A new energy grid-connected data intelligent auditing method and system

Abstract

The application provides a new energy grid connection data intelligent auditing method and system, relating to the technical field of new energy grid connection approval. The method comprises the following steps: performing semantic metadata encapsulation on key data fields in the data to obtain a first semantic data package; attaching loss information to the first semantic data package to obtain a second semantic data package; performing informed data conversion on the key data fields in the second semantic data package based on a preset semantic mapping rule to generate view data conforming to the next department's business requirements; storing the first semantic data package and performing cross-department semantic consistency auditing based on the semantic metadata in the first semantic data package and the view data. The method aims to solve the problem of cross-department semantic inconsistency of new energy grid connection data, effectively ensures the consistency of cross-department data semantics, reduces errors caused by interpretation bias and interface loss, and improves the auditing efficiency and intelligent level.

Description

Technical Field

[0001] This invention relates to the field of new energy grid connection approval technology, and more specifically, to a method and system for intelligent review of new energy grid connection data. Background Technology

[0002] Ensuring semantic consistency of cross-departmental data and information during the grid connection approval process for new energy projects faces severe challenges. With the rapid development of the new energy industry, different approval departments have developed differentiated business practices regarding key data fields, and these differences have not been promptly synchronized to a unified review rule system. Traditional review mechanisms suffer from three core flaws: First, the business semantic emphasis formed by each department during data processing is not effectively recorded, leading to interpretation discrepancies in the same data under different business scenarios; second, legacy data interfaces cause structural losses during information conversion, and these losses lack a traceability mechanism; and finally, existing data cleaning tools, based on rigid rules for standardization, inadvertently erase the original business intent carried by the data.

[0003] Specifically, when precise engineering parameters recorded by the technical department (e.g., 100.567MW) are transmitted to the finance department via the old interface, they may be forcibly rounded to 101MW due to interface specifications. Although the numerical values ​​appear to match, the original design intent and engineering precision requirements are lost. More seriously, the existing review system cannot recognize this semantic drift caused by interface conversion or departmental business practices, and can only perform simple numerical comparisons. This deep semantic inconsistency is often only discovered in the later stages of approval, leading to project rework and schedule delays.

[0004] Existing technologies attempt to resolve format differences through data cleaning middleware, but their standardization logic fails to consider the diversity of business semantics. For example, forcibly modifying technical parameters to a preset format can disrupt the correlation between the data and the original business scenario. Furthermore, cross-departmental data flows lack complete semantic metadata records, making it impossible to trace the entire lifecycle of data from collection and transformation to use. This makes it difficult for auditors to distinguish between reasonable business deviations and genuine semantic conflicts. This deficiency means that the approval process for new energy grid connection remains highly reliant on manual review, severely hindering improvements in approval efficiency and intelligent development.

[0005] There is currently no effective technical solution to the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent review method and system for new energy grid connection data. It aims to solve the severe challenges faced in ensuring the semantic consistency of cross-departmental data information during the grid connection approval process of new energy projects, as well as the problem that the semantic focus of data processing in each department is not effectively recorded in the traditional review mechanism, resulting in interpretation deviations of the same data in different business scenarios, which in turn leads to deep semantic inconsistencies. This invention can effectively ensure the semantic consistency of cross-departmental data, reduce errors caused by interpretation deviations and interface losses, and improve review efficiency and intelligence.

[0007] In a first aspect, the present invention provides an intelligent verification method for new energy grid connection data, comprising the following steps: S1. When data flows out from the previous department, semantic metadata is encapsulated on the key data fields in the data to obtain the first semantic data packet; S2. During the flow of the first semantic data packet, record the loss information caused by data interface limitations, and append the loss information to the first semantic data packet to obtain the second semantic data packet; S3. When the second semantic data packet flows into the next department, the semantic metadata in the second semantic data packet is interpreted, and based on the preset semantic mapping rules, the key data fields in the second semantic data packet are subjected to informed data transformation to generate view data that meets the business needs of the next department; S4. Store the first semantic data packet, and perform cross-departmental semantic consistency audit based on the semantic metadata in the first semantic data packet and the view data.

[0008] The intelligent auditing method for new energy grid connection data provided by this invention can encapsulate key data fields with semantic metadata from the source of data outflow, record loss information during data flow, and finally perform informed data conversion when the data flows into the next department, and conduct cross-departmental semantic consistency auditing. This effectively solves the problem of cross-departmental semantic inconsistency in new energy grid connection data and improves auditing efficiency and accuracy.

[0009] Secondly, the present invention provides an intelligent verification system for new energy grid connection data, comprising: The outflow module is used to encapsulate the key data fields in the data with semantic metadata when data flows out from the previous department, so as to obtain the first semantic data packet; The flow module is used to record loss information caused by data interface limitations during the flow of the first semantic data packet, and to append the loss information to the first semantic data packet to obtain the second semantic data packet; The inflow module is used to interpret the semantic metadata in the second semantic data packet when the second semantic data packet flows into the next department, and perform informed data transformation on the key data fields in the second semantic data packet based on preset semantic mapping rules to generate view data that meets the business needs of the next department. The audit module is used to store the first semantic data packet and perform cross-departmental semantic consistency audit based on the semantic metadata in the first semantic data packet and the view data.

[0010] As can be seen from the above, the intelligent auditing method for new energy grid connection data provided by the present invention ensures the semantic integrity of data in cross-departmental flow by encapsulating semantic metadata and recording flow losses, thereby solving the problem of cross-departmental data semantic drift, effectively ensuring the consistency of cross-departmental data semantics, reducing errors caused by interpretation deviation and interface loss, and improving auditing efficiency and intelligence level.

[0011] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0012] Figure 1 A flowchart illustrating an intelligent verification method for new energy grid connection data provided in an embodiment of the present invention.

[0013] Figure 2 This is a schematic diagram of a new energy grid connection data intelligent review system provided in an embodiment of the present invention.

[0014] Label Explanation: 100. Outflow module; 200. Flow module; 300. Inflow module; 400. Review module. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0016] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0017] In the traditional grid connection approval process for new energy projects, the consistency detection mechanism for cross-departmental data and information has significant flaws. Due to divergent understandings and interpretations of key data fields among different approval departments during business operations, these differences fail to be synchronized with the central rule set. Furthermore, legacy data interfaces introduce format variations during information conversion, and data cleaning tools enforce standardization based on the initial rule set, resulting in hidden biases in the numerical expression, precision retention, and contextual semantics of key fields. These biases manifest as surface-level data format matching but inaccurate underlying business meanings, directly impacting the coherence of the approval process, the timeliness of project progress, and the reliability of compliance verification, thereby weakening the decision-making foundation of automated approval systems.

[0018] For example, in the approval process for energy storage grid-connected projects, the technical department entered the original value "50.789MWh" for the "battery pack rated capacity" field, strictly retaining three decimal places required for engineering calculations. However, the regulatory department, based on standard requirements, stored the same field as "50.79MWh," using the convention of rounding to two decimal places. When the data flowed through an early XML-formatted API interface, special unit symbols in the technical department's data were truncated due to character set compatibility limitations. Subsequently, the data cleaning middleware, based on the initial matching rule set, forcibly corrected the technical department's "50.789MWh" to "51MWh" to match the integer value convention used by the finance department. During the final review stage before grid connection, manual comparison of the original technical documents and approval documents revealed differences in the precision of the capacity parameters and ambiguities in unit labeling, forcing the approval process to be interrupted and initiating a data traceability procedure.

[0019] If these issues are not addressed, hidden semantic inconsistencies will continue to accumulate in the cross-departmental data flow chain, leading to frequent manual interventions at critical later stages of the approval process. The resulting repetitive data corrections and cross-departmental coordination will significantly prolong the project grid connection cycle, increase the complexity of compliance verification, and render automated approval mechanisms ineffective due to their inability to identify deep semantic conflicts. Ultimately, these problems will hinder the efficient allocation of resources in new energy projects, increase the uncertainty and risk of project implementation, and restrict the reliable application of intelligent decision-making systems in the approval process.

[0020] For reference, see the appendix. Figure 1This invention provides an intelligent verification method for new energy grid connection data, comprising the following steps: S1. When data flows out from the previous department, semantic metadata is encapsulated on the key data fields to obtain the first semantic data package. The semantic metadata includes the original input numerical information, the original source information, the original format information, the original precision information (the precision here refers to the numerical precision determined based on the original input data, which is only related to the original input value and not to the business meaning; the business meaning has corresponding numerical ranges and precision requirements based on a preset business meaning mapping table), the business meaning, the purpose of use, the version identifier, and the processing conventions; the original source information includes the department identifier, the operator ID, the data timestamp, and the business name. S2. During the flow of the first semantic data packet, record the loss information caused by the data interface limitation, and append the loss information to the first semantic data packet to obtain the second semantic data packet; S3. When the second semantic data packet flows into the next department, interpret the semantic metadata in the second semantic data packet, and based on the preset semantic mapping rules, perform informed data transformation on the key data fields in the second semantic data packet to generate view data that meets the business needs of the next department; S4. Store the first semantic data package and conduct cross-departmental semantic consistency audit based on the semantic metadata and view data in the first semantic data package.

