Urban carbon emission monitoring method and system based on edge-cloud cooperation

Abstract

This invention relates to the field of carbon emission data monitoring technology, specifically to a method and system for urban carbon emission monitoring based on edge-cloud collaboration. The method includes: collecting carbon emission-related data for local carbon emission calculation, generating carbon emission calculation results, intermediate quantity summaries, and calculation statements; Fog nodes performing regional summary calculations based on the carbon emission calculation results and regional division rules, constructing a result evidence chain; regional autonomous monitoring nodes performing regional consistency checks on the intermediate quantity summaries and calculation statements based on regional rule mirroring, generating regional-level monitoring conclusions; constructing a computational-level self-traceable ledger corresponding to the edge nodes based on the regional-level monitoring conclusions; and real-time monitoring and traceability of carbon emission calculation results for all regions in the cloud. This invention implements a verifiable and traceable edge-cloud collaborative monitoring method for urban carbon emission results by constructing regional autonomous monitoring nodes logically parallel to Fog nodes.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, specifically to a method and system for urban carbon emission monitoring based on edge-cloud collaboration. Background Technology

[0002] With the continuous digital development of city-level energy systems, infrastructure systems, and environmental monitoring systems, data processing architectures based on cloud computing and edge computing have been widely applied in scenarios involving the collection, processing, and analysis of multi-source sensing data. Especially in data processing tasks such as carbon emission monitoring that require coverage of multiple regions, multiple entities, and long-term operation, a distributed data processing model is typically adopted, where edge nodes perform on-site data processing and the cloud performs centralized aggregation and result display. Edge computing nodes are responsible for preprocessing or preliminary calculations on the collected raw sensing data, while the cloud performs cross-regional integration, statistical analysis, and unified output based on the processing results uploaded from the edge. This reduces the cloud computing load to some extent and alleviates the bandwidth pressure caused by large-scale data backhaul. In the aforementioned cloud-edge collaborative data processing architecture, when the cloud does not directly participate in the data computation process on the edge side and only relies on the computation results reported by the edge nodes for subsequent processing, a type of technical problem related to computational reliability and result interpretability will be exposed. On the one hand, without direct participation in the edge computation process, the cloud finds it difficult to independently verify whether the edge nodes have performed legitimate data computation behavior according to the expected computation rules, computational scope, or parameter conditions. Existing technologies can usually only perform post-event verification through result integrity checks, log records, or device status information, which is insufficient to effectively constrain the legality and consistency of the computation behavior itself. On the other hand, without returning the original data and without structurally recording the intermediate computation process, the computation results output by the edge nodes can usually only correspond to timestamps, node identifiers, or simple operation logs. When faced with abnormal results or verification requirements, the cloud finds it difficult to trace the basis for the formation of the result from the causal relationship level, and can only stay at the log-level and event-level tracking level, thus limiting the ability of the distributed data processing system to support the reliability and interpretability of computation results in long-term operation scenarios. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for monitoring urban carbon emissions based on edge-cloud collaboration, in order to solve a type of technical problem related to computational reliability and result interpretability when the cloud does not directly participate in the data calculation process on the edge side and only relies on the calculation results reported by the edge nodes for subsequent processing.

[0004] To achieve the above objectives, the technical solution of the present invention is: a city carbon emission monitoring method based on edge-cloud collaboration, comprising: S1. Edge nodes collect carbon emission-related data and perform local carbon emission calculations, generating carbon emission calculation results, intermediate quantity summaries, and calculation declarations. They then send the carbon emission calculation results to the Fog node and the intermediate quantity summaries and calculation declarations to the regional computing autonomous supervision node. S2 and Fog nodes perform regional aggregate calculations based on carbon emission calculation results and regional division rules to generate regional carbon emission aggregate results; based on the regional carbon emission aggregate results, they construct result evidence chains that correspond one-to-one with edge nodes and send the regional carbon emission aggregate results and result evidence chains to the cloud. S3, the regional computing autonomous supervision node receives intermediate quantity summaries and computing declarations, performs regional collaborative consistency checks on the intermediate quantity summaries and computing declarations based on the regional rule mirror, generates regional-level supervision conclusions, and adaptively updates the regional rule mirrors based on the historical self-traceable ledger; constructs a computing-level self-traceable ledger corresponding to the edge node based on the regional-level supervision conclusions, and sends the regional-level supervision conclusions and computing-level self-traceable ledgers to the cloud. S4, based on the result evidence chain of Fog nodes in the cloud, combined with the regional rule mirroring of regional computing autonomous supervision nodes and the computational self-traceability ledger, monitors and traces the carbon emission calculation results of all regions in real time.

