Multi-criteria based zero-carbon cloud library carbon emission accounting and dynamic monitoring method

Abstract

The present application relates to the technical field of carbon emission accounting and dynamic monitoring, and more particularly to a multi-standard-based zero-carbon cloud library carbon emission accounting and dynamic monitoring method. The method comprises the following steps: obtaining multi-standard accounting rule data, basic data and interface data, and generating unified accounting boundary data through standardized processing and caliber mapping; generating baseline data in combination with traditional paper reading benchmark data, and generating path accounting tasks corresponding to printing substitution emission reduction, transportation substitution emission reduction, storage substitution emission reduction, entity library operation and maintenance substitution emission reduction, paperless emission reduction and operation optimization emission reduction; generating library-level emission reduction accounting data through a library-level edge computing node, and after cross-verification by a district-level aggregation node, generating platform reporting data packets and feedback confirmation data by a national-level master control platform, and generating early warning information and monitoring records. The present application effectively improves the standardization, traceability and dynamic monitoring consistency of zero-carbon cloud library carbon emission accounting.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting and dynamic monitoring technology, and in particular to a method for carbon emission accounting and dynamic monitoring based on a multi-standard zero-carbon cloud library. Background Technology

[0002] In the field of carbon emission accounting and dynamic monitoring technology, existing solutions for zero-carbon cloud libraries typically collect digital reading operation data, equipment energy consumption data, and green reading activity data. These data are then combined with carbon emission factors, accounting cycles, and reporting fields to generate carbon emission or emission reduction records, which are subsequently managed through platform reporting or early warning processing. Existing methods often rely on a single accounting boundary or a single data caliber, failing to adequately handle field differences between multi-standard accounting rule data, basic data, and interface data. This leads to problems such as inconsistent accounting boundaries, inconsistent data fields, mismatched reporting fields, and difficulties in synchronizing verification rules.

[0003] In scenarios involving traditional paper-based reading baseline data, digital reading operation data, equipment energy consumption data, and green reading activity data in zero-carbon cloud libraries, existing solutions typically aggregate data from different sources for calculation, lacking a process to transform unified accounting boundary data and traditional paper-based reading baseline data into path accounting tasks. Emission reductions from printing substitution, transportation substitution, warehousing substitution, physical library operation and maintenance substitution, paperless operation, and operational optimization are easily mixed, leading to unclear correspondences between baseline data, library-level operation data, report fields, and verification rules, making it difficult to support the continuous generation of library-level emission reduction accounting data.

[0004] For the joint processing of unified accounting boundary data, traditional paper-based reading baseline data, path accounting tasks, and library-level emission reduction accounting data, existing technologies generally focus on single-library collection, single-item accounting, or single-platform reporting, lacking sufficient coordination for cross-verification, trend analysis, and anomaly identification among multiple libraries in district-level aggregation nodes. Library-level emission reduction accounting data uploaded from different library-level edge computing nodes lacks a verification process centered around the same path identifier, making it difficult to separate verified data and anomaly-marked data, thus affecting the continuous link between platform-reported data packets, feedback confirmation data, early warning information, and monitoring records. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for carbon emission accounting and dynamic monitoring of a zero-carbon cloud library based on multiple standards, comprising:

[0006] S100: Obtain multi-standard accounting rule data, basic data, and interface data; standardize and map accounting boundaries, data fields, report fields, and verification rules to generate unified accounting boundary data.

[0007] S200. Based on the unified accounting boundary data and traditional paper reading benchmark data, perform field validation and path identifier matching to generate traditional paper reading benchmark data;

[0008] S300. Based on the unified accounting boundary data and the traditional paper reading baseline data, generate a path accounting task;

[0009] The path calculation tasks include corresponding emission reductions through printing substitution, transportation substitution, warehousing substitution, physical museum operation and maintenance substitution, paperless emission reduction, and operation optimization.

[0010] S400. Based on the path accounting task, collect digital reading operation data, equipment energy consumption data and green reading activity data through the library-level edge computing node to generate library-level emission reduction accounting data.

[0011] S500. Based on the museum-level emission reduction accounting data, multi-museum cross-verification, trend analysis and anomaly identification are performed through the district-level aggregation node to generate verification pass data and anomaly marker data.

[0012] S600. Based on the verification pass data, generate a platform reporting data packet and feedback confirmation data through the national-level main control platform;

[0013] S700. Based on the anomaly marker data and the feedback confirmation data, generate early warning information and monitoring records.

[0014] Furthermore, the multi-standard accounting rule data in S100 includes standard source, applicable objects, accounting boundaries, data fields, report fields, verification rules, and methodology selection basis; the basic data includes library-level identifier, operating entity, service scope, library-level equipment information, and activity record interface; the interface data includes data exchange format, interface specification, verification rules, and feedback confirmation field.

[0015] Furthermore, the traditional paper reading benchmark data mentioned in S200 includes the carbon footprint benchmark value of paper production, the carbon emission factor of printing process, the carbon emission factor of logistics and transportation, the energy consumption benchmark value of warehousing, and the energy consumption benchmark value of physical library operation and maintenance. The traditional paper reading benchmark data records the source, applicable time, applicable area, version identifier, and path identifier of the benchmark data.

[0016] Furthermore, the path accounting task described in S300 includes path identifiers, accounting cycles, data sources, baseline data, museum-level operational data, report fields, and verification rules; the path identifiers correspond to emission reduction through printing substitution, emission reduction through transportation substitution, emission reduction through warehousing substitution, emission reduction through physical museum operation and maintenance substitution, paperless emission reduction, and emission reduction through operational optimization.

[0017] Furthermore, the library-level edge computing node described in S400 connects to the digital reading platform, equipment energy consumption sensors, and green reading activity record interface through an IoT gateway to collect access logs, reading resource call records, digital reading platform operating energy consumption data, library-level equipment energy consumption data, and activity records.

[0018] Furthermore, the museum-level edge computing node in S400 performs timestamp alignment, data cleaning, standardization processing, and path identifier matching on access logs, reading resource call records, digital reading platform operation energy consumption data, museum-level equipment energy consumption data, and activity records to generate museum-level periodic monitoring data, and generates museum-level emission reduction accounting data from the museum-level periodic monitoring data.

[0019] Furthermore, the multi-library cross-verification mentioned in S500 includes trend analysis of library-level emission reduction accounting data and historical monitoring data under the same path identifier, comparison of library-level emission reduction accounting data of similar zero-carbon cloud libraries, matching verification of digital reading operation data and equipment energy consumption data, and matching verification of green reading activity data and paperless emission reduction path emission reduction.

[0020] Furthermore, the anomaly identification described in S500 includes abnormal fluctuations in path emission reduction, missing data fields, failure of museum-level edge computing nodes to upload data according to the accounting cycle, mismatch between digital reading operation data and equipment energy consumption data, mismatch between green reading activity data and path emission reduction, and failure to receive feedback confirmation data for data packets reported by the platform.

[0021] Furthermore, the data packets reported by the platform in S600 include a museum-level identifier, accounting cycle, path identifier, path emission reduction, total emission reduction, accounting boundary description, reference data source, methodology selection basis, verification result, and report fields; the feedback confirmation data includes receipt confirmation, verification passed, verification failed, and retransmission request.

[0022] Furthermore, the early warning information in S700 includes early warning level, museum-level identifier, accounting cycle, path identifier, anomaly type, triggering reason, and processing status; the monitoring record includes platform-reported data packets, feedback confirmation data, anomaly marker data, early warning information, and processing status.

[0023] The following are its main beneficial effects:

[0024] (1) To address the problem that multi-standard accounting rule data and traditional paper reading benchmark data are difficult to correspond, path accounting tasks are generated by unifying accounting boundary data and traditional paper reading benchmark data, so that printing substitution emission reduction, transportation substitution emission reduction, warehousing substitution emission reduction, physical museum operation and maintenance substitution emission reduction, paperless emission reduction, and operation optimization emission reduction have corresponding benchmark data, museum-level operation data, report fields and verification rules respectively.