[0021] For ease of understanding, the following explains some key terms in this embodiment: Semantic metadata refers to descriptive information attached to raw data fields. Its purpose is to provide the data's context, source, format, precision, business meaning, purpose of use, version identifier, and processing conventions. Semantic metadata ensures that the original intent and deeper meaning of data are preserved and accurately understood during cross-departmental flow and processing, avoiding data inconsistencies caused by information loss or misunderstanding.

[0022] The first semantic data packet refers to the data structure obtained after semantic metadata encapsulation of key data fields when data flows out from the previous department. This data packet contains the original data and its associated semantic metadata, representing the initial form of data in the cross-departmental flow process and carrying the complete semantic information of the data in the source department.

[0023] Loss information refers to the loss or variation in data content or format caused by data interface limitations (such as character length limitations, data type incompatibility, etc.) during the flow of the first semantic data packet. Loss information is recorded and attached to the semantic data packet. Its purpose is to explicitly mark the changes that may occur in the data during transmission, so that downstream departments can "informedly" compensate or adjust when processing the data.

[0024] Second semantic data packets refer to the data structure after loss information is added during the flow of first semantic data packets. Building upon the first semantic data packet, second semantic data packets further include records of physical or format-level losses that the data may encounter in the transmission link, providing a more comprehensive context for subsequent informed data conversion.

[0025] Semantic mapping rules refer to a pre-defined set of rules used to guide data transformation and interpretation. These rules define the mapping relationships between different departments regarding the business meaning, format, precision, etc., of the same data fields, and how to perform data transformation in specific business scenarios to meet the needs of the receiving department. Semantic mapping rules are the core basis for achieving informed data transformation.

[0026] Informed data transformation refers to the process of intelligently transforming data based on semantic metadata and semantic mapping rules. Unlike traditional mandatory standardization, informed data transformation fully considers the original intent and source of the data, as well as the possible losses that may occur during the flow, thereby generating view data that meets the business needs of the receiving department, while preserving the deep semantic consistency of the data to the greatest extent.

[0027] View data refers to data generated and provided to the next department after being transformed from informed data. View data is customized according to the specific business needs and semantic mapping rules of the receiving department. While meeting the usage habits of the receiving department, it ensures the traceability and consistency of data at the semantic level by associating it with the original semantic data package.

[0028] Cross-departmental semantic consistency audit: This refers to the process of detecting and distinguishing reasonable deviations and deep semantic inconsistencies in data flow across departments by comparing the semantic metadata in the original semantic data package with the transformed view data and its transformation records. This audit mechanism aims to ensure the semantic integrity and accuracy of data throughout its entire lifecycle.

[0029] This embodiment provides an intelligent review method for new energy grid connection data, which aims to solve the problem of deep semantic inconsistencies caused by differences in departmental understanding, data interface limitations, and the mandatory standardization of data cleaning tools during the cross-departmental transfer of new energy grid connection data.

[0030] In step S1, when data flows out from the previous department, semantic metadata is encapsulated on the key data fields to obtain the first semantic data package. The semantic metadata includes original input numerical information, original source information, original format information, original precision information, business meaning, purpose of use, version identifier, and processing conventions. The original source information includes the department identifier, operator ID, data timestamp, and business name.

[0031] Specifically, when any key data field (such as "project installed capacity" or "grid-connected voltage level") is about to flow out from a department's business system, a software component called a "data export agent module" will automatically intervene. This module is typically integrated into the business applications of various departments in the form of a plugin or service interface. For example, this module will be triggered when a user saves or submits "project installed capacity" data in the "grid-connected scheme design system" of the technology department. The data export agent module automatically captures and records the original source information of the data from the current operating environment. This includes, but is not limited to: the department identifier that generated the data (e.g., "technology department", "finance department"), the user ID of the operator, the timestamp of data generation or last modification, and the name of the specific business application module that generated the data (e.g., "technical solution entry module", "financial budget preparation module"). This information is appended to the data in a structured manner (e.g., JSON format key-value pairs) to form part of the metadata. The data export agent module obtains the original format and precision information of the field according to the pre-configured "field semantic configuration table". For example, for the "Project Installed Capacity" field, the configuration table might specify that the technical department's system stores it as a floating-point number, accurate to three decimal places, in "MW". This configuration information is jointly defined by business experts and technical personnel during system deployment and stored in a queryable central configuration service. When data flows out, the module queries this service and appends information such as "floating-point number", "three decimal places", and "MW" as metadata. To address the issue of differing interpretations of the same data fields across departments, the data export agent module appends the specific meaning and purpose of each key field in the sending department's business process, based on the field semantic configuration table. For example, the technical department's "Installed Capacity" field might be defined as "Engineering Design Capacity," used to assess the technical feasibility and safety of grid connection schemes; while the finance department's "Installed Capacity" field might be defined as "Financial Accounting Baseline Capacity," used to calculate return on investment and subsidy amounts. These business meanings are stored in the metadata in the form of text descriptions or predefined codes. The data export agent module maintains a version identifier for each data field and automatically updates it each time data is modified or flows out. Simultaneously, it records any specific processing conventions that the sending department might have for the data. For example, the technical department might have strict precision requirements for certain key technical parameters (such as "inverter efficiency"), stipulating that four decimal places must be retained and rounding is not allowed. These conventions are attached to the metadata in the form of rule identifiers or text descriptions, guiding downstream systems on how to process the data "informedly." Data fields with semantic metadata will be transmitted as a complete "semantic data packet." This data packet can be a composite structure containing raw data and JSON-formatted metadata.

[0032] In step S2, during the flow of the first semantic data packet, loss information caused by data interface limitations is recorded, and the loss information is appended to the first semantic data packet to obtain the second semantic data packet.

[0033] Specifically, data transmission no longer relies directly on inter-departmental database connections, but instead uses a "data relay service." When data needs to be exchanged via legacy application programming interfaces (APIs), a "compatibility adapter module" is deployed between the data relay service and the old API. This adapter module makes necessary adjustments to the original data before transmission (such as truncation) based on the limitations of the old API (e.g., character length limits, character set encoding limitations), while ensuring that the original semantic metadata can be transmitted intact, or explicitly marking the "physical" loss caused by the limitations of the old API in the metadata (e.g., {"original length":20,"actual transmission length":10,"loss type":"truncation"}).

[0034] In step S3, when the second semantic data packet flows into the next department, the semantic metadata in the second semantic data packet is interpreted, and based on the preset semantic mapping rules, the key data fields in the second semantic data packet are transformed into informed data to generate view data that meets the business needs of the next department.

[0035] Specifically, the receiving department's system, through its internal "data receiving agent module," prioritizes parsing the semantic metadata contained in the received semantic data packets. This module reads information such as "original format and precision," "business meaning and intent," and "version and processing conventions" from the metadata. For example, if the received "installed capacity" value is "100 MW," but the metadata explicitly states that the original data is "100.567 MW," and the "original precision" is three decimal places, then the receiving system can immediately identify that the data may have suffered precision loss or truncation during transmission. The receiving system no longer blindly accepts or forcibly standardizes data, but instead intelligently generates a "view data" that meets its own needs based on its own business requirements and preset "semantic mapping rules," using the received original data and semantic metadata for processing. These semantic mapping rules are stored in a "semantic transformation rule engine." For example, the finance department's rule might be defined as: "When receiving the 'engineering design capacity' from the technology department, round it to the nearest integer and convert it into the 'financial accounting baseline capacity.'"

[0036] In step S4, the first semantic data packet is stored, and a cross-departmental semantic consistency audit is performed based on the semantic metadata and view data in the first semantic data packet.

[0037] Specifically, regardless of how data is transformed or views are generated during the data flow, the original semantic data package and its complete metadata are always stored in a "semantic data lake" or "data archive service." This service serves as the single source of truth for all cross-departmental data flows, ensuring that the authenticity and integrity of the data can be traced back at any time. When processing view data, each department's local system retains a unique identifier pointing to the original data package in the semantic data lake for traceability and comparison when needed. A separate "cross-departmental semantic consistency check service" is periodically or triggered at key approval nodes to perform consistency checks on the same key data fields across different departments. This service no longer simply compares the surface values ​​or formats of data fields, but also compares the semantic metadata they carry.

[0038] The following example will provide a more detailed explanation of the above technical solution: Suppose that in the grid connection approval process for a new energy project, the technical department needs to submit "project installed capacity" data, while the finance department needs to use this data for financial accounting. Traditional intelligent review methods for new energy grid connection data suffer from subtle differences in understanding and operational habits of certain key data fields among different approval departments when handling their own business. These differences fail to be reflected in a unified set of rules in a timely manner. Furthermore, legacy data interfaces may cause data loss during information conversion, and data cleaning tools may unintentionally distort the original meaning of data during forced "standardization." This results in approval documents that appear consistent on the surface but actually contain hidden mismatches in deeper semantics and contextual information. Such inconsistencies are often only discovered manually in the later stages of the approval process, severely impacting approval efficiency and project progress.