[0005] Preferably, in step S2, the result evidence chain is a regional-level result mapping structure established by the Fog node based on the regional carbon emission summary result and the carbon emission calculation results of the corresponding edge nodes. It is used to describe the correspondence between the regional carbon emission summary result and the carbon emission calculation results of each edge node. The data structure of the result evidence chain includes: an edge node identifier field, an edge node carbon emission calculation result field, a region identifier field participating in the regional summary calculation, and a summary index field corresponding to the regional carbon emission summary result. The result evidence chain associates the carbon emission calculation results of each edge node according to the edge node identifier field and the region identifier field, so that the regional carbon emission summary result can be traced back to each edge node participating in the regional summary calculation through the result evidence chain.

[0006] Preferably, in S3, the regional computing autonomous supervision node is an independent computing supervision node deployed at the regional level, which is logically set in parallel with the Fog node and is used to perform regional-level computing legal supervision and self-traceability management.

[0007] Preferably, in S3, the regional rule mirror is a set of regional rule states constructed by the regional computing autonomous supervision node based on the regional computing rule source from the cloud, which is used to limit the legality constraints of intermediate quantity summaries and calculation declarations in the carbon emission calculation process of edge nodes within the region; the regional rule mirror has a rule version identifier field, a regional identifier field, and a rule parameter set field, and distinguishes and calls the regional computing rule states of different time periods according to the rule version identifier field.

[0008] Preferably, in S3, the historical self-traceability ledger is a collection of historical regional-level supervision conclusions and computational-level self-traceability ledger history records continuously stored by the regional computing autonomous supervision node over multiple time periods. It is used to record the historical correlation between the carbon emission calculation behavior of edge nodes within the region and the corresponding regional-level supervision conclusions. The method of adaptively updating the regional rule mirror based on the historical self-traceability ledger is as follows: extract the historical regional-level supervision conclusions and rule version identifier fields corresponding to the current regional rule mirror from the historical self-traceability ledger, and update the rule parameter set fields in the regional rule mirror based on the historical regional-level supervision conclusions.

[0009] Preferably, in S3, the regional collaborative consistency verification is a method performed by the regional computing autonomous supervision node based on the regional rule mirror, which performs cross-node consistency comparison and rule constraint matching on intermediate quantity summaries and computational declarations from multiple edge nodes. This method is used to determine whether the carbon emission calculation behavior of multiple edge nodes meets the consistency and legality constraints under the regional rule mirror. The regional collaborative consistency verification uses the edge node identifier field, intermediate quantity summary field, computational declaration field, and rule version identifier field as verification inputs. The regional-level supervision conclusion is a regional computing legality judgment result generated by the regional computing autonomous supervision node based on the regional collaborative consistency verification. This result is used to characterize the carbon emission calculation consistency status of multiple edge nodes within the region under the current rule version. The data structure of the regional-level supervision conclusion includes a regional identifier field, a rule version identifier field, an edge node identifier set field, and a consistency judgment result field, and is associated with the intermediate quantity summary and computational declaration through the edge node identifier field.

[0010] Preferably, in S3, the computational self-traceability ledger is a regional computation process record structure constructed by the regional computational autonomous supervision node based on the regional supervision conclusion, used to record the carbon emission calculation and verification process of multiple edge nodes in the region under the corresponding rule version; the data structure of the computational self-traceability ledger includes a region identifier field, a rule version identifier field, an edge node identifier set field, an intermediate quantity summary index field, a calculation declaration index field, and a supervision conclusion identifier field corresponding to the regional supervision conclusion.