[0025] (2) To address the issue of mixed processing of digital reading operation data, equipment energy consumption data and green reading activity data, the museum-level edge computing node generates museum-level emission reduction accounting data according to the path accounting task, so that access logs, reading resource call records, digital reading platform operation energy consumption data, museum-level equipment energy consumption data, activity records and paperless records are respectively entered into the corresponding path identifiers.

[0026] (3) To address the problem of insufficient data verification in single-museum collection, single-item accounting and single-platform reporting, the district-level aggregation node is used to conduct multi-museum cross-verification, trend analysis and anomaly identification of the museum-level emission reduction accounting data, so that the verification data and anomaly-marked data are diverted.

[0027] (4) To address the problem of the difficulty in connecting the data packets reported by the platform with the feedback confirmation data, the national-level main control platform generates the data packets reported by the platform and the feedback confirmation data based on the verification data. The embassy-level identifier, accounting cycle, path identifier, path emission reduction, total emission reduction, accounting boundary description, benchmark data source, methodology selection basis, verification results and report fields are kept related. Attached Figure Description

[0028] Figure 1 A flowchart illustrating the carbon emission accounting and dynamic monitoring method for a multi-standard zero-carbon cloud library provided in this application embodiment;

[0029] Figure 2 The structural block diagram of the carbon emission accounting and dynamic monitoring method for a zero-carbon cloud library based on multiple standards provided in the embodiments of this application is shown. Detailed Implementation

[0030] Example 1: Refer to Figure 1 This is a flowchart illustrating the carbon emission accounting and dynamic monitoring method for a zero-carbon cloud library based on multiple standards provided in this embodiment of the invention. The process may include at least steps S100-S700:

[0031] S100: Obtain multi-standard accounting rule data, basic data, and interface data; standardize and map accounting boundaries, data fields, report fields, and verification rules to generate unified accounting boundary data.

[0032] S200. Based on the unified accounting boundary data and traditional paper reading benchmark data, generate traditional paper reading benchmark data;

[0033] S300. Based on the unified accounting boundary data and the traditional paper reading baseline data, generate corresponding path accounting tasks for emission reduction through printing substitution, emission reduction through transportation substitution, emission reduction through warehousing substitution, emission reduction through physical museum operation and maintenance substitution, paperless emission reduction, and emission reduction through operation optimization.

[0034] S400. Based on the path accounting task, collect digital reading operation data, equipment energy consumption data and green reading activity data through the library-level edge computing node to generate library-level emission reduction accounting data.

[0035] S500. Based on the museum-level emission reduction accounting data, multi-museum cross-verification, trend analysis and anomaly identification are performed through the district-level aggregation node to generate verification pass data and anomaly marker data.

[0036] S600. Based on the verification pass data, generate a platform reporting data packet and feedback confirmation data through the national-level main control platform;

[0037] S700. Based on the anomaly marker data and the feedback confirmation data, generate early warning information and monitoring records.

[0038] S100: Obtain multi-standard accounting rule data, basic data, and interface data; standardize and map accounting boundaries, data fields, report fields, and verification rules to generate unified accounting boundary data.

[0039] Specifically, step S100 is executed by the data processing module within the national-level main control platform. This module receives multi-standard accounting rule data, basic data, and interface data, and writes these data into the same accounting cycle. The multi-standard accounting rule data refers to a set of rules related to carbon emission accounting for the Zero-Carbon Cloud Library from different standard sources, including standard sources, applicable objects, accounting boundaries, data fields, report fields, verification rules, and methodology selection criteria. The basic data refers to the basic records of the Zero-Carbon Cloud Library in its library-level management, including library-level identification, operating entity, service scope, library-level equipment information, and activity record interfaces. The interface data refers to the interface records called by local carbon emission statistics platforms and public institution carbon accounting systems when receiving data, including data exchange formats, interface specifications, verification rules, and feedback confirmation fields. This step is triggered by the data processing module upon new library access, changes in standard source versions, changes in library-level equipment information, changes in activity record interfaces, changes in interface specifications, and the start of an accounting cycle.

[0040] Furthermore, the data processing module first reads the standard source and applicable objects from the multi-standard accounting rule data, and writes the accounting boundaries, data fields, report fields, and verification rules under the same standard source into the rule temporary storage area. The accounting boundaries refer to the limitations on the scope of emission activities and emission reduction activities in the standard source; the data fields refer to the field names, field types, and field value ranges required for subsequent data collection and accounting; the report fields refer to the field names that need to appear in the emission reduction accounting report and the platform's data packet; the verification rules refer to field integrity verification, field type verification, time sequence verification, and caliber consistency verification. The rule temporary storage area stores the rule version identifier and rule reception time within the same accounting cycle. Fields that fail verification are marked with a rule exception flag, and the original field content is retained.

[0041] Furthermore, the data processing module standardizes the accounting boundaries. This standardization process involves converting accounting boundaries from different standard sources into unified field names and types. Specifically, the data processing module extracts the emission activity name, emission reduction activity name, applicable object, and accounting cycle from the accounting boundaries, and categorizes the emission activity name and emission reduction activity name into unified boundary fields. For fields with different names but the same meaning, the data processing module uses caliber mapping to form the same field. For cases where the same field has different values ​​in different standard sources, the data processing module records the standard source, field value, and rule version identifier, and generates a caliber mapping record. This caliber mapping record does not change the original rule content but provides a unified field entry point for the subsequent S200 call to traditional paper-based benchmark data.

[0042] Furthermore, the data processing module performs synchronous processing on the data fields, the report fields, and the verification rules. For the data fields, the data processing module performs field name unification, field type unification, and field value range verification to obtain unified data fields. For the report fields, the data processing module performs report field name matching and platform field name matching to obtain unified report fields. For the verification rules, the data processing module writes field integrity verification, field type verification, time sequence verification, and caliber consistency verification into the unified verification rules. The unified data fields, the unified report fields, and the unified verification rules are all associated with the standard source, rule version identifier, and library-level identifier.

[0043] In the engineering implementation, after a public cultural venue connects to the zero-carbon cloud library, the data processing module receives the venue's library-level identifier, operating entity, service scope, library-level equipment information, and activity record interface, as well as multi-standard accounting rule data and interface data. The data processing module first writes fields related to digital reading, paper book printing, logistics and transportation, warehousing, physical library operation and maintenance, and green reading activities from the accounting boundary into the rule temporary storage area, and then standardizes the data fields and report fields. If the data exchange format returned by the local carbon emission statistics platform differs in field names from the data exchange format of the public institution's carbon accounting system, the data processing module writes the two field names into the entry mapping record and forms a unified report field. If an activity record interface lacks an interface specification field, the data processing module writes a rule anomaly flag and retains this field in the pending correction section of the unified accounting boundary data.

[0044] Understandably, the minimum set of core parameters for S100 includes standard source, applicable objects, accounting boundary, data fields, report fields, verification rules, library-level identifier, operating entity, service scope, data exchange format, interface specification, and feedback confirmation fields. The methodology selection criteria, library-level equipment information, activity record interface, and rule version identifier are written as preferred extended fields into the unified accounting boundary data. The unified accounting boundary data output by S100 includes unified boundary fields, unified data fields, unified report fields, unified verification rules, caliber mapping records, library-level identifier, and rule version identifier. This unified accounting boundary data serves as direct input for S200 in generating traditional paper-based reading baseline data and is further invoked when S300 generates path accounting tasks.

[0045] In summary, the technical effects of this step are as follows: Step S100 organizes multi-standard accounting rule data, basic data, and interface data into unified accounting boundary data, ensuring that subsequent baseline generation and path accounting tasks share a common field source. Step S100, through standardization and caliber mapping, integrates accounting boundaries, data fields, report fields, and verification rules into the same processing chain. The caliber mapping records and rule version identifiers generated by Step S100 provide a field basis for the association of subsequent platform-reported data packets and feedback confirmation data.