[0039] In response, this application proposes an intelligent verification method for new energy grid connection data.

[0040] First, in step S1, when an engineer from the technical department enters "Project Installed Capacity" as "100.567 MW" into the "Grid Connection Scheme Design System" and saves it, the "Data Export Agent Module" is triggered. This module automatically captures and encapsulates semantic metadata. For example, the original source information is recorded as: department identifier "Technical Department", operator ID "Engineer_A", data timestamp "2024-05-20 10:00:00", and business name "Grid Connection Scheme Design". Simultaneously, according to the "Field Semantic Configuration Table", the original format information is identified as "floating-point number", the original precision information is "three decimal places", the business meaning is defined as "engineering design capacity", the purpose of use is "evaluating technical feasibility", the version identifier is "V1.0", and the processing convention is "original precision must be retained". This metadata, along with the original value "100.567 MW", is encapsulated into the first semantic data packet.

[0041] Next, in step S2, the first semantic data packet needs to be transmitted to the data relay service through a legacy data interface. Because this old interface has a character length limit for the "Project Installed Capacity" field, for example, it can only transmit integers or one decimal place. At this point, the "Compatibility Adapter Module" intervenes. It recognizes this limitation and truncates the original value "100.567MW" to "100.5MW". Simultaneously, the Compatibility Adapter Module records loss information, such as {"Loss Type":"Precision Truncation","Original Value":"100.567MW","Transmitted Value":"100.5MW","Loss Reason":"Interface Length Limitation"}. This loss information is appended to the semantic metadata of the first semantic data packet, forming the second semantic data packet.

[0042] Subsequently, in step S3, when the second semantic data packet flows into the finance department's "financial accounting system," the "data receiving agent module" interprets the semantic metadata within it. It reads that the original value is "100.567 MW," but the transmitted value is "100.5 MW," and the metadata explicitly records the loss information due to "precision truncation." The finance department's preset semantic mapping rules define: "When receiving the 'engineering design capacity' from the technology department, it should be rounded to the nearest integer and converted into 'financial accounting baseline capacity.'" Based on this information, the data receiving agent module performs informed data conversion. It first identifies the original precision loss, but because the finance department's rule is to round to the nearest integer, it rounds the original "100.567 MW" to "101 MW" and converts its business meaning to "financial accounting baseline capacity." Finally, it generates view data that meets the finance department's business needs: "101 MW," with the business meaning of "financial accounting baseline capacity."

[0043] Finally, in step S4, the original first semantic data packet is stored in the "semantic data lake." A separate "cross-departmental semantic consistency detection service" periodically audits the original first semantic data packet from the technology department and the view data generated by the finance department. This service compares the technology department's original "project installed capacity" (100.567 MW, business meaning "engineering design capacity") with the finance department's view data (101 MW, business meaning "financial accounting baseline capacity"). By examining the loss information in the semantic data packet and the finance department's conversion records, the audit service finds that although the values ​​are different, this difference is due to precision truncation caused by interface limitations and the finance department's "rounding to integers" semantic mapping rule, which is a reasonable business conversion rather than a deep semantic inconsistency. Therefore, the audit result shows consistency.

[0044] The above technical solution solves the problem of deep semantic inconsistency caused by differences in understanding, interface loss, and data cleaning in cross-departmental data flow by encapsulating semantic metadata, recording loss information, converting informed data, and verifying consistency.

[0045] Specifically, in step S1, by encapsulating key data fields with semantic metadata, the complete context and intent of the original data are captured and preserved. For example, the original precision information independently defines business-independent precision based on the original input values, and combined with the business meaning mapping table, it ensures that the numerical range and precision requirements are not misunderstood, thereby preventing initial deviations caused by differences in understanding and emphasis between departments. Compared to traditional methods where only the original values ​​are transmitted when data flows out, which is prone to misunderstanding or forced standardization by downstream departments due to a lack of contextual information, this application ensures the integrity of data semantics from the source by attaching rich semantic metadata.

[0046] In step S2, by recording loss information caused by data interface limitations and appending it to the first semantic data packet, information loss during interface conversion, such as format changes or data truncation, is explicitly recorded and preserved. This facilitates the identification and compensation of these losses in subsequent steps, avoiding hidden semantic distortions introduced by interface incompatibility. In traditional methods, losses caused by data interface limitations are often implicit and imperceptible to downstream departments, leading to data being misprocessed without their knowledge. This application improves the transparency and traceability of data flow by explicitly recording loss information.

[0047] In step S3, by interpreting the semantic metadata in the second semantic data packet and performing informed data transformation based on preset semantic mapping rules, view data that meets the business needs of the next department is generated. This ensures that the data transformation is based on the original metadata and dynamic rules rather than forced standardization. For example, the business meaning and purpose of use in the semantic metadata guide the transformation, thereby maintaining deep semantic consistency and department-specific needs. Traditional data cleaning tools often perform forced "standardization," erasing key contextual information and department-specific business meanings, leading to deep semantic distortion. The informed data transformation of this application can intelligently adjust the data based on the actual needs of the receiving department while preserving the original semantics.

[0048] In step S4, a first semantic data packet is stored, and a cross-departmental semantic consistency audit is performed based on the semantic metadata and view data in the first semantic data packet. Deep inconsistencies are detected by comparing the original context and the transformation results. For example, reasonable deviations and semantic conflicts are identified based on version identifiers and processing conventions in the metadata, ensuring that the audit process comprehensively covers the entire data flow chain. Traditional consistency detection mechanisms mainly rely on surface numerical or format comparisons, which cannot detect hidden inconsistencies caused by semantic distortion. This application, by combining the original semantic metadata and transformation records for auditing, can distinguish between reasonable deviations and deep semantic inconsistencies, significantly improving the accuracy and efficiency of the audit.

[0049] In some embodiments, the specific steps in step S1 include: S11. Based on the business meaning, determine the corresponding numerical range or precision requirement through a preset business meaning mapping table; S12. Based on the original input values ​​in the original input numerical information, and based on the determined numerical range or precision requirements, assess whether there is a semantic deviation between the original input values ​​and the business meaning, and obtain the semantic deviation assessment result. S13. When the semantic deviation assessment results show that there is semantic deviation, display the intent clarification interface to the operator; the intent clarification interface provides a list of alternative business intents; S14. Receive the service intent selected by the operator from the list of alternative service intents; S15. Based on the business intent selected by the operator, reconstruct the semantic metadata of the key data fields, and encapsulate the first semantic data packet based on the reconstructed semantic metadata.

[0050] In some implementations of the above method, the step of semantic metadata encapsulation of key data fields in the data when data flows out from the previous department can be further refined. Specifically, in step S11, the corresponding numerical range or precision requirement is determined by a preset business meaning mapping table based on the business meaning. The business meaning can refer to the business context and purpose of the data, such as predefined enumerated values, like "engineering design capacity," "financial accounting benchmark capacity," or "maximum grid-connected injection capacity," or it can be a semantic concept identifier defined based on ontology or knowledge graph. The preset business meaning mapping table is used to store the configuration table of the relationship between business meaning and data attributes (such as numerical range and precision). It can be stored in a relational database and includes fields such as field name, business meaning ID, numerical range (minimum value, maximum value), and precision requirement (decimal places, significant digits), or it can exist in the form of an XML or JSON configuration file, maintained and updated by business experts. By querying this mapping table, the numerical range and precision constraints associated with a specific business meaning can be obtained.

[0051] In step S12, based on the original input values ​​in the original input value information, and based on a determined value range or precision requirement, an assessment is made to determine whether there is a semantic deviation between the original input values ​​and the business meaning, thus obtaining a semantic deviation assessment result. The original input values ​​refer to the data values ​​initially entered by the user or system, which can be directly obtained from the user interface input box or received from an external system interface. The assessment process may include writing a data validation function to check whether the value is within a specified range and to calculate the difference between the actual precision and the required precision. Specifically, the data validation function is typically a piece of program code that receives the original input values ​​and the value range and precision requirements obtained from the business meaning mapping table as input parameters. For the evaluation of the value range, the function directly compares whether the original input values ​​fall between a preset minimum and maximum value. For example, if the business meaning requires the value range of "project installed capacity" to be 50 MW to 200 MW, while the original input value is 250 MW, the validation function will determine that the value exceeds the preset range. For accuracy requirement assessment, the function calculates the actual precision of the original input value (e.g., by parsing the numeric string or floating-point representation to determine the number of decimal places) and then compares it with the precision required by the business meaning. For example, if the business meaning requires the precision of "wind turbine blade length" to three decimal places, while the original input value "80.5 meters" only retains one decimal place, a precision mismatch will be identified. Data validation functions are existing technology and will not be elaborated here. Alternatively, a rule engine can be used to define the numerical range and precision requirements as rules to match and judge the original values. The semantic deviation assessment result can be a Boolean value indicating whether a deviation exists, along with the deviation type (e.g., "out of range," "precision mismatch"), or a structured report containing the deviation level, deviation description, and suggested handling methods.