[0011] Preferably, in step S3, the computational-level self-traceability ledger establishes a reference relationship with the regional-level supervision conclusion through the supervision conclusion identifier field, and establishes a corresponding relationship with the regional rule mirror through the rule version identifier field, so that the regional-level supervision conclusion can be traced back to the edge nodes, intermediate quantity summaries and computational claims that participated in the regional collaborative consistency verification through the computational-level self-traceability ledger.

[0012] Preferably, in step S4, the cloud does not participate in the carbon emission calculation of edge nodes and the aggregated calculation of regional carbon emissions. Based on the result evidence chain of Fog nodes, and combined with the regional rule mirror of the regional computing autonomous supervision node and the computational self-traceability ledger, the cloud monitors and traces the carbon emission calculation results of all regions in real time.

[0013] On the other hand, the present invention provides an urban carbon emission monitoring system based on edge-cloud collaboration, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the aforementioned urban carbon emission monitoring method based on edge-cloud collaboration.

[0014] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: 1. In this invention, a regional computing autonomous supervision node is set up in parallel with edge nodes and Fog nodes. Without participating in carbon emission calculation and regional summary calculation, it performs regional consistency verification on the intermediate quantity summary and calculation declaration submitted by the edge nodes. It also generates regional supervision conclusions and computing-level self-traceable ledgers by combining regional rule mirroring. This allows the cloud to determine whether the regional carbon emission results conform to the established calculation rules without obtaining the original sensing data, thus solving the problem that the cloud cannot independently verify the legality of edge computing in the traditional cloud-edge collaboration system. 2. In this invention, by constructing a result evidence chain that corresponds one-to-one with the regional carbon emission summary results and edge nodes at the Fog node, and constructing a computational self-traceability ledger with rule mirroring and supervision conclusions as the core at the regional computational autonomous supervision node, the cloud can perform causal-level tracing and verification of carbon emission results in any region without recalculation or back transmission of large-scale data, thereby achieving the technical effect of verifiable results, traceable sources, and verifiable processes in the process of urban carbon emission monitoring. Attached Figure Description

[0015] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0016] Example 1, as Figure 1 As shown, the specific implementation steps of the urban carbon emission monitoring method based on edge-cloud collaboration proposed in this invention are as follows: S1. Edge nodes collect carbon emission-related data and perform local carbon emission calculations, generating carbon emission calculation results, intermediate quantity summaries, and calculation declarations. They then send the carbon emission calculation results to the Fog node and the intermediate quantity summaries and calculation declarations to the regional computing autonomous supervision node. S2 and Fog nodes perform regional aggregate calculations based on carbon emission calculation results and regional division rules to generate regional carbon emission aggregate results; based on the regional carbon emission aggregate results, they construct result evidence chains that correspond one-to-one with edge nodes and send the regional carbon emission aggregate results and result evidence chains to the cloud. S3, the regional computing autonomous supervision node receives intermediate quantity summaries and computing declarations, performs regional collaborative consistency checks on the intermediate quantity summaries and computing declarations based on the regional rule mirror, generates regional-level supervision conclusions, and adaptively updates the regional rule mirrors based on the historical self-traceable ledger; constructs a computing-level self-traceable ledger corresponding to the edge node based on the regional-level supervision conclusions, and sends the regional-level supervision conclusions and computing-level self-traceable ledgers to the cloud. S4, based on the result evidence chain of Fog nodes in the cloud, combined with the regional rule mirroring of regional computing autonomous supervision nodes and the computational self-traceability ledger, monitors and traces the carbon emission calculation results of all regions in real time.

[0017] In this embodiment S1, the edge node is a field computing node deployed near urban emission sources or energy-consuming units. It includes a data acquisition interface, a communication interface, and a local computing module, which is used to perceive the energy consumption and operating status corresponding to the emission source in real time, and perform carbon emission calculation locally to generate carbon emission calculation results corresponding to the current emission behavior. The edge node can be configured to correspond to buildings, enterprise production equipment, transportation facilities, or other emission units, so that each edge node is spatially and operationally bound to one or a group of specific emission sources, thereby giving its output calculation results a clear emission source indication. When the edge node performs local carbon emission calculation, it uses the collected carbon emission-related data as input and processes the input data based on preset carbon emission calculation rules to obtain the carbon emission calculation results. The carbon emission calculation rules include at least emission factor parameters, statistical caliber parameters, and time window parameters, which are used to map energy consumption or operating status data into carbon emission values.