[0046] S200. Based on the unified accounting boundary data and traditional paper reading benchmark data, generate traditional paper reading benchmark data;

[0047] Specifically, S200 is executed by the baseline processing module within the national-level main control platform. This baseline processing module receives the unified accounting boundary data output from S100 and also receives traditional paper-based reading baseline data. The traditional paper-based reading baseline data refers to the baseline records corresponding to paper production, printing processes, logistics and transportation, warehousing, and physical library operation and maintenance for traditional paper-based reading services. This includes baseline values ​​for paper production carbon footprint, printing process carbon emission factors, logistics and transportation carbon emission factors, warehousing energy consumption, and physical library operation and maintenance energy consumption. The baseline processing module triggers this step when the unified accounting boundary data is updated, the traditional paper-based reading baseline data is updated, the accounting cycle begins, and a new library-level identifier is added.

[0048] Furthermore, the baseline processing module first reads the unified boundary field, unified data field, unified report field, unified verification rule, caliber mapping record, library-level identifier, and rule version identifier from the unified accounting boundary data, and writes the traditional paper reading baseline data into the baseline data temporary storage area. The paper production carbon footprint baseline value is used to record the carbon emission baseline of the paper production process corresponding to paper books; the printing process carbon emission factor is used to record the carbon emission conversion field of the paper book printing process; the logistics and transportation carbon emission factor is used to record the carbon emission conversion field of the paper book transportation process; the warehousing energy consumption baseline value is used to record the energy consumption baseline of the paper book warehousing process; and the physical library operation and maintenance energy consumption baseline value is used to record the energy consumption baseline of the physical library operation and maintenance process. Each piece of traditional paper reading baseline data is written with the baseline data source, applicable time, applicable region, and version identifier.

[0049] Furthermore, the baseline processing module performs field validation and path identifier matching on the traditional paper reading baseline data. The field validation includes baseline data source validation, applicable time validation, applicable region validation, version identifier validation, and field type validation. The path identifier matching refers to writing the paper production carbon footprint baseline value and printing process carbon emission factor into the path identifier corresponding to printing substitution emission reduction, the logistics and transportation carbon emission factor into the path identifier corresponding to transportation substitution emission reduction, the warehousing energy consumption baseline value into the path identifier corresponding to warehousing substitution emission reduction, the physical library operation and maintenance energy consumption baseline value into the path identifier corresponding to physical library operation and maintenance substitution emission reduction, and writing the boundary fields required for paperless emission reduction and operational optimization emission reduction in green reading activities into the corresponding path identifiers. If a traditional paper reading baseline data does not match the applicable region, the baseline processing module writes a baseline anomaly flag and retains the original field content of that baseline data.

[0050] Further, the baseline processing module generates baseline report fields based on the unified report fields. The baseline report fields include baseline data source, applicable time, applicable region, version identifier, path identifier, and baseline field value. The baseline processing module associates each baseline field value with the unified verification rule to generate traditional paper reading baseline data. This traditional paper reading baseline data is not a single value, but rather consists of a set of baseline fields corresponding to multiple path identifiers. Each path identifier corresponds one-to-one with a path calculation task in S300, and the set of baseline fields is used as baseline data in S300.

[0051] In an engineering implementation, the service area of ​​a zero-carbon cloud library covers urban communities and government office areas. The baseline processing module reads the library's corresponding library-level identifier and applicable area, and then reads the baseline values ​​for paper production carbon footprint, printing process carbon emission factors, logistics and transportation carbon emission factors, warehousing energy consumption, and physical library operation and maintenance energy consumption from traditional paper reading baseline data. The baseline processing module writes the printing process carbon emission factor into the printing alternative emission reduction path, the logistics and transportation carbon emission factor into the transportation alternative emission reduction path, the warehousing energy consumption baseline value into the warehousing alternative emission reduction path, and the physical library operation and maintenance energy consumption baseline value into the physical library operation and maintenance alternative emission reduction path. If the applicable area of ​​a certain logistics and transportation carbon emission factor is inconsistent with the area corresponding to the library-level identifier, the baseline processing module writes a baseline anomaly mark and sends the field to the review section.

[0052] Understandably, the minimum set of core parameters for S200 includes a unified boundary field, a unified data field, a carbon footprint benchmark value for paper production, a carbon emission factor for printing processes, a carbon emission factor for logistics and transportation, a benchmark value for warehousing energy consumption, a benchmark value for the energy consumption of physical museum operations and maintenance, a benchmark data source, an applicable time, an applicable region, a version identifier, and a path identifier. The benchmark anomaly flag and benchmark report field are written into the traditional paper reading benchmark data as preferred extended fields. The traditional paper reading benchmark data output by S200 includes a path identifier, benchmark data, a benchmark data source, an applicable time, an applicable region, a version identifier, and a benchmark anomaly flag. This traditional paper reading benchmark data serves as the direct input for the path calculation task generated by S300 and is called as the benchmark data source field when the S600 generation platform sends data packets.

[0053] In summary, the technical effects of this step are as follows: Step S200 organizes traditional paper-based reading baseline data into traditional paper-based reading baseline data with path identifiers, enabling the baseline fields from the paper-based reading process to be included in subsequent path calculation tasks. Step S200 binds the baseline data source, applicable time, applicable region, and version identifier to the baseline fields, forming a traceable baseline record. Step S200 connects the traditional paper-based reading baseline data with the unified calculation boundary data, supporting the subsequent itemized processing of the six emission reduction paths.

[0054] S300. Based on the unified accounting boundary data and the traditional paper reading baseline data, generate corresponding path accounting tasks for emission reduction through printing substitution, emission reduction through transportation substitution, emission reduction through warehousing substitution, emission reduction through physical museum operation and maintenance substitution, paperless emission reduction, and emission reduction through operation optimization.

[0055] Specifically, S300 is executed by the task generation module within the national-level main control platform. The task generation module receives the unified accounting boundary data output by S100 and the traditional paper-based reading baseline data output by S200. The path accounting task refers to an accounting processing object established around a single path identifier, including the path identifier, accounting cycle, data source, baseline data, library-level operational data, report fields, and verification rules. The path accounting task is generated or updated when the unified accounting boundary data is updated, the traditional paper-based reading baseline data is updated, the accounting cycle begins, a library-level identifier is added, or the path identifier is changed.

[0056] Furthermore, the task generation module first reads the unified boundary field, unified data field, unified report field, unified verification rules, and caliber mapping record from the unified accounting boundary data, and then reads the path identifier, benchmark data, benchmark data source, applicable time, applicable region, and version identifier from the traditional paper reading baseline data. The task generation module writes the path identifier into six task segments: printing substitution emission reduction, transportation substitution emission reduction, warehousing substitution emission reduction, physical library operation and maintenance substitution emission reduction, paperless emission reduction, and operation optimization emission reduction. The printing substitution emission reduction refers to the substitution emission reduction path formed by digital reading services for the printing of paper books; the transportation substitution emission reduction refers to the substitution emission reduction path formed by digital reading services for the logistics and transportation of paper books; the warehousing substitution emission reduction refers to the substitution emission reduction path formed by digital reading services for the warehousing of paper books; the physical library operation and maintenance substitution emission reduction refers to the substitution emission reduction path formed by digital reading services for the operation and maintenance of physical libraries; the paperless emission reduction refers to the emission reduction path corresponding to paperless records in green reading activities; and the operation optimization emission reduction refers to the optimized emission reduction path corresponding to the energy consumption data of the digital reading platform.

[0057] Furthermore, the task generation module loads task fields for each path identifier. For printing substitution emission reduction, the task generation module loads the baseline value of paper production carbon footprint, the carbon emission factor of printing process, digital reading operation data fields, and report fields. For transportation substitution emission reduction, the task generation module loads the logistics transportation carbon emission factor, digital reading operation data fields, service scope fields, and report fields. For warehousing substitution emission reduction, the task generation module loads the baseline value of warehousing energy consumption, the reading resource call record fields, and report fields. For physical library operation and maintenance substitution emission reduction, the task generation module loads the baseline value of physical library operation and maintenance energy consumption, library-level equipment energy consumption data fields, and report fields. For paperless emission reduction, the task generation module loads green reading activity data fields, activity implementation record fields, paperless record fields, and report fields. For operational optimization emission reduction, the task generation module loads the digital reading platform operation energy consumption data fields, library-level equipment information fields, and report fields.