[0052] In step S13, when the semantic deviation assessment result indicates the existence of semantic deviation, the system displays an intent clarification interface to the operator. This intent clarification interface is a user interface used to interact with the operator and obtain their true intent. It can be presented as a pop-up dialog box, containing raw data, default business meaning, deviation prompts, and a candidate list, or as a form area on a web page that dynamically loads candidate business intents. The intent clarification interface provides a list of candidate business intents. These candidate intents can be dynamically obtained from a preset "policy semantic management platform," either as an enumeration list of business meanings related to the current data type or project type, or as a list of business intents recommended by a machine learning model based on raw data and contextual information, sorted by relevance.

[0053] In step S14, the system receives the business intent selected by the operator from the list of alternative business intents. This can be achieved by capturing user clicks or selections through a user interface event listener to obtain the selected business intent ID or text, or by receiving form data submitted from the front-end interface through an API interface.

[0054] In step S15, based on the business intent selected by the operator, the semantic metadata of the key data fields is reconstructed, and the first semantic data packet is encapsulated based on the reconstructed semantic metadata. The reconstruction process can either directly write the business intent selected by the operator into the "Business Meaning" field of the semantic metadata and record an "Intent Clarification Record," or, based on the selected business intent, re-query the "Field Semantic Configuration Table" to obtain the numerical range, precision requirements, processing conventions, etc., associated with the new business intent, and update them in the semantic metadata. Finally, the original data and the updated semantic metadata are combined into a complete "first semantic data packet," for example, encapsulated into a composite JSON object, or transmitted as the message payload and message header in a message queue service.

[0055] This application's solution introduces a human-computer interaction clarification mechanism, allowing operators to explicitly select or confirm the true business intent of data when the system detects a deviation. This ensures that the encapsulated semantic metadata accurately reflects the actual intent carried by the data fields, eliminating hidden semantic deviations at the source. Specifically, when data flows out from the previous department and is ready for semantic metadata encapsulation, the system first retrieves the corresponding numerical range or precision requirements from a preset business meaning mapping table based on the initial business meaning of the data fields. These requirements serve as objective standards for subsequent semantic deviation assessment. Subsequently, the system compares the original input values ​​with these determined numerical ranges or precision requirements to assess whether there is a semantic deviation between the original input values ​​and the business meaning. Once the assessment result indicates a semantic deviation, the system does not immediately encapsulate the data but pauses the automated process and displays an intent clarification interface to the operator. This interface not only highlights potential deviations but, more importantly, provides a list of alternative business intents, allowing operators to select based on their true intent. Upon receiving the operator's explicitly selected business intent, the system reconstructs the semantic metadata of key data fields based on this selection, ensuring the accuracy of the "business meaning" field. Finally, based on the reconstructed semantic metadata, the first semantic data packet is encapsulated.

[0056] Through the aforementioned mechanism, this application effectively addresses the issue of discrepancies between semantic metadata and actual business intent caused by policy updates and system lags in the intelligent review method for new energy grid connection data. This solution, combined with a basic semantic metadata encapsulation method, ensures that the semantic metadata generated during data outflow not only includes original input numerical information, original source information, original format information, original precision information, business meaning, purpose of use, version identifier, and processing conventions, but also guarantees the accuracy and authenticity of the "business meaning." This significantly improves the reliability of the first semantic data packet, providing a more solid and accurate foundation for subsequent data flow processes, including recording loss information, informed data conversion in the next department, and ultimately cross-departmental semantic consistency review. This significantly enhances the accuracy and intelligence level of the entire intelligent review method for new energy grid connection data.

[0057] The following is a specific example to illustrate this. As a concrete implementation, when a key data field in new energy grid connection data, such as "installed capacity," is entered or modified by an engineer in the technical department's "grid connection scheme design application" and an attempt is made to save it, a data export proxy module immediately initiates monitoring of this operation. This module first obtains the currently input value, such as "99.850 MW," and performs a preliminary comparison with the default business meaning preset for that field in the system's internally maintained "field semantic configuration table," such as "engineering design capacity." The data export proxy module contains a "numerical range and intent association rule set," which may define the typical numerical range of "engineering design capacity" (e.g., for wind power projects, typically between 50 MW and 200 MW, and the value is usually an integer or one decimal place). When the module detects a significant difference between the number of decimal places (three) in the input "99.850 MW" and the default precision requirement for "engineering design capacity" (e.g., usually an integer or one decimal place), it preliminarily determines that semantic drift may exist.

[0058] At this point, the data export agent module will immediately pause the automatic metadata encapsulation operation and display a dynamic intent clarification interface to the operations engineer. This interface will appear as a pop-up dialog box on the engineer's "Grid Connection Scheme Design Application" front end, clearly displaying the engineer's currently input value "99.850 MW" and the system's inferred default business meaning, "Engineering Design Capacity." More importantly, this interface will provide a drop-down list or a set of radio buttons containing a series of alternative business intents closely related to current renewable energy grid connection policies and regulations, such as "Engineering Design Capacity," "Maximum Grid Connection Injection Capacity," and "Initial Planned Capacity." The engineer must clearly select the option that best represents the true intent of this data input from these choices.

[0059] Suppose that the engineer, based on the actual situation, selected "maximum grid-connected injection capacity" as the true business intent for "99.850 MW". The data export agent module will immediately reconstruct the semantic metadata of this data field based on this explicit user selection. The new semantic metadata will include: original source department: "Technical Department", original source system: "Grid Connection Scheme Design System", original value: "99.850", original format: "floating-point number", original precision: "three decimal places", original unit: "MW", business meaning: "maximum grid-connected injection capacity", processing convention: "actual grid-connected capacity limited by the grid, accurate to three decimal places", data version: "V1.1", and intent clarification record, which details the original inferred intent, user-selected intent, clarification time, and operator ID. Finally, based on the reconstructed semantic metadata, the first semantic data packet is encapsulated, which will accurately reflect the engineer's true business intent.

[0060] Through the above technical solution, this application effectively solves the problem of hidden semantic discrepancies between the business meaning and actual intent of data fields caused by policy updates and system lags in traditional methods. This solution introduces a human-computer interaction clarification mechanism before data encapsulation, ensuring that semantic metadata accurately reflects the actual intent carried by the data fields, thereby eliminating potential semantic inconsistencies at the source. Specifically, a pre-set business meaning mapping table provides an objective and quantifiable standard for assessing semantic discrepancies, avoiding errors caused by subjective judgment. Evaluating the original input values ​​against their business meanings automatically identifies potential inconsistencies, effectively preventing erroneous or ambiguous data from entering subsequent processes. When a discrepancy is detected, an intent clarification interface is displayed to the operator, providing a list of alternative business intents, enabling the operator to proactively clarify the true intent of the data, resolving ambiguities that may arise from human input. Receiving the business intent selected by the operator ensures the correctness of the data intent, avoiding secondary discrepancies that may result from blind system corrections. Finally, the semantic metadata is reconstructed based on the business intent selected by the operator and encapsulated to obtain the first semantic data packet, ensuring the accuracy and reliability of the semantic metadata and providing a solid foundation for subsequent data flow, informed data conversion, and cross-departmental semantic consistency audits. Therefore, the solution proposed in this application significantly improves the accuracy, reliability, and intelligence of the intelligent review method for new energy grid connection data, and effectively reduces the approval risk and rework cost caused by semantic inconsistencies.

[0061] In some embodiments, the specific steps in step S2 include: S21. For complex data structures contained in key data fields, extract the original structural feature information of the complex data structures; the original structural feature information includes the hierarchical relationship of the complex data structures, the number of internal fields, and the identifiers of key related fields; S22. Before the first semantic data packet is transferred to the data interface through the compatibility adapter module, the compatibility adapter module performs pre-verification on the complex data structure in the first semantic data packet according to the structural constraint specifications of the data interface, and obtains the pre-verification result; S23. When the pre-verification result shows that the complex data structure does not conform to the structural constraint specification of the data interface, the compatibility adapter module performs structural adjustment on the complex data structure, identifies the structural loss generated during the structural adjustment process, and records it as structural loss information; the structural loss information includes the structural path where the loss occurs, the type of loss, and the cause of the loss. S24. The compatibility adapter module appends the structural loss information to the semantic metadata of the first semantic data packet in a path-based manner to obtain the second semantic data packet.

[0062] Specifically, for complex data structures contained in key data fields, the original structural feature information of these complex data structures is extracted to comprehensively capture the original structural form of the data in the source department. Complex data structures typically refer to data organization forms with nested, correlated, or multidimensional characteristics, such as JSON objects, XML documents, or database records with parent-child relationships. Original structural feature information is metadata describing the inherent attributes of these complex data structures, serving as a benchmark for subsequent data transformation and loss identification. The hierarchical relationships can be described using tree structures, path expressions (such as XPath or JSONPath), or flattened mapping tables to clearly show the nesting and subordinate relationships between data elements. The number of internal fields refers to the total number of independent data items contained in the complex data structure, which can be counted by traversing the structure nodes. The key related field identifier refers to the field used in the complex data structure to establish logical relationships between data or uniquely identify data entities, such as primary keys, foreign keys, or unique business codes; its extraction can be achieved through preset field rules or pattern matching.