[0018] In this embodiment S1, carbon emission related data is acquired by the edge node through its data acquisition interface. The data sources include energy metering devices, equipment operation monitoring devices, and emission source identification devices. The data types include at least energy consumption data, operating condition data, and emission source identification data. Energy consumption data is used to reflect the consumption of electricity, fuel, or other energy sources. Operating condition data is used to reflect the operating status of equipment or facilities within a corresponding time period. Emission source identification data is used to identify the specific emission entity corresponding to the current data. The above-mentioned carbon emission related data is continuously collected in time series form and written into the local computing module of the edge node according to a preset sampling period.

[0019] In this embodiment S1, during the local carbon emission calculation, the edge node also synchronously generates an intermediate quantity summary. The intermediate quantity summary is obtained by filtering and compressing key intermediate variables generated during the local calculation process. It is used to express the main characteristics of the calculation process. The intermediate quantity summary includes at least the energy consumption aggregate corresponding to the time period, the operating condition statistics, and the intermediate calculation result identifier associated with the emission factor, so that the summary can reflect the main characteristics of the calculation process without containing the original data. The edge node also generates a calculation declaration, which describes the calculation conditions and calculation scope of this local carbon emission calculation. It includes an edge node identifier field, a time period identifier field, a rule version identifier field, and a calculation scope parameter identifier field, which are used to indicate which version of the carbon emission calculation rule and which parameter conditions the current carbon emission calculation result is based on.

[0020] In this embodiment S1, the Fog node is an intermediate computing device deployed between the regional edge and the cloud. Other names for the Fog node are fog computing device nodes. It is used to receive and aggregate carbon emission calculation results from multiple edge nodes, perform summary aggregation calculations according to preset regional division rules, and build a result evidence chain to support traceability and verification in the cloud.

[0021] In this embodiment S1, the edge node sends the carbon emission calculation results to the Fog node in a structured data format. This structured data includes at least an edge node identifier field, a time period identifier field, and a carbon emission calculation result field, which is used to support the Fog node in performing regional aggregation calculations on the calculation results from multiple edge nodes. The sending process can be completed through a preset data communication protocol and is triggered at the end of each sampling period or at the end of each calculation window, thereby ensuring that the Fog node can continuously obtain the latest carbon emission results for regional aggregation. The edge node sends the intermediate quantity summary and calculation declaration to the regional computing autonomous supervision node. This sending process is independent of the link sent to the Fog node, which is used to enable the regional computing autonomous supervision node to perform supervision and verification on the calculation process of the edge node based only on the intermediate quantity summary and calculation declaration without receiving the carbon emission calculation results, thereby achieving separation of calculation and supervision in the system structure.

[0022] In this embodiment S2, the Fog node is a regional computing node deployed between the edge nodes and the cloud. It possesses the computing capabilities to receive, store, and summarize carbon emission calculation results reported by multiple edge nodes, as well as the ability to interact with the cloud. The Fog node receives the carbon emission calculation result dataset sent by each edge node through a communication interface, and assigns the edge nodes to different regional identifiers according to pre-configured regional division rules. Based on the assignment results, it performs regional-level summary calculations and result evidence chain construction locally, and sends the obtained regional carbon emission summary results and result evidence chains to the cloud through a data communication channel established with the cloud. The regional division rules are a set of regional mapping rules used to determine the correspondence between edge nodes and regional identifiers. Rules can be configured based on administrative divisions, energy supply zones, or business logic zones, and stored in Fog nodes in the form of a mapping table between region identifier fields and edge node identifier fields. When receiving carbon emission calculation results, Fog nodes take the edge node identifier field as input, determine the region identifier to which the edge node belongs by looking up the region division rules, and aggregate the carbon emission calculation results of multiple edge nodes under the same region identifier within a preset time window to generate a regional carbon emission summary result. The aggregation operation can include summing the carbon emission calculation result fields or grouping and summing by emission source type. During the regional summary calculation process, Fog nodes do not recalculate the original data or intermediate quantities of edge nodes, but only perform summary processing based on the carbon emission calculation result fields reported by the edge nodes.