[0058] Furthermore, the task generation module writes the calculation period into each path calculation task. The calculation period refers to the time range within which the path calculation task receives museum-level operational data and generates path emission reductions. The data source refers to the source fields of the access logs, reading resource call records, digital reading platform operation energy consumption data, museum-level equipment energy consumption data, activity records, and paperless records called by the path calculation task. The baseline data refers to the set of baseline fields corresponding to the path identifier in the traditional paper reading baseline data. The museum-level operational data refers to the museum-level periodic monitoring data collected and generated by the museum-level edge computing node in the S400. The report fields refer to the fields that the S600 generation platform needs to call when sending data packets. The verification rules refer to the field integrity verification, field type verification, time sequence verification, and caliber consistency verification written in the unified calculation boundary data. The task generation module encapsulates the above fields into a path calculation task and writes a task version identifier.

[0059] In an engineering implementation, after a public institution constructs multiple zero-carbon cloud libraries, the task generation module generates six path accounting tasks based on library-level identifiers. For the zero-carbon cloud library within the institution's office area, the task generation module writes reading resource access records and paper production carbon footprint baseline values ​​into the printing substitution emission reduction task, activity records and paperless records into the paperless emission reduction task, and digital reading platform operation energy consumption data into the operation optimization emission reduction task. If a library-level identifier lacks a library-level equipment energy consumption data field, the task generation module writes a task anomaly flag into the physical library operation and maintenance substitution emission reduction task and the operation optimization emission reduction task, while retaining the other fields of that path accounting task.

[0060] Understandably, the minimum set of core parameters for S300 includes path identifier, calculation cycle, data source, baseline data, library-level operational data, report fields, and verification rules. The task version identifier and task anomaly flag are written into the path calculation task as preferred extended fields. The path calculation tasks output by S300 correspond to emission reduction through printing substitution, transportation substitution, warehousing substitution, physical library maintenance substitution, paperless emission reduction, and operational optimization, respectively, and serve as direct inputs for S400 to generate library-level emission reduction calculation data after collecting digital reading operation data, equipment energy consumption data, and green reading activity data.

[0061] In summary, the technical effects of this step are as follows: S300 converts unified accounting boundary data and traditional paper-based baseline data into six path accounting tasks, ensuring each emission reduction path has a fixed path identifier, baseline data, and reporting fields. S300 establishes a correspondence between baseline data and museum-level operational data at the task level, reducing path mixing and field omissions. The path accounting tasks output by S300 provide direct calling objects for museum-level edge computing nodes to collect and calculate data.

[0062] S400. Based on the path accounting task, collect digital reading operation data, equipment energy consumption data and green reading activity data through the library-level edge computing node to generate library-level emission reduction accounting data.

[0063] Specifically, S400 is executed by a library-level edge computing node. This library-level edge computing node connects to the digital reading platform, device energy consumption sensors, and the green reading activity recording interface via an IoT gateway. The library-level edge computing node receives the path calculation task output by S300 and triggers the data collection process when the path calculation task arrives, the calculation cycle begins, the digital reading platform generates new access logs, the device energy consumption sensor generates new records, or the green reading activity recording interface generates new activity records. The digital reading operation data refers to the access logs and reading resource call records generated by the digital reading platform. The device energy consumption data refers to the energy consumption data of the digital reading platform and the energy consumption data of library-level devices. The green reading activity data refers to activity records and paperless records.

[0064] Furthermore, the library-level edge computing node first reads the path identifier, calculation cycle, data source, baseline data, library-level operational data, report fields, and verification rules from the path calculation task, and establishes the current round of data collection window. This current round of data collection window is jointly defined by the library-level identifier, calculation cycle, and path identifier. The library-level edge computing node collects access logs and reading resource call records through the digital reading platform interface. The access logs include library-level identifier, access time, reading object identifier, and access status fields. The reading resource call records include library-level identifier, call time, reading resource identifier, and call status fields. The library-level edge computing node collects digital reading platform operational energy consumption data and library-level device energy consumption data through device energy consumption sensors. The digital reading platform operational energy consumption data includes collection time, device identifier, and energy consumption fields. The library-level device energy consumption data includes library-level device information, collection time, and energy consumption fields. The museum-level edge computing node collects activity implementation records and paperless records through the green reading activity record interface. The activity implementation records include activity identifier, activity time, activity scenario, and participation record fields. The paperless records include activity identifier, paperless field, and record time field.

[0065] Furthermore, the museum-level edge computing node performs timestamp alignment, data cleaning, standardization, and path identifier matching on access logs, reading resource call records, digital reading platform operation energy consumption data, museum-level equipment energy consumption data, activity records, and paperless records. Timestamp alignment refers to writing data from different sources within the same accounting period into the same time window. Data cleaning refers to removing duplicate records, marking missing fields, marking fields with abnormal formats, and retaining the original field content. Standardization refers to converting the field names and types output from different interfaces into data fields in the path accounting task. Path identifier matching refers to writing access logs and reading resource call records into the relevant task segments for printing substitution emission reduction, transportation substitution emission reduction, and warehousing substitution emission reduction; writing museum-level equipment energy consumption data into the relevant task segment for physical museum operation and maintenance substitution emission reduction; writing activity records and paperless records into the relevant task segment for paperless emission reduction; and writing digital reading platform operation energy consumption data into the relevant task segment for operation optimization emission reduction.

[0066] Furthermore, the museum-level edge computing node encapsulates the data from path identifier matching into museum-level periodic monitoring data. This museum-level periodic monitoring data includes a museum-level identifier, calculation period, path identifier, access log field, reading resource call record field, digital reading platform operating energy consumption field, museum-level equipment energy consumption field, activity record field, paperless record field, data cleaning marker, and collection time. The museum-level edge computing node inputs this museum-level periodic monitoring data into the corresponding path calculation task, generating path emission reductions according to the baseline data, museum-level operating data, report fields, and verification rules in the path calculation task. The path emission reductions are associated with the path identifier, baseline data source, collection time, and data cleaning marker, and are then aggregated to generate museum-level emission reduction calculation data. This museum-level emission reduction calculation data includes a museum-level identifier, calculation period, path identifier, path emission reduction amount, baseline data source, data cleaning marker, and report fields.

[0067] In an engineering implementation, a zero-carbon cloud library generates access logs, reading resource call records, digital reading platform operating energy consumption data, library-level equipment energy consumption data, and activity records within one accounting cycle. The library-level edge computing node first establishes a collection window based on the accounting cycle in the path accounting task. Then, it reads access logs and reading resource call records from the digital reading platform, energy consumption fields from equipment energy consumption sensors, and activity records and paperless records from the green reading activity record interface. If the access log arrives later than the energy consumption data, the library-level edge computing node writes a timestamp-aligned record according to the collection time and adds a data cleaning flag to the late field. If an activity record lacks a paperless field, the library-level edge computing node retains the activity record and adds a data cleaning flag to indicate the missing paperless field. Subsequently, the library-level edge computing node generates path emission reductions according to path identifiers and summarizes them to generate library-level emission reduction accounting data.

[0068] Understandably, the minimum set of core parameters for S400 includes path accounting tasks, library-level identifiers, accounting cycles, path identifiers, access logs, reading resource call records, digital reading platform operating energy consumption data, library-level equipment energy consumption data, activity records, paperless records, and baseline data. The data cleaning markers, collection time, and report fields are written into the library-level emission reduction accounting data as preferred extended fields. The library-level emission reduction accounting data output by S400 serves as direct input for multi-library cross-verification, trend analysis, and anomaly identification by S500, and is used as the source fields for path emission reduction and total emission reduction when S600 generates the platform reporting data packet.

