle. S42. Based on the diagnostic results and on-site verification data, reverse-optimize the benchmark library and health records of the point verification unit, as well as the verification rules and scoring weights of the quality assessment unit; S43. Generate a full-chain compliance audit report to complete closed-loop governance throughout the entire lifecycle.

[0057] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A power plant data full life cycle governance system, characterized in that, It adapts to the power safety zone protection architecture, including point verification unit, quality assessment unit, deep diagnosis unit, as well as a globally unique identifier inheritance module, standardized interaction protocol module, and closed-loop self-optimization engine that runs through the entire unit; The point verification unit is used to build a unified measurement point benchmark library for the entire station, assign a globally unique identifier (GUID) to the measurement point, complete the same-partition source verification and cross-security partition end-to-end point verification, and output locked measurement point benchmark data with GUID. The quality assessment unit communicates with the point verification unit through a standardized interaction protocol module. It inherits the GUID and the measurement point benchmark library, performs data quality assessment with the GUID as the unique primary key, and outputs a list of abnormal measurement points bound with GUID, quality score labels, and abnormal category location results. The deep diagnostic unit inherits GUID, measurement point benchmark library and full-cycle quality assessment data through a standardized interaction protocol module. It uses GUID as a unique index to distinguish between data anomalies and equipment status anomalies, and outputs diagnostic results with GUID binding. The globally unique identifier inheritance module is used to manage the rules of the entire life cycle of GUID, ensuring that GUID, as the unique identity credential of the measurement point, is immutable, non-repeatable, and unchangeable throughout the entire process, and that all data processing uses GUID as the unique association key; The standardized interaction protocol module is used to define the benchmark synchronization interaction protocol between the point verification unit and the quality assessment unit, the abnormal data interaction protocol between the quality assessment unit and the deep diagnosis unit, and the closed-loop feedback interaction protocol between the deep diagnosis unit and the point verification unit and the quality assessment unit. The closed-loop self-optimization engine is used to match corresponding test points with GUIDs based on the diagnostic results output by the deep diagnostic unit, and to reverse-optimize the test point benchmark library and quality verification rules, thereby realizing system self-learning and closed-loop governance.

2. The system of claim 1, wherein, In the globally unique identifier inheritance module, the GUID adopts fixed-length structured encoding, and the encoding segment includes security partition encoding, manufacturer encoding, system encoding, equipment encoding, and measurement point serial number; the GUID remains unique and unchangeable throughout the entire lifecycle of measurement point access, parsing, transmission, processing, and archiving.

3. The system of claim 1, wherein, The globally unique identifier inheritance module is configured with first-level inheritance rules: all verification rules, threshold configurations, anomaly markers, and quality scores of the quality assessment unit are bound to the corresponding GUID. Measurement points that fail the point verification or have no valid GUID are automatically included in the quality assessment blacklist and are prohibited from accessing the formal business data channel.

4. The system according to claim 1, characterized in that, The globally unique identifier inheritance module is configured with a two-level inheritance rule: the deep diagnostic unit retrieves the point-to-point verification benchmark information, full-cycle quality assessment data and real-time operation data of the corresponding measurement point through the GUID. All diagnostic processes and results are bound to the corresponding GUID. Measurement point data without a valid GUID will not be subject to diagnostic analysis.

5. The system according to claim 1, characterized in that, In the standardized interaction protocol module, the benchmark synchronization interaction protocol supports full synchronization, incremental synchronization, and timed reconciliation synchronization. It adopts a fixed-structure data packet consisting of a header segment, an index segment, and a benchmark data segment in sequence. The header segment contains a unique batch number, synchronization type, sending timestamp, total number of measurement points, and checksum. The index segment contains the measurement point GUID, data offset, and data length, arranged in ascending order by GUID. The benchmark data segment contains the complete benchmark information of the measurement point corresponding to each GUID.

6. The system according to claim 1, characterized in that, In the standardized interaction protocol module, the abnormal data interaction protocol supports real-time push, on-demand push, and timed statistical push. It adopts a fixed-structure data packet consisting of a header segment, an abnormal index segment, and an abnormal data segment in sequence. The header segment contains the abnormal batch number, trigger timestamp, abnormal type code, and total number of associated GUIDs. The abnormal index segment contains the measurement point GUID, abnormal type code, and abnormal occurrence timestamp, arranged in descending order of abnormal occurrence time. The abnormal data segment contains the complete data of the abnormal measurement point corresponding to each GUID.

7. The system according to claim 1, characterized in that, In the standardized interaction protocol module, the closed-loop feedback interaction protocol supports real-time feedback, confirmation feedback, and statistical feedback. It adopts a fixed-structure data packet consisting of a header segment, a feedback index segment, and a feedback data segment in sequence. The header segment includes the feedback batch number, generation time stamp, feedback type, and total number of associated GUIDs. The feedback index segment includes the measurement point GUID and the discrimination result code. The feedback data segment includes the complete feedback content of the measurement point corresponding to each GUID.

8. The system according to claim 1, characterized in that, The closed-loop self-optimization engine is also used to collect the full-link historical data of each GUID corresponding to the test point, construct a full lifecycle digital archive of the test point, and generate an immutable compliance audit report for the entire link.

9. The system according to claim 1, characterized in that, The point-to-point verification unit is deployed at the front-end acquisition device of each production control safety zone and the central node of the management information zone, while the quality assessment unit and the deep diagnosis unit are deployed on the power plant-level data platform of the management information zone.

10. A method for full lifecycle governance of power plant data, characterized in that, The system implementation based on any one of claims 1-9 includes the following steps: S1. The point verification unit assigns a globally unique identifier (GUID) to each measurement point to be connected, completes source verification within the same partition and end-to-end point verification across partitions, and outputs locked measurement point reference data with GUID. S2. The quality assessment unit inherits the GUID and measurement point benchmark library output by the point verification unit. It performs a full-process data quality assessment with GUID as the unique primary key, and outputs a list of abnormal measurement points with GUID binding, quality score labels and abnormal category location results. S3, the deep diagnostic unit inherits the GUID of the corresponding measurement point, the measurement point benchmark library and the full-cycle quality assessment data. Using the GUID as the unique index, it accurately distinguishes between abnormal data and abnormal equipment status for abnormal measurement points, and outputs the final diagnostic result and confidence rating with GUID binding. S4. The closed-loop self-optimization engine matches the corresponding test points through GUID based on the diagnostic results, and reverse-optimizes the test point benchmark library of the test point verification unit and the verification rules of the quality assessment unit to complete the closed-loop governance of the entire life cycle.

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