[0023] In this embodiment S2, the result evidence chain is a regional-level result mapping structure established by the Fog node based on the regional carbon emission summary result and the carbon emission calculation result of the corresponding edge node. It is used to describe the correspondence between the regional carbon emission summary result and the carbon emission calculation result of each edge node. The data structure of the result evidence chain includes: an edge node identifier field, an edge node carbon emission calculation result field, a region identifier field participating in the regional summary calculation, and a summary index field corresponding to the regional carbon emission summary result. The result evidence chain associates the carbon emission calculation results of each edge node according to the edge node identifier field and the region identifier field, so that the regional carbon emission summary result can be traced back to each edge node participating in the regional summary calculation through the result evidence chain.

[0024] In this embodiment S2, the regional carbon emission summary result is a regional-level carbon emission data structure obtained by the Fog node performing aggregation operations on the carbon emission calculation results of multiple edge nodes under the constraints of the regional division rules. The regional carbon emission summary result includes at least a region identifier field, a time period identifier field, and a regional carbon emission summary field. The region identifier field is used to identify the region corresponding to the current summary result, the time period identifier field is used to identify the statistical time window corresponding to the current summary result, and the regional carbon emission summary field is the regional carbon emission value obtained by aggregating the carbon emission calculation results of multiple edge nodes within the region and the time window. The regional carbon emission summary result is stored in the Fog node in the form of a structured record.

[0025] In this embodiment S2, the result evidence chain is a regional-level result mapping structure established by the Fog node based on the regional carbon emission summary result and the corresponding edge node carbon emission calculation result. The result evidence chain is stored in the form of a record set or table structure, and is used to establish a traceable correspondence between the regional carbon emission summary result and the carbon emission calculation results of each edge node participating in the summary. Each record in the result evidence chain includes at least an edge node identifier field, an edge node carbon emission calculation result field, a region identifier field, and a summary index field. The edge node identifier field is used to identify a single edge node, the edge node carbon emission calculation result field is used to store the carbon emission calculation results reported by the edge node within the statistical time window, and the region identifier field is used to store the carbon emission calculation results reported by the edge node within the statistical time window. The identifier field indicates the region corresponding to the record, and the summary index field points to the carbon emission summary result of the region corresponding to the region identifier and time period identifier. When constructing the result evidence chain, the Fog node uses the region identifier field and time period identifier field in the regional carbon emission summary result as the aggregation index. It generates corresponding records for the carbon emission calculation results of multiple edge nodes in the same region and time period, and sets the region identifier field in each record to the corresponding region. The summary index field is set to point to the position or identifier of the regional carbon emission summary result in the storage structure, so that the regional carbon emission summary result can be traced back to each edge node that participated in the summary and its corresponding carbon emission calculation result through the result evidence chain.

[0026] In this embodiment S2, the summary index field is an index identifier used to associate a single record in the result evidence chain with the regional carbon emission summary result record. The summary index field can be the primary key identifier, storage location identifier, or hash identifier of the regional carbon emission summary result. When writing the regional carbon emission summary result, the Fog node generates the corresponding summary index and writes the summary index into the summary index field when constructing the result evidence chain record. The one-to-one correspondence between the regional carbon emission summary result and the carbon emission calculation result of each edge node is restored by combining the edge node identifier field and the edge node carbon emission calculation result field.

[0027] In this embodiment S3, the regional computing autonomous supervision node is an independent computing supervision node deployed at the regional level. It is set up in parallel with the Fog node in logic and is used to perform regional-level computing legal supervision and self-traceability management.