[0069] In summary, the technical effects of this step are as follows: The S400 process collects, aligns, cleans, and aggregates digital reading operation data, equipment energy consumption data, and green reading activity data at the library-level edge computing node. The S400 inputs library-level periodic monitoring data into the path calculation task, generating library-level emission reduction calculation data with path identifiers and baseline data sources. The S400 establishes a connection between library-level operation data and traditional paper-based reading baseline data at the path level.

[0070] S500. Based on the museum-level emission reduction accounting data, multi-museum cross-verification, trend analysis and anomaly identification are performed through the district-level aggregation node to generate verification pass data and anomaly marker data.

[0071] Specifically, S500 is executed by a district-level aggregation node. This node receives library-level emission reduction accounting data uploaded by multiple library-level edge computing nodes and reads historical monitoring data, data from similar zero-carbon cloud libraries, digital reading operation data, equipment energy consumption data, and green reading activity data. The multi-library cross-verification refers to the district-level aggregation node performing field integrity verification, trend analysis of the same path identifier, comparison of similar zero-carbon cloud library data, and source data matching verification on library-level emission reduction accounting data corresponding to multiple library-level identifiers within the same region. The trend analysis refers to performing a time-series comparison between library-level emission reduction accounting data under the same path identifier and historical monitoring data. The anomaly identification refers to generating anomaly marker data based on the cross-verification and trend analysis results.

[0072] Further, the district-level aggregation node first performs reception verification on the museum-level emission reduction accounting data. This reception verification includes verification of the museum-level identifier, accounting cycle, path identifier, path emission reduction field, baseline data source, data cleaning mark, and report field. If the reception verification passes, the district-level aggregation node writes the museum-level emission reduction accounting data into the district-level data temporary storage area. If the reception verification fails, the district-level aggregation node generates a reception anomaly flag and writes the corresponding museum-level identifier, accounting cycle, and path identifier into the anomaly flag data.

[0073] Furthermore, the district-level aggregation node performs cross-verification across multiple libraries. Specifically, the district-level aggregation node performs trend analysis on library-level emission reduction accounting data and historical monitoring data under the same path identifier to generate a path trend field; compares library-level emission reduction accounting data of similar zero-carbon cloud libraries to generate a similar library comparison field; matches and verifies digital reading operation data with equipment energy consumption data to generate an operation energy consumption matching field; and matches and verifies green reading activity data with the path emission reduction amount of paperless emission reduction to generate an activity emission reduction matching field. The "similar zero-carbon cloud libraries" refer to library-level objects with similar service scope, operating entity type, and library-level equipment information. The matching verification does not change the original fields in the library-level emission reduction accounting data, but rather generates verification result fields.

[0074] Furthermore, the district-level aggregation node performs anomaly identification based on the verification result fields. The anomaly identification includes abnormal fluctuations in path emission reduction, missing data fields, failure of the museum-level edge computing node to upload data according to the accounting cycle, mismatch between digital reading operation data and equipment energy consumption data, mismatch between green reading activity data and path emission reduction, and failure to receive feedback confirmation data for platform-submitted data packets. Abnormal fluctuations in path emission reduction refer to abnormal changes in path emission reduction under the same museum-level identifier and the same path identifier compared to historical monitoring data; missing data fields refer to required fields in the museum-level emission reduction accounting data being empty or of incorrect type; failure to upload data according to the accounting cycle refers to the museum-level edge computing node failing to submit museum-level emission reduction accounting data within the accounting cycle; and mismatch refers to the field relationship between the source data and the path emission reduction failing the verification rules. The district-level aggregation node writes the verified data into the verified data and the failed data into the anomaly marker data.

[0075] In this engineering implementation, a district-level aggregation node receives library-level emission reduction accounting data uploaded by three zero-carbon cloud libraries. The district-level aggregation node first completes the receiving verification according to the library-level identifier and accounting cycle, and then performs trend analysis on the printing substitution emission reduction paths under the three library-level identifiers. If the reading resource call records of one library-level identifier show a significant increase, but the device energy consumption data and access log fields do not change synchronously, the district-level aggregation node generates an operational energy consumption matching anomaly flag. If another library-level identifier uploads green reading activity data, but the paperless record field is missing, the district-level aggregation node generates a data field missing anomaly flag. After the remaining fields pass verification, the district-level aggregation node generates verification-passed data and writes the abnormal fields into the anomaly flag data.

[0076] Understandably, the minimum set of core parameters for S500 includes museum-level emission reduction accounting data, museum-level identifier, accounting cycle, path identifier, path emission reduction amount, historical monitoring data, digital reading operation data, equipment energy consumption data, and green reading activity data. The path trend field, similar museum comparison field, operation energy consumption matching field, activity emission reduction matching field, and anomaly type are written as preferred extended fields into the verification pass data and anomaly marker data. The verification pass data output by S500 serves as direct input to the data packet and feedback confirmation data generated by the S600 platform, and the anomaly marker data serves as direct input to the early warning information and monitoring records generated by S700.

[0077] In summary, the technical effects of this step are as follows: The S500 aggregates and cross-verifies the museum-level emission reduction accounting data uploaded from multiple museum-level edge computing nodes at the district level. The S500 separates abnormal fields and verification data through trend analysis and source data matching. The verification data and anomaly marker data generated by the S500 provide branch inputs for platform reporting and early warning records.

[0078] S600. Based on the verification pass data, generate a platform reporting data packet and feedback confirmation data through the national-level main control platform;

[0079] Specifically, S600 is executed by the national-level main control platform. The national-level main control platform receives the verification pass data output by S500 and reads the unified accounting boundary data generated by S100, the traditional paper-based reading baseline data generated by S200, and the path accounting task generated by S300. The platform-reported data package refers to the data encapsulation object for local carbon emission statistics platforms and public institution carbon accounting systems, including library-level identifier, accounting cycle, path identifier, path emission reduction, total emission reduction, accounting boundary description, baseline data source, methodology selection basis, verification results, and report fields. The feedback confirmation data refers to the reception confirmation, verification pass, verification fail, and retransmission request returned by local carbon emission statistics platforms and public institution carbon accounting systems.

[0080] Further, the national-level control platform first performs summary processing on the verified data. This summary processing refers to summarizing path emission reductions according to the library-level identifier, accounting cycle, and path identifier, and generating a total emission reduction. The national-level control platform retrieves the accounting boundary description, methodology selection criteria, and reporting fields from the unified accounting boundary data, the benchmark data source from the traditional paper-based reading baseline data, and the verification rules and path identifiers from the path accounting task. The accounting boundary description refers to the boundary field description corresponding to the data packet submitted by the platform in the unified accounting boundary data. The total emission reduction refers to the field after summarizing multiple path emission reductions within the same library-level identifier and the same accounting cycle. The verification result refers to the record after the verification passed field generated by the S500 and the national-level control platform performs a second field integrity verification.

[0081] Further, the national-level master control platform generates platform reporting data packets. The national-level master control platform reads the data exchange format, interface specifications, verification rules, and feedback confirmation fields from the interface data, and generates corresponding platform reporting data packets according to the interface specifications of the local carbon emission statistics platform and the public institution carbon accounting system. The data exchange format refers to the data field arrangement and field type required when the platform receives data. The interface specifications refer to the transmission address, transmission method, field verification requirements, and feedback confirmation fields called by the platform when receiving data. Before generating the platform reporting data packets, the national-level master control platform performs field integrity verification, field type verification, accounting cycle verification, institution-level identifier verification, and path identifier verification. After successful verification, the national-level master control platform pushes the platform reporting data packets to the local carbon emission statistics platform and the public institution carbon accounting system; if the verification fails, the national-level master control platform writes a reporting anomaly flag and sends the flag to the S700.

[0082] Furthermore, the national-level master control platform receives feedback confirmation data from local carbon emission statistics platforms and public institution carbon accounting systems. The "receive confirmation" refers to the reception status field returned by the platform after receiving the data packet submitted by the platform. "Verification passed" refers to the status field returned by the platform after verifying the fields and definitions in the submitted data packet. "Verification failed" refers to the status field returned by the platform after failing to verify the fields, definitions, or accounting periods. "Retransmission request" refers to the status field of the platform requesting the national-level master control platform to resend the submitted data packet. The national-level master control platform associates the feedback confirmation data with the submitted data packet, the institution-level identifier, the accounting period, and the path identifier, and generates a feedback confirmation data record.