[0063] Before the first semantic data packet is transferred to the data interface via the compatibility adapter module, the compatibility adapter module pre-validates the complex data structures in the first semantic data packet according to the structural constraints of the data interface, obtaining a pre-validation result. The compatibility adapter module is a software component located between the data relay service and the target data interface; its core function is to coordinate data format and structural differences between different systems. The structural constraints of the data interface refer to the constraints imposed by the target system or API on the received data structure, such as field length limits, data type requirements, whether nested structures are supported, and the mandatory nature of specific fields. Pre-validation refers to a simulated check of the data structure before actual data transmission or conversion to assess whether it conforms to the target interface's specifications. The pre-validation result can be a Boolean value (compliant / non-compliant) or it can include a detailed violation report indicating specific incompatible structural elements.

[0064] When the pre-verification result shows that the complex data structure does not conform to the structural constraints of the data interface, the compatibility adapter module performs structural adjustments on the complex data structure, identifies the structural losses generated during the adjustment process, and records them as structural loss information. Structural adjustment refers to modifications made to the complex data structure to conform to the target interface specification, such as flattening nested structures, truncating excessively long fields, deleting incompatible fields, or merging multiple fields. Structural loss refers to the phenomenon where the integrity, hierarchical relationship, or internal association information of the data structure changes or is lost due to the constraints of the target interface during the structural adjustment process. The structural path where the loss occurs refers to the specific location of the loss within the complex data structure, which can be precisely indicated by a path expression. The loss type describes the nature of the loss, such as "field truncation," "hierarchical flattening," "field deletion," or "data type conversion." The loss cause explains the specific constraints that led to the loss, such as "target interface field length limitation," "target interface does not support nested structures," or "target interface does not support this data type."

[0065] The compatibility adapter module appends the structural loss information to the semantic metadata of the first semantic data packet in a path-based manner to obtain the second semantic data packet. The path-based manner means adding the structural loss information to the semantic metadata in a structured form (e.g., nested JSON objects or XML elements), and this information explicitly points to the specific location in the original data structure where the loss occurred. In this way, the loss information is closely associated with the original data and its semantic metadata, forming a complete data packet containing the original data, the original semantic metadata, and detailed loss records, i.e., the second semantic data packet.

[0066] This application's solution fundamentally changes the traditional physical loss recording model, which only focuses on numerical or textual changes, by performing refined structural feature extraction, pre-verification, structural adjustment, and loss recording on complex data structures during data flow. Instead, it proactively manages the structural variations of complex data structures during transmission. First, by extracting original structural feature information, a fundamental understanding of the data structure is provided for subsequent operations, avoiding the omission of loss details due to structural complexity. Second, before the first semantic data packet is transferred, a compatibility adapter module performs pre-verification according to the structural constraints of the data interface, identifying structural incompatibility risks in advance and preventing unexpected losses during conversion. Next, when the pre-verification result indicates that the structure does not conform to the specification, the compatibility adapter module performs structural adjustment and identifies the resulting structural loss, recording information including the loss structure path, type, and cause. This ensures that the loss during the structural adjustment process is captured in detail without losing contextual details. Finally, the compatibility adapter module appends the structural loss information to the semantic metadata in a path-based manner, associating the loss information with the original data, facilitating traceability and auditing in subsequent flows.

[0067] This solution, combined with the aforementioned intelligent auditing method for new energy grid connection data, encapsulates key data fields with semantic metadata as data flows out from the previous department, resulting in a first semantic data package. Building upon this, the solution further enhances semantic integrity assurance during data flow. By meticulously recording structural losses in complex data structures, even if data changes physically due to interface limitations, its original structural intent and detailed loss information are fully transmitted, ensuring deep semantic integrity. This enables subsequent cross-departmental semantic consistency auditing (S4) to be based on a more comprehensive and informed semantic data package. It not only compares surface values ​​but also deeply analyzes the impact of structural losses on data semantics, thereby more accurately identifying deep semantic inconsistencies and improving the accuracy and reliability of the entire intelligent auditing method.

[0068] In some embodiments, the specific steps in step S3 include: S31. Parse the semantic metadata in the second semantic data packet and identify the unstructured policy supplementary explanations contained in the semantic metadata; S32. Based on the business meaning in the semantic metadata and the supplementary explanation of unstructured policies, obtain the dynamic calculation rules related to the key data fields; S33. Based on the dynamic calculation rules, determine whether it is necessary to obtain external real-time dynamic data; S34. When it is determined that external real-time dynamic data needs to be obtained, a request is sent to the external data service to obtain the external real-time dynamic data. S35. Adjust the preset semantic mapping rules based on the dynamic calculation rules, external real-time dynamic data, and the original data in the second semantic data packet; S36. Based on the adjusted semantic mapping rules, perform informed data transformation on the key data fields in the second semantic data packet to generate view data that meets the business needs of the next department; S37. Record the process of adjusting semantic mapping rules, acquiring information on external real-time dynamic data, and the process of informed data transformation.

[0069] First, in step S31, parsing the semantic metadata in the second semantic data packet refers to performing structured analysis on the received second semantic data packet to extract the various metadata information it contains. This can be achieved using various techniques, such as extracting fields from JSON-formatted metadata using a JSON parser, or traversing nodes from XML-formatted metadata using an XML parser. The purpose is to obtain structured information carried in the data packet, such as original input numerical information, original source information, original format information, original precision information, business meaning, purpose of use, version identifier, and processing conventions. Identifying the unstructured policy supplementary explanations contained in the semantic metadata refers to the system's ability to extract policy-related descriptions or explanations in unstructured, free-text form from the metadata during the process of parsing the semantic metadata. This can be achieved through text analysis techniques, such as using regular expressions to match predefined keywords or patterns, or using Natural Language Processing (NLP) techniques to perform semantic analysis and entity recognition on the text content to identify policy-related supplementary information. This unstructured information is usually a further supplement and refinement of the structured metadata, reflecting policy requirements or special processing rules under specific business scenarios.

[0070] Secondly, in step S32, based on the business meaning in the semantic metadata and the unstructured policy supplementary explanation, dynamic calculation rules related to the key data field are obtained. This means that the system uses the parsed structured business meaning and unstructured policy supplementary explanation to retrieve and determine the calculation logic applicable to the current key data field from a preset rule base or knowledge network. This can be achieved using a rule engine-based matching mechanism. For example, the business meaning and policy supplementary explanation can be used as query conditions to query the "policy semantic knowledge network" to obtain the latest calculation formula or processing flow related to a specific data field (such as "installed capacity"). Another implementation method is to train a machine learning model on historical data and policy text, enabling it to dynamically recommend or generate calculation rules based on input information.

[0071] Next, in step S33, based on the dynamic calculation rules, it is determined whether external real-time dynamic data needs to be obtained. This means that the system analyzes whether the execution of the dynamic calculation rules obtained in step S32 depends on real-time information provided by external, non-local data sources. For example, if the dynamic calculation rule includes an operation such as "multiply by the regional power grid absorption coefficient," the system will determine whether the "regional power grid absorption coefficient" is external real-time dynamic data. This can be achieved through a rule parser, which parses the rule expression and identifies whether it contains references to specific external data variables. Alternatively, a predefined external data dependency table can be used to associate the dynamic calculation rules with the required external data sources for judgment.

[0072] Furthermore, in step S34, when it is determined that external real-time dynamic data is needed, a request is sent to an external data service to obtain the external real-time dynamic data. This means that after confirming the need for external real-time dynamic data, the system actively establishes a connection with the external data provider and requests the data. This can be done using various communication protocols and interface standards, such as sending a GET request to a RESTful API via HTTP / HTTPS, or calling a Web Service interface via SOAP. The request typically includes necessary parameters, such as data type, time range, and geographical location. The external data service could be a real-time power consumption data interface provided by a power grid company, or a real-time weather data interface provided by a meteorological bureau, etc.

[0073] Furthermore, in step S35, adjusting the preset semantic mapping rules based on the dynamic calculation rules, the external real-time dynamic data, and the original data in the second semantic data packet refers to the system comprehensively utilizing the dynamic calculation rules, the real-time data obtained from the outside, and the original data contained in the second semantic data packet to modify or supplement the original, static semantic mapping rules. This can be achieved using the dynamic configuration function of a rule engine, for example, loading new rule fragments or modifying the parameters of existing rules at runtime. Another implementation method is to use a "semantic mapping rule adaptive orchestration engine" to intelligently orchestrate new data transformation logic processes based on the dynamic calculation rules and external data, thereby achieving the adjustment of the preset rules.