[0028] In this embodiment S3, the regional computing autonomous supervision node is an independent computing supervision node deployed at the regional level. Its physical form can be a server or edge cloud node with storage and computing capabilities. It establishes data channels with multiple edge nodes, Fog nodes, and the cloud via communication interfaces. In the system topology, it is at the same level as the Fog node. It does not receive regional carbon emission summary results or carbon emission calculation results from the Fog node, and does not participate in regional summary calculations. It only receives intermediate quantity summaries and calculation declarations sent by edge nodes, as well as regional computing rule source configurations issued by the cloud. It performs rule mirror maintenance, regional-level computing supervision, and self-traceability management, and sends the processing results to the cloud. The input to the regional computing autonomous supervision node includes data from multiple edge nodes. The nodes' intermediate quantity summaries and computational declarations, along with update information from the regional computational rule source in the cloud, output regional-level supervision conclusions, a computational-level self-traceable ledger, and an adaptively updated regional rule image. The intermediate quantity summaries and computational declarations are continuously reported to the regional computational autonomous supervision node via a preset communication protocol and time window. Upon receiving the data, the regional computational autonomous supervision node caches and archives the input according to the edge node identifier field and the time period identifier field, providing input data for subsequent regional collaborative consistency verification and the construction of the computational-level self-traceable ledger. The regional computational autonomous supervision node receives the regional computational rule source and its version information through a control channel with the cloud, constructs the regional rule image based on the received rule source, and maintains its versioned status.

[0029] In this embodiment S3, the regional rule mirror is a set of regional rule states constructed by the regional computing autonomous supervision node based on the regional computing rule source from the cloud. It is used to limit the legality constraints of intermediate quantity summaries and calculation declarations in the carbon emission calculation process of edge nodes within the region. The regional rule mirror has a rule version identifier field, a regional identifier field, and a rule parameter set field, and distinguishes and calls the regional computing rule states of different time periods according to the rule version identifier field.

[0030] In this embodiment S3, the cloud-based regional computing rule source is a set of carbon emission calculation rules configured for different regions of the city. The rule source is stored in the cloud-based rule management module in the form of a set of rule records. Each rule record includes at least a region identifier field, a rule version identifier field, an emission factor parameter field, a statistical caliber parameter field, and a time window parameter field. The region identifier field in the regional computing rule source is used to identify the region to which the rule applies; the rule version identifier field is used to identify the rule versions that take effect in the same region at different times; the emission factor parameter field is used to store the emission factor configurations corresponding to different emission source types; the statistical caliber parameter field is used to identify the measurement caliber and summarization logic used in carbon emission statistics; and the time window parameter field is used to identify the time range to which the rule applies. The regional rule mirror is generated by the regional computing autonomous supervision node. Based on the construction and maintenance of regional computing rule sources, the construction method is as follows: the regional computing autonomous supervision node pulls a set of rule records related to its own region from the cloud, filters the rule records corresponding to the current region according to the regional identifier field in the rule records, and generates a mapping table structure locally for the rule version identifier field, the regional identifier field, and the rule parameter set field to form a regionalized rule status set for the current region. Among them, the rule version identifier field is used to distinguish the rule status of different effective time periods or different configuration versions in the regional rule image, the regional identifier field is used to indicate which region the rule image belongs to, and the rule parameter set field is used to centrally store specific rule parameters such as emission factor parameters, statistical caliber parameters, and time window parameters, so that the regional computing autonomous supervision node can access the rule status related to the current region locally.

[0031] In this embodiment S3, the regional rule mirror distinguishes and calls the regional computation rule status for different time periods according to the rule version identifier field. The calling method is as follows: when the regional computation autonomous supervision node receives the intermediate quantity summary and computation declaration reported by the edge node, it matches the rule record set in the regional rule mirror with the same regional identifier field and the time window parameter field covering the time period identifier based on the time period identifier field and regional identifier information in the computation declaration. Then, it selects the currently effective rule version as the rule status used for this verification according to the rule version identifier field in the matched rule record. When performing regional collaborative consistency verification or other supervision processing, the regional computation autonomous supervision node compares and checks the intermediate quantity summary and computation declaration with the rule parameters based on the selected rule version identifier field and the corresponding rule parameter set field, so that each supervision processing is completed in a clear regional and rule version context.