[0083] In the engineering implementation, after receiving the verified data uploaded by a district-level aggregation node, the national-level control platform aggregates the emission reductions corresponding to the six path identifiers according to the library-level identifier and generates the total emission reduction. The national-level control platform retrieves report fields and interface specifications from the unified accounting boundary data, reference data sources from traditional paper-based baseline data, and methodology selection criteria and verification rules from the path accounting task. Subsequently, the national-level control platform generates platform reporting data packages for local carbon emission statistics platforms and for public institution carbon accounting systems. If the local carbon emission statistics platform returns a receipt confirmation and verification pass, and the public institution carbon accounting system returns a retransmission request, the national-level control platform records the feedback confirmation data and writes the corresponding fields of the retransmission request into the pending processing section.

[0084] Understandably, the minimum set of core parameters for the S600 includes verification pass data, museum-level identifier, accounting cycle, path identifier, path emission reduction, total emission reduction, accounting boundary description, baseline data source, methodology selection basis, verification results, and report fields. The data exchange format, interface specification, feedback confirmation field, reporting anomaly marker, and pending section are written as preferred extended fields into the platform reporting data packet and feedback confirmation data. The platform reporting data packet and feedback confirmation data output by the S600 serve as direct inputs for the S700 to generate early warning information and monitoring records.

[0085] In summary, the technical effects of this step are as follows: Step S600 converts the verified data into a platform reporting data packet and writes the accounting boundary description, benchmark data source, methodology selection basis, and verification results into the same reporting object. Step S600 processes feedback confirmation data from local carbon emission statistics platforms and public institution carbon accounting systems respectively. Through the feedback confirmation data records, Step S600 provides platform status fields for subsequent early warning information and monitoring records.

[0086] S700. Based on the anomaly marker data and the feedback confirmation data, generate early warning information and monitoring records.

[0087] Specifically, S700 is executed by the early warning processing module within the national-level main control platform. This module receives the anomaly marker data output by S500 and the platform reporting data packet and feedback confirmation data output by S600. The early warning information refers to a status record formed around the library-level identifier, accounting cycle, path identifier, and anomaly type, including early warning level, library-level identifier, accounting cycle, path identifier, anomaly type, triggering reason, and processing status. The monitoring record refers to the closed-loop record of this round of accounting and dynamic monitoring, including platform reporting data packets, feedback confirmation data, anomaly marker data, early warning information, and processing status. The early warning processing module triggers this step when anomaly marker data arrives, feedback confirmation data arrives, platform reporting data packets enter the pending processing section, the accounting cycle ends, and a retransmission request is generated.

[0088] Further, the early warning processing module first reads the library-level identifier, accounting cycle, path identifier, anomaly type, and triggering reason from the anomaly marker data. The anomaly types include abnormal fluctuations in path emission reduction, missing data fields, library-level edge computing nodes failing to upload data according to the accounting cycle, mismatch between digital reading operation data and equipment energy consumption data, mismatch between green reading activity data and path emission reduction, and failure to receive feedback confirmation data for platform-reported data packets. The triggering reason refers to the anomaly source field written by the S500 during anomaly identification, or the verification failure and retransmission request fields recorded by the S600 in the feedback confirmation data. The early warning processing module writes the anomaly type and triggering reason into the early warning temporary storage area and associates them with the platform-reported data packets.

[0089] Further, the early warning processing module reads the feedback confirmation data. If the feedback confirmation data is a receipt confirmation or verification passed, the early warning processing module writes the corresponding platform-reported data packet into the confirmed state and writes the confirmed state into the monitoring record. If the feedback confirmation data is a verification failed or a retransmission request, the early warning processing module writes the corresponding platform-reported data packet into the pending state and writes the pending state into the early warning information. If no feedback confirmation data is received after the calculation period ends, the early warning processing module generates an unconfirmed state and writes the unconfirmed state into the early warning information. The processing states include confirmed, pending, unconfirmed, and closed. The closed state is generated after the anomaly marker data has been reviewed and the feedback confirmation data has been written back.

[0090] Furthermore, the early warning processing module generates an early warning level based on the anomaly type and feedback confirmation data. The early warning level refers to an early warning grade field formed according to the anomaly source, path identifier, calculation cycle, and processing status. For abnormal fluctuations in path emission reduction, missing data fields, and failure of museum-level edge computing nodes to upload data according to the calculation cycle, the early warning processing module reads the anomaly marker data to generate a museum-level early warning level. For mismatches between digital reading operation data and equipment energy consumption data, and between green reading activity data and path emission reduction, the early warning processing module reads the path identifier to generate a path early warning level. For platform-reported data packets that do not receive feedback confirmation data, fail verification, or trigger retransmission requests, the early warning processing module reads the feedback confirmation data to generate a platform early warning level. The early warning processing module encapsulates the early warning level, museum-level identifier, calculation cycle, path identifier, anomaly type, triggering reason, and processing status into early warning information.

[0091] Furthermore, the early warning processing module generates monitoring records. These records associate platform-reported data packets, feedback confirmation data, anomaly marker data, early warning information, and processing status. The monitoring records also include rule version identifiers, baseline data sources, task version identifiers, and collection times, enabling cross-step association between the monitoring records and S100, S200, S300, S400, S500, and S600. If the processing status of the early warning information is pending, the early warning processing module writes back the corresponding library-level identifier, accounting cycle, and path identifier to the pending review section of the district-level aggregation node and the library-level edge computing node. If the feedback confirmation data is a retransmission request, the early warning processing module writes back the platform-reported data packets to the pending retransmission section of the national-level main control platform and re-invokes the reporting processing link of S600 upon the next reporting trigger. If the anomaly marker data has been reviewed, the early warning processing module updates the processing status and writes a closed status.

[0092] In an engineering embodiment, after a certain accounting cycle ends, the national-level main control platform receives abnormal marker data output from the district-level aggregation node. The abnormality type is a mismatch between green reading activity data and path emission reduction. Simultaneously, the public institution carbon accounting system returns a verification failure for the data packet submitted to the platform. After reading the abnormal marker data and feedback confirmation data, the early warning processing module writes the institution-level identifier, accounting cycle, path identifier corresponding to paperless emission reduction, and the verification failure field into the early warning temporary storage area, generating path early warning levels and platform early warning levels, and setting the processing status to pending. Subsequently, the early warning processing module associates the corresponding platform-submitted data packet, feedback confirmation data, abnormal marker data, and early warning information into a monitoring record, and writes the pending status back to the pending sections of the district-level aggregation node and the national-level main control platform. After verification, the early warning processing module updates the processing status and incorporates the updated monitoring record into the next accounting cycle.

[0093] Understandably, the minimum set of core parameters for S700 includes anomaly marker data, feedback confirmation data, platform-reported data packets, library-level identifier, accounting cycle, path identifier, anomaly type, triggering reason, warning level, and processing status. The rule version identifier, baseline data source, task version identifier, collection time, pending processing segment, and pending retransmission segment are written into the monitoring record as preferred extended fields. The warning information and monitoring records output by S700 serve as the closed-loop products of this round of methods. The processing status, feedback confirmation data, and anomaly marker data in the monitoring records serve as reference fields for the association of S100, S500, and S600 in subsequent accounting cycles.

[0094] In summary, the technical effects of this step are as follows: S700 merges and processes anomaly-marked data and feedback confirmation data to generate early warning information with a library-level identifier, accounting cycle, and path identifier. S700 writes platform-reported data packets, feedback confirmation data, anomaly-marked data, and processing status into the monitoring record, forming a cross-step closed loop. S700 connects the district-level aggregation node, library-level edge computing node, and national-level main control platform through the pending processing section and the pending retransmission section, providing relevant fields for the next accounting cycle.