[0074] Subsequently, in step S36, based on the adjusted semantic mapping rules, informed data transformation is performed on the key data fields in the second semantic data packet. This refers to the system performing a purposeful and evidence-based transformation of the original data in the second semantic data packet according to the semantic mapping rules updated or generated in step S35. This transformation is no longer a simple format or numerical alignment, but fully considers the business meaning of the data, policy requirements, and real-time external factors. For example, if the adjusted rules require multiplying the "engineering design capacity" by the "regional power grid absorption coefficient" and rounding it to the nearest integer, the system will strictly follow this logic. This can be achieved through a data transformation engine, which can interpret and execute complex transformation logic, including numerical calculations, unit conversions, and conditional judgments. Generating view data that meets the business needs of the next department means that after informed data transformation, the data format and content output by the system can directly meet the business processing requirements of the receiving department. For example, if the next department is the finance department, its business needs may require the "installed capacity" to be expressed in integer form with a business label of "subsidy calculation capacity". View data is the result of semantic enrichment and transformation of raw data, providing the receiving department with a data perspective that is more business-valued and compliant.

[0075] Finally, in step S37, recording the adjustment process of the semantic mapping rules, the acquisition information of the external real-time dynamic data, and the informed data conversion process refers to the system performing detailed log recording and audit tracking of the entire dynamic adjustment and data conversion process. This includes recording the specific content of the rule adjustments (e.g., which rules were modified, and the values ​​before and after the modifications), the request parameters for external data acquisition, the response results, the data source information, the timestamp, and the inputs, outputs, executed calculation formulas, and parameters of the informed data conversion. These records can be stored in a database, log file, or distributed ledger (such as a blockchain) to ensure data traceability, transparency, and compliance, facilitating subsequent auditing and problem investigation.

[0076] This application's solution, through a series of closely linked steps, achieves dynamic, intelligent, and compliant data transformation of new energy grid connection data during cross-departmental transfer. When the second semantic data packet flows into the next department, the system first parses its semantic metadata, extracting not only structured information but, more importantly, identifying any unstructured policy supplementary explanations it may contain. This identification process is crucial for initiating the subsequent dynamic adjustment mechanism, enabling the system to capture the latest policy requirements or special business practices that are difficult to cover with traditional static rules. Subsequently, the system combines these structured business meanings with unstructured policy supplementary explanations, querying and retrieving dynamic calculation rules related to the current key data fields from the "policy semantic knowledge network." These rules are no longer preset fixed logic but are generated based on real-time policies and business context. Based on the acquired dynamic calculation rules, the system intelligently determines whether external real-time dynamic data needs to be introduced. This on-demand acquisition mechanism avoids unnecessary external data calls and improves efficiency. Once it is determined that it is necessary, the system proactively sends requests to external data services to obtain the latest real-time data, such as the grid absorption coefficient. This external real-time data provides the latest environmental parameters for data transformation, ensuring the timeliness and accuracy of the transformation results. After acquiring dynamic calculation rules and external real-time dynamic data, the system uses this information, along with the original data in the second semantic data packet, to adjust the preset semantic mapping rules. This adjustment process is the core innovation of this solution, enabling semantic mapping rules to adaptively update based on the latest policies, business implications, and real-time external conditions, thus overcoming the problems of traditional static rules being outdated or inapplicable. Finally, based on these adjusted semantic mapping rules, the system performs informed data transformation on key data fields, generating view data that meets the business needs of the next department. This transformation is "informed" because it fully considers the multi-dimensional semantic information of the data, policy requirements, and real-time external impacts, ensuring the accuracy and compliance of the output data. Throughout the process, the system also records in detail the adjustment process of semantic mapping rules, the acquisition information of external real-time dynamic data, and the informed data transformation process. These records form a complete audit chain, providing a transparent and traceable basis for subsequent cross-departmental semantic consistency audits, greatly enhancing the reliability and compliance of data processing. This solution forms a beneficial combination with the basic semantic data packet encapsulation and flow mechanism. By encapsulating data with semantic metadata, rich data context information is provided, and loss information during the flow process is recorded. Building upon this foundation, this solution leverages rich semantic metadata (including business implications and unstructured policy supplements) and combines it with real-time external data to dynamically adjust the informed data transformation rules as data flows into the next department. This dynamic adjustment ensures that data transformation maintains semantic accuracy and compliance even under conditions of rapid policy changes and external uncertainty.Therefore, this solution not only solves the problems of outdated preset rules and missing external data, but also makes the final generated view data more real-time and accurate, thus providing high-quality input for subsequent cross-departmental semantic consistency audits and significantly improving the effectiveness and reliability of the entire intelligent audit method for new energy grid connection data.

[0077] The following is a concrete example to illustrate this. Suppose the technical department transmits a key data field regarding "project installed capacity" to the finance department. The original input value for this field is "100.567 MW," with a business meaning of "engineering design capacity." During the data flow, the semantic metadata contained in the second semantic data packet, in addition to structured information, also identifies an unstructured policy supplement: "The subsidy calculation capacity for this project needs to be calculated in conjunction with the daily regional grid absorption coefficient." After receiving this second semantic data packet, the system first parses its semantic metadata and identifies the aforementioned unstructured policy supplement. Subsequently, based on the business meaning of "engineering design capacity" and the unstructured policy supplement, the system queries the "policy semantic knowledge network" to obtain dynamic calculation rules related to "project installed capacity," such as: "Subsidy calculation capacity = engineering design capacity × regional grid absorption coefficient." Based on this dynamic calculation rule, the system determines that it needs to obtain the external real-time dynamic data of "regional grid absorption coefficient." At this point, the system sends a request to an external data service, such as a request to the power grid data platform via a "policy-driven real-time external data service agent," to obtain the regional power grid absorption coefficient for the day, assuming the obtained value is 0.95. Next, the system adjusts the preset semantic mapping rules based on the obtained dynamic calculation rules (V_subsidy = V_engineering design × C_absorption), external real-time dynamic data (C_absorption = 0.95), and the original data in the second semantic data packet (V_engineering design = 100.567MW). Specifically, the system will program new conversion logic, multiplying the original "engineering design capacity" by the real-time obtained "regional power grid absorption coefficient," and may round or truncate it according to the needs of the finance department. Based on the adjusted semantic mapping rules, the system performs informed data conversion on the "project installed capacity." For example, it calculates 100.567MW × 0.95 = 95.53865MW and rounds it to 95.538MW. This 95.538MW figure represents the "Subsidy Calculation Capacity" view data that meets the business needs of the finance department. Finally, the system meticulously records the entire process, including the original data packet ID, the original business meaning "Engineering Design Capacity," the parsed policy supplementary explanation "The subsidy calculation capacity for this project needs to be calculated in conjunction with the daily regional power grid absorption coefficient," the policy semantic knowledge network matching rule ID "R_Subsidy Calculation_Absorption Coefficient," external data source call records (e.g., the data source is "Regional Power Grid Absorption Coefficient API," request time, returned value 0.95, data version 20240520), the executed calculation formula V_Subsidy = V_Engineering Design × C_Absorption, and the final view data 95.538MW. This information is stored in the "Semantic Audit Log," ensuring the transparency and traceability of the data transformation process.

[0078] Through the above technical solution, this application effectively solves the problems in intelligent review methods for new energy grid connection data, such as the lag or inapplicability of preset semantic mapping rules due to rapid updates in policies and regulations, and the lack of external real-time dynamic data support for informed data conversion. Specifically, by identifying unstructured policy supplementary explanations in semantic metadata and obtaining dynamic calculation rules, this solution can respond to policy changes in real time, ensuring that the data conversion logic is always consistent with the latest policy requirements. Simultaneously, by acquiring external real-time dynamic data on demand according to dynamic rules, the data conversion process can fully consider the real-time impact of the external environment, significantly improving the timeliness and accuracy of the generated view data. By dynamically adjusting the preset semantic mapping rules, this solution achieves the adaptability of data conversion, avoiding semantic deviations and compliance risks caused by rigid rules. Furthermore, detailed recording of the entire adjustment and conversion process provides a solid and transparent audit basis for cross-departmental semantic consistency review, greatly enhancing the reliability and traceability of data processing. These improvements enable the receiving system to accurately perform informed data conversion, ensuring that the generated view data not only meets its business needs but is also highly accurate semantically and meets the latest compliance requirements, thereby significantly improving the efficiency and quality of intelligent review of new energy grid connection data.

[0079] In some embodiments, step S4, which involves performing a cross-departmental semantic consistency audit based on the semantic metadata and view data in the first semantic data packet, includes: S51. Obtain the project type, department identifier, and data version of the project to be reviewed; S52. Obtain the policy review rule set based on project type, department identifier, and data version; S53. Based on the policy review rule set, compare the semantic metadata in the first semantic data packet with the view data and its transformation records to obtain the comparison result; S54. Based on the comparison results and conversion records, distinguish between reasonable deviations and deep semantic inconsistencies between semantic metadata and view data in the first semantic data package; S55. Generate an audit report for deep semantic inconsistencies.