[0032] In this embodiment S3, the historical self-traceability ledger is a collection of historical regional-level supervision conclusions and computational-level self-traceability ledger historical records continuously stored by the regional computing autonomous supervision node over multiple time periods. It is used to record the historical correlation between the carbon emission calculation behavior of edge nodes within the region and the corresponding regional-level supervision conclusions. The method of adaptively updating the regional rule mirror based on the historical self-traceability ledger is as follows: extract the historical regional-level supervision conclusions and rule version identifier fields corresponding to the current regional rule mirror from the historical self-traceability ledger, and update the rule parameter set fields in the regional rule mirror based on the historical regional-level supervision conclusions.

[0033] In this embodiment S3, the historical self-traceability ledger is a collection of historical records accumulated chronologically during the operation of the regional computing autonomous supervision node. The overall structure adopts a ledger table format stored by record row. Each record corresponds to the process of performing a regional collaborative consistency check and generating a regional-level supervision conclusion for a certain region and a certain rule version within a certain time period. The fields in the ledger table include at least a region identifier field, a rule version identifier field, a time period identifier field, a supervision conclusion identifier field, an edge node identifier set field, and a ledger index field. The region identifier field is used to indicate the region to which the record belongs. The rule version identifier field is used to indicate the regional rule mirror version corresponding to the record. The time period identifier field is used to identify the statistical time window corresponding to the record. The supervision conclusion identifier field is used to point to the regional-level supervision conclusion corresponding to the record. The edge node identifier set field is used to record the edge node identifier set participating in this check. The ledger index field is used to point to the detailed calculation process record stored in the computing-level self-traceability ledger. The historical self-traceability ledger is stored in the persistent storage of the regional computing autonomous supervision node. The time period identifier field and the supervision conclusion identifier field reflect the historical evolution of the time dimension and the supervision process.

[0034] In this embodiment S3, the storage method of "historical regional-level supervision conclusions and computational-level self-traceability ledger historical record set" in the historical self-traceability ledger is as follows: regional-level supervision conclusions are stored in the form of supervision conclusion record table, with the regional identifier field, rule version identifier field, time period identifier field, and consistency judgment result field as the main fields in the record table; computational-level self-traceability ledger is stored in the form of computation process record table, with the supervision conclusion identifier field as the foreign key in the record table, establishing a one-to-one or one-to-many reference relationship with the supervision conclusion identifier field in the supervision conclusion record table. By introducing the supervision conclusion identifier field and the ledger index field into the historical self-traceability ledger, a reference link from the regional-level supervision conclusion to the corresponding computational-level self-traceability ledger is formed, thereby constituting a historical association between the carbon emission calculation behavior of edge nodes and the regional-level supervision conclusion. This association is jointly determined by the regional identifier field, rule version identifier field, time period identifier field, and edge node identifier set field, enabling regional computational autonomous supervision nodes to locate the rule version used at that time, the set of participating edge nodes, and the corresponding supervision conclusion and computation process record in any historical time period.

[0035] In this embodiment S3, when the regional computing autonomous supervision node adaptively updates the regional rule mirror based on the historical self-tracing ledger, a preset update strategy is used as the trigger condition. Rule update processing is triggered when the update cycle is reached or when the proportion of abnormal identifiers in the consistency judgment results of a certain region exceeds a threshold across multiple consecutive time periods. During rule update processing, a subset of historical records corresponding to the target region and the current rule version is selected from the historical self-tracing ledger. The distribution of the consistency judgment result field in this subset is statistically analyzed, and the distribution pattern of abnormal records in the time dimension is analyzed in conjunction with the time period identifier field. Based on this, the set of rule parameters in the regional rule mirror is adjusted according to a preset parameter adjustment strategy. The parameters related to the abnormal characteristics within the field are adjusted. Parameter adjustment can include limited addition or subtraction of threshold parameters or time window parameters within the allowed parameter value range. After the parameter adjustment is completed, a new rule version identifier is generated. The updated rule parameter set field and the new rule version identifier field are written into the regional rule mirror to form a new rule status version. At the same time, the rule version evolution information corresponding to this update is recorded in the historical self-tracing ledger. The adaptive update process is completed within the regional computing autonomous supervision node. The update is based on the historical self-tracing ledger. It does not require the cloud to participate in the specific parameter adjustment one by one, but the update results can be synchronized to the cloud through the rule version change summary.