[0095] Example 2: Figure 2 A structural block diagram of a multi-standard-based zero-carbon cloud library carbon emission accounting and dynamic monitoring method according to an embodiment of the present invention is shown. Figure 2 As shown, the structure may include:

[0096] The accounting boundary processing module 01 is used to acquire multi-standard accounting rule data, basic data, and interface data, and to standardize and map the accounting boundaries, data fields, report fields, and verification rules to generate unified accounting boundary data. Specifically, the accounting boundary processing module receives multi-standard accounting rule data from standard sources, basic data from the Zero Carbon Cloud Library, and interface data from local carbon emission statistics platforms and public institution carbon accounting systems. The multi-standard accounting rule data includes standard sources, applicable objects, accounting boundaries, data fields, report fields, verification rules, and methodology selection criteria. The basic data includes library-level identifiers, operating entities, service scope, library-level equipment information, and activity record interfaces. The interface data includes data exchange formats, interface specifications, verification rules, and feedback confirmation fields. The accounting boundary processing module initiates the processing flow when a new library is connected, the standard source changes, the interface specification changes, or the accounting cycle begins. During processing, the accounting boundary processing module first reads the accounting boundary and forms a unified boundary field, then reads the data field and forms a unified data field, subsequently reads the report field and forms a unified report field, and then writes the verification rules into the unified verification rules. For records with different field names but the same meaning, the accounting boundary processing module generates a caliber mapping record. For records with inconsistent field types or missing fields, the accounting boundary processing module writes a rule exception flag and retains the original field content. The unified boundary field, the unified data field, the unified report field, the unified verification rule, the caliber mapping record, the museum-level identifier, and the rule version identifier are collectively encapsulated into the unified accounting boundary data. The accounting boundary processing module provides the unified accounting boundary data to the baseline processing module and retains the caliber mapping record for use by the national-level master control platform when generating platform reporting data packets.

[0097] The baseline processing module 02, connected to the accounting boundary processing module, is used to generate traditional paper reading baseline data based on the unified accounting boundary data and traditional paper reading baseline data. Specifically, the baseline processing module receives the unified accounting boundary data output by the accounting boundary processing module and receives the traditional paper reading baseline data. The traditional paper reading baseline data includes baseline values ​​for paper production carbon footprint, printing process carbon emission factors, logistics and transportation carbon emission factors, warehousing energy consumption, and physical library operation and maintenance energy consumption. The baseline processing module reads the unified boundary field, unified data field, unified report field, and unified verification rules from the unified accounting boundary data, and performs baseline data source verification, applicable time verification, applicable region verification, version identifier verification, and field type verification on the traditional paper reading baseline data. Baseline data that passes verification is written to the corresponding baseline field according to the path identifier. Baseline data that fails verification is marked with a baseline anomaly flag, and the original field content is retained. The baseline processing module writes the carbon footprint baseline value of paper production and the carbon emission factor of printing process into the corresponding fields for printing substitution and emission reduction, the carbon emission factor of logistics and transportation into the corresponding field for transportation substitution and emission reduction, the baseline value of warehousing energy consumption into the corresponding field for warehousing substitution and emission reduction, and the baseline value of physical museum operation and maintenance energy consumption into the corresponding field for physical museum operation and maintenance substitution and emission reduction. It also simultaneously writes the baseline data source, applicable time, applicable region, version identifier, and path identifier into the baseline report field. These baseline fields, the baseline report field, and the baseline anomaly marker together form the traditional paper reading baseline data. The baseline processing module provides the traditional paper reading baseline data to the task generation module and provides the baseline data source to the national-level main control platform.

[0098] The task generation module 03, connected to the baseline processing module, is used to generate path calculation tasks for corresponding emission reductions through printing substitution, transportation substitution, warehousing substitution, physical library maintenance substitution, paperless operation, and operational optimization, based on the unified accounting boundary data and the traditional paper reading baseline data. Specifically, the task generation module receives the unified accounting boundary data generated by the accounting boundary processing module and the traditional paper reading baseline data generated by the baseline processing module. The task generation module reads the unified boundary field, unified data field, unified report field, unified verification rules, path identifier, baseline data, baseline data source, and version identifier, and generates path calculation tasks at the start of the accounting cycle, when a library-level identifier is added, or when the baseline data is updated. The task generation module writes the emission reductions through printing substitution, transportation substitution, warehousing substitution, physical library maintenance substitution, paperless operation, and operational optimization into task segments. Each task segment records the path identifier, accounting cycle, data source, baseline data, library-level operation data, report field, and verification rules. The task generation module configures access logs and reading resource call records as library-level operational data sources for emission reduction through printing, transportation, and warehousing substitutions; configures library-level equipment energy consumption data as a library-level operational data source for emission reduction through physical library maintenance substitutions; configures activity records and paperless records as a library-level operational data source for paperless emission reduction; and configures digital reading platform operational energy consumption data as a library-level operational data source for emission reduction through operational optimization. When a field is missing, the task generation module writes a task anomaly flag in the corresponding task segment. The path identifier, the calculation cycle, the data source, the baseline data, the library-level operational data, the report field, the verification rules, and the task anomaly flag are collectively encapsulated into the path calculation task. The task generation module provides the path calculation task to the library-level edge computing node and provides the path identifier and report field to the national-level main control platform.

[0099] Museum-level edge computing node 04, connected to the task generation module, is used to collect digital reading operation data, equipment energy consumption data, and green reading activity data based on the path accounting task, and generate museum-level emission reduction accounting data. Specifically, the museum-level edge computing node receives the path accounting task output by the task generation module and accesses the digital reading platform, equipment energy consumption sensor, and green reading activity record interface through an IoT gateway. The digital reading operation data includes access logs and reading resource call records. The equipment energy consumption data includes digital reading platform operation energy consumption data and museum-level equipment energy consumption data. The green reading activity data includes activity implementation records and paperless records. The museum-level edge computing node starts the collection process at the beginning of the accounting cycle, when access logs are generated, when reading resource call records are generated, when equipment energy consumption sensors generate new records, or when the activity record interface generates new records. After collection, the museum-level edge computing node performs timestamp alignment, data cleaning, standardization processing, and path identifier matching on the access logs, reading resource call records, digital reading platform operation energy consumption data, museum-level equipment energy consumption data, activity implementation records, and paperless records. Duplicate records are written to data cleaning tags. Missing fields are marked with data cleaning tags while retaining their original content. The museum-level edge computing node encapsulates the path identifier matching data into museum-level periodic monitoring data, and then inputs the museum-level periodic monitoring data into the corresponding path accounting task to generate path emission reduction amounts. The museum-level identifier, accounting period, path identifier, path emission reduction amount, baseline data source, data cleaning tags, and report fields together form the museum-level emission reduction accounting data. The museum-level edge computing node uploads the museum-level emission reduction accounting data to the district-level aggregation node and retains the museum-level periodic monitoring data for anomaly review.

[0100] District-level aggregation node 05, connected to the library-level edge computing node, is used to perform multi-library cross-verification, trend analysis, and anomaly identification based on the library-level emission reduction accounting data, generating verification-passed data and anomaly-marked data. Specifically, the district-level aggregation node receives the library-level emission reduction accounting data uploaded by multiple library-level edge computing nodes and reads historical monitoring data, similar zero-carbon cloud library data, digital reading operation data, equipment energy consumption data, and green reading activity data. The district-level aggregation node first verifies the library-level emission reduction accounting data. The verification includes library-level identifier verification, accounting cycle verification, path identifier verification, path emission reduction field verification, baseline data source verification, data cleaning mark verification, and report field verification. The library-level emission reduction accounting data that passes the verification enters the district-level data temporary storage area. The library-level emission reduction accounting data that fails the verification enters the anomaly section. Subsequently, the district-level aggregation node performs trend analysis on the library-level emission reduction accounting data and historical monitoring data under the same path identifier, compares the library-level emission reduction accounting data of similar zero-carbon cloud libraries, matches and verifies digital reading operation data with equipment energy consumption data, and matches and verifies green reading activity data with the path emission reduction amount of paperless emission reduction. Based on the verification results, the district-level aggregation node identifies abnormal fluctuations in path emission reduction, missing data fields, library-level edge computing nodes failing to upload data according to the accounting cycle, mismatches between digital reading operation data and equipment energy consumption data, and mismatches between green reading activity data and path emission reduction. Fields that pass verification are encapsulated as verified data. Fields that fail verification are encapsulated as anomaly marker data. The district-level aggregation node provides the verified data to the national-level main control platform and the anomaly marker data to the early warning processing module.