[0080] To implement the above review process, it is first necessary to obtain the project type, department identifier, and data version of the project to be reviewed. The project type refers to the specific classification of the new energy project, such as wind power, photovoltaic, energy storage, or multi-energy complementary projects. This can be obtained by extracting predefined classification tags from the metadata of the project management system or data package, or by identifying the project name through keyword matching. The department identifier is a unique identifier for the data source or target department, such as the technical department, finance department, legal department, or dispatch center. This can be obtained as a string encoding, such as "TECH_DEPT" or "FIN_DEPT," or it can be a system user group ID. The data version refers to the status identifier of the data at a specific point in time or after a specific batch of modifications. This can be obtained as a timestamp, a sequence number, or a hash value. Obtaining this information aims to provide precise context for subsequent reviews, ensuring the review is targeted.

[0081] Based on this, and according to the acquired project type, department identifier, and data version, the system can dynamically obtain a policy review rule set. The policy review rule set is a collection of logical rules used to assess the compliance and consistency of new energy grid connection data. These rules are dynamic and can be customized according to project type, department, and data version. For example, for wind power projects, there might be a set of rules regarding turbine parameters and grid connection voltage; for photovoltaic projects, there might be rules regarding component efficiency and inverter configuration. For data from the finance department, the focus might be on cost accounting and subsidy calculation rules; for the technology department, the focus might be on engineering parameters and technical specifications. These rule sets can be stored in the rule engine, defined in XML, JSON, or domain-specific languages ​​(DSLs), and retrieved through a query service based on context parameters.

[0082] Subsequently, based on the acquired policy review rule set, the system compares the semantic metadata in the first semantic data packet with the view data and its transformation records to obtain the comparison results. Comparison refers to comparing two or more data items to discover their consistency, differences, or potential conflicts. The semantic metadata in the first semantic data packet is the original data and its accompanying meta-information after semantic metadata encapsulation when the data flows out of the source department. It includes the original input numerical information, original source information, original format information, original precision information, business meaning, purpose of use, version identifier, and processing conventions, providing the most original and comprehensive semantic context of the data. View data is the data generated to meet the business needs of the next department after the second semantic data packet flows into it and undergoes informed data transformation. Transformation records refer to all data processing operations, parameter adjustments, loss information, and semantic mapping decisions automatically recorded by the system throughout the entire transformation process from the first semantic data packet to the second semantic data packet and then to the view data. For example, if the original data is truncated, rounded, unit converted, or the business meaning is reinterpreted, these operations will be recorded in detail. The comparison can include numerical comparison, format comparison, semantic meaning comparison, precision comparison, etc.

[0083] Furthermore, based on the comparison results and the conversion records, the system can distinguish between reasonable deviations and deep semantic inconsistencies between the semantic metadata in the first semantic data packet and the view data. Reasonable deviations refer to acceptable differences that arise during data flow and conversion between different departments due to varying business needs, format specifications, or accuracy requirements, but are clearly defined and recorded. For example, if the technology department's "100.567 MW" is "knowingly" converted to "101 MW" (rounded) by the finance department, and this conversion conforms to preset semantic mapping rules and is recorded, it is considered a reasonable deviation. Deep semantic inconsistencies refer to substantial deviations in the core business meaning, original intent, or key attributes of data during flow and conversion due to information loss, misunderstanding, improper processing, or unrecognized semantic drift, and such deviations do not conform to any preset reasonable conversion rules. For example, the technology department's "engineering design capacity" is incorrectly interpreted as "maximum grid-connected injection capacity," or an unrecorded truncation occurs during the conversion process, distorting the meaning of the data.

[0084] Finally, the system will generate an audit report on the deep semantic inconsistencies. The audit report is a detailed document documenting the audit process, comparison results, identified deep semantic inconsistencies, their possible causes, and recommended corrective actions. This report aims to provide decision support for human auditors, helping them quickly locate problems, understand their root causes, and take corrective measures. The audit report can be output as a structured document (such as PDF or HTML) or a data interface (such as JSON), containing information such as inconsistent data fields, original values, view values, transformation records, policy and rule references, inconsistency type, and severity.

[0085] This application's solution effectively addresses the problems of traditional static audit rules being outdated and unable to adapt to changing policies and departmental differences in interpretation by introducing dynamic context awareness and informed comparison mechanisms into the basic cross-departmental semantic consistency audit process. When data flows out of one department, key data fields are encapsulated with semantic metadata to obtain a first semantic data packet. This packet carries the original input numerical information, original source information, original format information, original precision information, business meaning, purpose of use, version identifier, and processing conventions, laying the foundation for subsequent in-depth audits. During the flow of the first semantic data packet, loss information caused by data interface limitations is recorded and appended to the first semantic data packet to obtain a second semantic data packet. This ensures that any changes to the data during transmission are traceable. When the second semantic data packet flows into the next department, the semantic metadata in the second semantic data packet is interpreted, and based on preset semantic mapping rules, informed data transformation is performed on the key data fields in the second semantic data packet to generate view data that meets the business needs of the next department. This informed transformation ensures that while the data meets the specific needs of the department, the transformation process and its reasons are preserved.

[0086] Building upon this foundation, the review method in this application further enhances the intelligence level of the review process. First, by acquiring the project type, department identifier, and data version of the project to be reviewed, the system can accurately identify the specific context of the current review task. Based on this contextual information, the system dynamically acquires the policy review rule set that best matches the current scenario, rather than relying on static, unchanging rules. This dynamic rule acquisition mechanism enables the review process to respond in real-time to frequent updates to new energy grid connection policies and regulations, as well as the differentiated interpretations of policies by various approval departments. Subsequently, based on these dynamically acquired policy review rule sets, the system comprehensively compares the original semantic metadata in the first semantic data packet, the view data generated by the target department, and the conversion records generated during the data conversion process. This comparison not only focuses on surface values ​​or formats but also delves into the original semantic meaning and conversion logic of the data. By combining conversion records, the system can clearly track the evolution path of data from its original state to view data, thereby intelligently distinguishing reasonable deviations caused by business needs or regulatory differences, supported by clear conversion records, from deep semantic inconsistencies caused by information loss, misunderstanding, or unrecognized semantic drift. Ultimately, the system generates detailed audit reports only for identified deep semantic inconsistencies, thus avoiding false alarms for reasonable deviations and improving the accuracy and efficiency of the audit. The entire process forms a closed loop, ensuring the semantic consistency, compliance, and traceability of new energy grid connection data during cross-departmental transfer and auditing. This effectively solves the problems of misjudgment, omissions, and the impact on the authenticity and accuracy of approval data that arise when traditional auditing mechanisms face complex business scenarios.

[0087] The following is a concrete example to illustrate this. A standalone "cross-departmental semantic consistency detection service" periodically or triggered at key approval nodes performs consistency checks on the same key data fields across different departments. This service no longer simply compares the surface values ​​or formats of data fields, but also compares the semantic metadata they carry. Suppose that the "installed capacity" data of a "wind power project" is undergoing cross-departmental review. First, the review system obtains that the project type is "wind power project," the departments to be reviewed involve "Technology Department" and "Finance Department," and the current data version is "20240520." Based on this contextual information, the system dynamically retrieves and loads the policy review rule sets applicable to the "Technology Department" and "Finance Department" under version "20240520" from the rule base. The rule set may stipulate that the "installed capacity" provided by the technical department should be accurate to three decimal places, and its business meaning is "engineering design capacity"; while the "installed capacity" received and used by the finance department should be rounded to the nearest integer, and its business meaning is "financial accounting benchmark capacity"; and the conversion from "engineering design capacity" to "financial accounting benchmark capacity" allows for rounding to the nearest integer.

[0088] Subsequently, the system compares the semantic metadata in the first semantic data packet flowing from the Technology Department with the view data and its transformation records generated by the Finance Department. For example, in the first semantic data packet provided by the Technology Department, the semantic metadata for "installed capacity" might include: the original input numerical information is "100.567 MW", the original precision is "three decimal places", and the business meaning is "engineering design capacity". However, in the view data generated by the Finance Department, "installed capacity" is displayed as "101 MW". At the same time, the system will query the transformation record from the Technology Department data to the Finance Department view data. This record clearly indicates that the "installed capacity" field has been converted from "100.567 MW" to "101 MW" through a "rounding" operation, and is associated with the corresponding transformation rule identifier.

[0089] Based on the comparison results and conversion records, the system will make a distinction. In this example, the system found a numerical difference (100.567 MW vs. 101 MW), but by querying the conversion records, the system confirmed a clear "rounding" conversion operation, which conforms to the rule defined in the previously obtained policy review rules set that "rounding to integers is allowed from 'engineering design capacity' to 'financial accounting benchmark capacity'." Therefore, the system will classify this difference as a reasonable deviation, rather than a deep semantic inconsistency. However, if the conversion records show that the data was truncated to "100 MW" without reason, or if the conversion records are missing, and the conversion does not conform to any preset policy rules, then the system will mark it as a deep semantic inconsistency. For example, if the original data from the technical department is "100.567 MW," and the metadata indicates its business intent is "engineering design capacity," while the original data from the financial department is "100 MW," and the metadata indicates its business intent is "financial accounting benchmark capacity." The detection service checks whether a valid semantic mapping rule exists that can reasonably convert "100.567 MW" of "engineering design capacity" to "100 MW" of "financial accounting baseline capacity" (e.g., a truncation rule). If the actual conversion result (e.g., the view data from the finance department) does not match the expected conversion result, or if the metadata clearly indicates unacceptable losses due to historical interfaces, the system will immediately issue a warning, pointing out the specific semantic conflict point (e.g., "the installed capacity of the technology department was originally accurate to three decimal places, but the data received by the finance department is an integer, and the metadata does not clearly state the reasonableness of this conversion or that unacceptable truncation has occurred").