[0101] The national-level master control platform 06, connected to the district-level aggregation node, is used to generate platform reporting data packets and feedback confirmation data based on the verified data. Specifically, the national-level master control platform receives the verified data output by the district-level aggregation node and calls the unified accounting boundary data, the traditional paper reading baseline data, and the path accounting task. The national-level master control platform summarizes and processes the verified data according to the library-level identifier, accounting cycle, and path identifier to form path emission reduction and total emission reduction. The national-level master control platform reads the accounting boundary description, methodology selection basis, and report fields from the unified accounting boundary data, the baseline data source from the traditional paper reading baseline data, and the verification rules and path identifier from the path accounting task. Subsequently, the national-level master control platform generates a platform reporting data packet according to the data exchange format, interface specification, verification rules, and feedback confirmation fields in the interface data. The platform's reported data packet includes a museum-level identifier, accounting period, path identifier, path emission reduction, total emission reduction, accounting boundary description, baseline data source, methodology selection basis, verification results, and report fields. The national-level control platform submits the reported data packet to local carbon emission statistics platforms and public institution carbon accounting systems, respectively, and receives confirmation of receipt, verification passed, verification failed, and retransmission requests. The confirmation of receipt, verification passed, verification failed, and retransmission requests are written into the feedback confirmation data. The national-level control platform provides the reported data packet and the feedback confirmation data to the early warning processing module and saves the reporting status for subsequent accounting periods.

[0102] The early warning processing module 07, connected to the district-level aggregation node and the national-level main control platform, is used to generate early warning information and monitoring records based on the anomaly marker data and the feedback confirmation data. Specifically, the early warning processing module receives the anomaly marker data output by the district-level aggregation node and the platform reporting data packet and the feedback confirmation data output by the national-level main control platform. The early warning processing module reads the library-level identifier, accounting cycle, path identifier, anomaly type, and triggering reason from the anomaly marker data, and reads the reception confirmation, verification passed, verification failed, and retransmission request from the feedback confirmation data. The early warning processing module initiates the processing flow when the anomaly marker data arrives, the feedback confirmation data arrives, the platform reporting data packet enters the pending state, the accounting cycle ends, or a retransmission request is generated. When the feedback confirmation data is a reception confirmation or verification passed, the early warning processing module writes the corresponding platform reporting data packet to the confirmed state. When the feedback confirmation data is a verification failed or a retransmission request, the early warning processing module writes the corresponding platform reporting data packet to the pending state. If no feedback confirmation data is received after the accounting cycle ends, the early warning processing module writes an unconfirmed status. The early warning processing module generates an early warning level based on the anomaly type, triggering reason, and processing status, and encapsulates the early warning level, library-level identifier, accounting cycle, path identifier, anomaly type, triggering reason, and processing status into the early warning information. The early warning processing module associates the platform-reported data packet, the feedback confirmation data, the anomaly marker data, the early warning information, and the processing status into the monitoring record, and writes the pending status back to the district-level aggregation node and the national-level main control platform.

Claims

1. A method for carbon emission accounting and dynamic monitoring of a zero-carbon cloud library based on multiple standards, characterized in that, include: S100: Obtain multi-standard accounting rule data, basic data, and interface data; standardize and map accounting boundaries, data fields, report fields, and verification rules to generate unified accounting boundary data. S200. Based on the unified accounting boundary data and traditional paper reading benchmark data, perform field validation and path identifier matching to generate traditional paper reading benchmark data; S300. Based on the unified accounting boundary data and the traditional paper reading baseline data, generate a path accounting task; The path calculation tasks include corresponding emission reductions through printing substitution, transportation substitution, warehousing substitution, physical museum operation and maintenance substitution, paperless emission reduction, and operation optimization. S400. Based on the path accounting task, collect digital reading operation data, equipment energy consumption data and green reading activity data through the library-level edge computing node to generate library-level emission reduction accounting data. S500. Based on the museum-level emission reduction accounting data, multi-museum cross-verification, trend analysis and anomaly identification are performed through the district-level aggregation node to generate verification pass data and anomaly marker data. S600. Based on the verification pass data, generate a platform reporting data packet and feedback confirmation data through the national-level main control platform; S700. Based on the anomaly marker data and the feedback confirmation data, generate early warning information and monitoring records.

2. The method according to claim 1, characterized in that, The multi-standard accounting rule data in S100 includes standard source, applicable objects, accounting boundaries, data fields, report fields, verification rules, and methodology selection basis; the basic data includes library-level identifier, operating entity, service scope, library-level equipment information, and activity record interface; the interface data includes data exchange format, interface specification, verification rules, and feedback confirmation field.

3. The method according to claim 1, characterized in that, The traditional paper reading benchmark data mentioned in S200 includes the carbon footprint benchmark value of paper production, the carbon emission factor of printing process, the carbon emission factor of logistics and transportation, the energy consumption benchmark value of warehousing, and the energy consumption benchmark value of physical library operation and maintenance. The traditional paper reading benchmark data records the source, applicable time, applicable area, version identifier, and path identifier of the benchmark data.

4. The method according to claim 1, characterized in that, The path accounting task described in S300 includes path identifiers, accounting cycles, data sources, baseline data, museum-level operational data, report fields, and verification rules; the path identifiers correspond to emission reduction through printing substitution, emission reduction through transportation substitution, emission reduction through warehousing substitution, emission reduction through physical museum operation and maintenance substitution, paperless emission reduction, and emission reduction through operational optimization.

5. The method according to claim 1, characterized in that, The library-level edge computing node described in S400 connects to the digital reading platform, equipment energy consumption sensors, and green reading activity record interface through an IoT gateway to collect access logs, reading resource call records, digital reading platform operating energy consumption data, library-level equipment energy consumption data, and activity records.

6. The method according to claim 5, characterized in that, The museum-level edge computing node in S400 performs timestamp alignment, data cleaning, standardization processing, and path identifier matching on access logs, reading resource call records, digital reading platform operation energy consumption data, museum-level equipment energy consumption data, and activity records to generate museum-level periodic monitoring data, and generates museum-level emission reduction accounting data from the museum-level periodic monitoring data.

7. The method according to claim 1, characterized in that, The multi-library cross-verification mentioned in S500 includes trend analysis of library-level emission reduction accounting data and historical monitoring data under the same path identifier, comparison of library-level emission reduction accounting data of similar zero-carbon cloud libraries, matching verification of digital reading operation data and equipment energy consumption data, and matching verification of green reading activity data and paperless emission reduction path emission reduction.

8. The method according to claim 1, characterized in that, The anomaly identification described in S500 includes abnormal fluctuations in path emission reduction, missing data fields, failure of museum-level edge computing nodes to upload data according to the accounting cycle, mismatch between digital reading operation data and equipment energy consumption data, mismatch between green reading activity data and path emission reduction, and failure to receive feedback confirmation data for data packets reported by the platform.

9. The method according to claim 1, characterized in that, The data packets reported by the platform in S600 include the museum-level identifier, accounting cycle, path identifier, path emission reduction, total emission reduction, accounting boundary description, benchmark data source, methodology selection basis, verification result, and report fields; the feedback confirmation data includes receipt confirmation, verification passed, verification failed, and retransmission request.

10. The method according to claim 1, characterized in that, The early warning information in S700 includes early warning level, museum-level identifier, accounting cycle, path identifier, anomaly type, triggering reason, and processing status; the monitoring record includes platform-reported data packets, feedback confirmation data, anomaly marker data, early warning information, and processing status.

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