[0090] Ultimately, if the system identifies a deep semantic inconsistency—for example, the technical department's original data is "100.567MW," but the finance department's view data is "99MW," and there is no reasonable explanation in the conversion records—the system will generate a deep semantic inconsistency audit report. This report will detail that "the technical department's original installed capacity is 100.567 MW, meaning the engineering design capacity, while the finance department's view data is 99 MW, and there are no valid conversion records or the conversion does not comply with policy rules, indicating a deep semantic inconsistency," and may recommend manual verification.

[0091] Through the above technical solutions, this application can significantly improve the accuracy and adaptability of intelligent review of new energy grid connection data. By dynamically acquiring the project type, department identifier, and data version of the project to be reviewed, and based on this, obtaining a real-time updated policy review rule set, this solution effectively solves the problem that traditional static review rules are lagging behind or unable to adapt to the frequent updates of new energy grid connection policies and regulations, as well as the differentiated interpretations by various approval departments, thereby avoiding misjudgments or omissions caused by rule mismatches. In addition, by comparing the semantic metadata in the first semantic data package with the view data and its transformation records based on the policy review rule set, and distinguishing between reasonable deviations and deep semantic inconsistencies, this solution enables the review process to accurately identify substantial semantic distortions that occur during data flow, rather than merely staying at the level of surface numerical or format comparisons, which greatly improves the authenticity and accuracy of the review results. Finally, generating a review report on deep semantic inconsistencies allows reviewers to focus their attention on the real problems that need to be solved, significantly improving review efficiency, reducing the complexity and cost of manual review, and effectively ensuring the reliability of approval data. This solution, combined with the basic solution, can make full use of the rich semantic metadata encapsulated in the first semantic data packet and the informed transformation information contained in the view data to perform deeper semantic comparison, thereby effectively identifying data that appears consistent on the surface but whose deep meaning has been distorted. This solves the problem mentioned in the background technology of "appearing consistent on the surface, but in fact there is a hidden mismatch between deep semantics and contextual information".

[0092] Reference Appendix Figure 2 This invention provides an intelligent verification system for new energy grid connection data (this intelligent verification system for new energy grid connection data adopts the intelligent verification method for new energy grid connection data of the above embodiments, and the specific process is referred to the corresponding steps above), including: The outflow module 100 is used to encapsulate the key data fields in the data with semantic metadata when data flows out from the previous department, so as to obtain the first semantic data packet. The transfer module 200 is used to record the loss information caused by data interface limitations during the transfer of the first semantic data packet, and to attach the loss information to the first semantic data packet to obtain the second semantic data packet; The inflow module 300 is used to interpret the semantic metadata in the second semantic data packet when the second semantic data packet flows into the next department, and perform informed data transformation on the key data fields in the second semantic data packet based on the preset semantic mapping rules to generate view data that meets the business needs of the next department. The audit module 400 is used to store the first semantic data package and to perform cross-departmental semantic consistency audits based on the semantic metadata and view data in the first semantic data package.

[0093] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0094] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent verification of new energy grid connection data, characterized in that, Includes the following steps: S1. When data flows out from the previous department, semantic metadata is encapsulated on the key data fields in the data to obtain the first semantic data packet; S2. During the flow of the first semantic data packet, record the loss information caused by data interface limitations, and append the loss information to the first semantic data packet to obtain the second semantic data packet; S3. When the second semantic data packet flows into the next department, the semantic metadata in the second semantic data packet is interpreted, and based on the preset semantic mapping rules, the key data fields in the second semantic data packet are subjected to informed data transformation to generate view data that meets the business needs of the next department; S4. Store the first semantic data packet, and perform cross-departmental semantic consistency audit based on the semantic metadata in the first semantic data packet and the view data.

2. The intelligent verification method for new energy grid connection data according to claim 1, characterized in that, Semantic metadata includes raw input numerical information, raw source information, raw format information, raw precision information, business meaning, purpose of use, version identifier, and processing conventions.

3. The intelligent verification method for new energy grid connection data according to claim 2, characterized in that, The original source information includes department identifier, operator ID, data timestamp, and business name.

4. The intelligent verification method for new energy grid connection data according to claim 2, characterized in that, The specific steps in step S1 include: S11. Based on the business meaning, determine the corresponding numerical range or precision requirement through a preset business meaning mapping table; S12. Based on the original input values ​​in the original input value information, and based on the determined value range or precision requirements, evaluate whether there is a semantic deviation between the original input values ​​and the business meaning, and obtain the semantic deviation evaluation result; S13. When the semantic deviation assessment result shows that there is a semantic deviation, provide the operator with a list of alternative business intentions; S14. Receive the service intent selected by the operator from the list of alternative service intents; S15. Based on the business intent selected by the operator, reconstruct the semantic metadata of the key data fields, and encapsulate the first semantic data packet based on the reconstructed semantic metadata.

5. The intelligent verification method for new energy grid connection data according to claim 1, characterized in that, The specific steps in step S2 include: S21. For the complex data structures contained in the key data fields, extract the original structural feature information of the complex data structures; S22. Based on the structural constraint specifications of the data interface, pre-validate the complex data structure in the first semantic data packet to obtain the pre-validation result; S23. When the pre-verification result shows that the complex data structure does not conform to the structural constraint specification of the data interface, the complex data structure is structurally adjusted, and the structural loss generated during the structural adjustment process is identified and recorded as structural loss information; S24. The structural loss information is appended to the semantic metadata of the first semantic data packet in a path-based manner to obtain the second semantic data packet.

6. The intelligent verification method for new energy grid connection data according to claim 5, characterized in that, The original structural feature information includes the hierarchical relationship of the complex data structure, the number of internal fields, and the identifiers of key related fields.

7. The intelligent verification method for new energy grid connection data according to claim 5, characterized in that, The structural loss information includes the structural path in which the loss occurs, the type of loss, and the cause of the loss.

8. The intelligent verification method for new energy grid connection data according to claim 1, characterized in that, The specific steps in step S3 include: S31. Parse the semantic metadata in the second semantic data packet and identify the unstructured policy supplementary descriptions contained in the semantic metadata; S32. Based on the business meaning in the semantic metadata and the supplementary explanation of the unstructured policy, obtain the dynamic calculation rules related to the key data fields; S33. Based on the dynamic calculation rules, determine whether it is necessary to obtain external real-time dynamic data; S34. When it is determined that external real-time dynamic data needs to be obtained, a request is sent to the external data service to obtain the external real-time dynamic data; S35. Adjust the preset semantic mapping rule according to the dynamic calculation rule, the external real-time dynamic data, and the original data in the second semantic data packet; S36. Based on the adjusted semantic mapping rules, perform informed data transformation on the key data fields in the second semantic data packet to generate view data that meets the business needs of the next department; S37. Record the adjustment process of the semantic mapping rules, the acquisition information of the external real-time dynamic data, and the informed data conversion process.

9. The intelligent verification method for new energy grid connection data according to claim 1, characterized in that, Step S4, the step of performing cross-departmental semantic consistency audit based on the semantic metadata in the first semantic data packet and the view data, includes: S51. Obtain the project type, department identifier, and data version of the project to be reviewed; S52. Based on the project type, the department identifier, and the data version, obtain the policy review rule set; S53. Based on the policy review rule set, compare the semantic metadata in the first semantic data packet with the view data and its transformation records to obtain the comparison result; S54. Based on the comparison results and the conversion records, distinguish between reasonable deviations and deep semantic inconsistencies between the semantic metadata in the first semantic data packet and the view data; S55. Generate an audit report on the deep semantic inconsistency.

10. A smart verification system for new energy grid connection data, characterized in that, include: The outflow module is used to encapsulate the key data fields in the data with semantic metadata when data flows out from the previous department, so as to obtain the first semantic data packet; The flow module is used to record loss information caused by data interface limitations during the flow of the first semantic data packet, and to append the loss information to the first semantic data packet to obtain the second semantic data packet; The inflow module is used to interpret the semantic metadata in the second semantic data packet when the second semantic data packet flows into the next department, and perform informed data transformation on the key data fields in the second semantic data packet based on preset semantic mapping rules to generate view data that meets the business needs of the next department. The audit module is used to store the first semantic data packet and perform cross-departmental semantic consistency audit based on the semantic metadata in the first semantic data packet and the view data.

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