[0036] In this embodiment S3, the regional collaborative consistency check is a method performed by the regional computing autonomous supervisory node based on the regional rule mirror, which performs cross-node consistency comparison and rule constraint matching on intermediate quantity summaries and computational declarations from multiple edge nodes. This method is used to determine whether the carbon emission calculation behavior of multiple edge nodes meets the consistency and legality constraints under the regional rule mirror. The regional collaborative consistency check uses the edge node identifier field, intermediate quantity summary field, computational declaration field, and rule version identifier field as check inputs. The regional-level supervisory conclusion is a regional computing legality judgment result generated by the regional computing autonomous supervisory node based on the regional collaborative consistency check. This result is used to characterize the carbon emission calculation consistency status of multiple edge nodes in the region under the current rule version. The data structure of the regional-level supervisory conclusion includes a regional identifier field, a rule version identifier field, an edge node identifier set field, and a consistency judgment result field. These fields are associated with the intermediate quantity summary and computational declaration through the edge node identifier field.

[0037] In this embodiment S3, the regional collaborative consistency check is a cross-node consistency comparison and rule constraint matching process performed by the regional computing autonomous supervision node on intermediate quantity summaries and computational declarations from multiple edge nodes within the same region and time period under the current regional rule mirroring constraints. The verification object is the intermediate quantity summary records and corresponding computational declaration records of edge nodes aggregated under a certain region identifier and a certain time period identifier. The regional computing autonomous supervision node first filters out the set of intermediate quantity summaries and computational declarations in the current verification window based on the time period identifier field and the region identifier field. Then, it verifies whether the rule version used by each edge node is consistent with the selected version of the current regional rule mirroring based on the rule version identifier field in the computational declaration. Then, based on the rule parameter set fields in the regional rule mirror, threshold constraint checks, time window constraint checks, and trend constraint checks are performed on intermediate quantity fields such as energy consumption aggregation and operating condition statistics in the intermediate quantity summary. During the cross-node consistency comparison process, the intermediate quantity summaries of multiple edge nodes are compared horizontally to determine whether there are nodes that significantly deviate from the overall characteristics of the region or violate the regional matching constraints. Finally, based on the rule constraint matching results and cross-node comparison results, the values ​​of the consistency judgment result field are generated. The consistency judgment result field includes at least two types of values: pass and anomaly, which are used to distinguish whether the overall calculation behavior in this regional collaborative consistency verification meets the consistency and legality requirements under the current regional rule mirror.

[0038] In this embodiment S3, the regional-level supervision conclusion is a regional-level judgment record generated by the regional computing autonomous supervision node after completing a regional collaborative consistency verification. The data structure includes a regional identifier field, a rule version identifier field, an edge node identifier set field, and a consistency judgment result field. The regional identifier field identifies the region to which this supervision conclusion belongs; the rule version identifier field identifies the regional rule mirror version used when performing this verification; the edge node identifier set field records all edge node identifiers participating in this verification; and the consistency judgment result field records the final consistency judgment result of this verification. The regional-level supervision conclusion is linked to the edge node identifier set field, intermediate quantity summary, and computational voice... The association is established by storing the edge node identifier field and the time period identifier field in the intermediate quantity summary and the calculation declaration, respectively. After the regional supervision conclusion records the edge node identifier set field and the time period identifier field, the regional computing autonomous supervision node can retrieve the specific records participating in this verification in the storage structure of the intermediate quantity summary and the calculation declaration based on the edge node identifier set field and the time period identifier field. This establishes a one-to-many association relationship of "regional supervision conclusion - intermediate quantity summary and calculation declaration participating in the verification". This association relationship is further solidified in the historical self-traceability ledger through the supervision conclusion identifier field and the ledger index field to support subsequent traceability and rule adaptive update processing.

[0039] In this embodiment S3, the computational self-traceability ledger is a regional computation process record structure constructed by the regional computational autonomous supervision node based on the regional supervision conclusion. It is used to record the carbon emission calculation and verification process of multiple edge nodes in the region under the corresponding rule version. The data structure of the computational self-traceability ledger includes a region identifier field, a rule version identifier field, an edge node identifier set field, an intermediate quantity summary index field, a calculation declaration index field, and a supervision conclusion identifier field corresponding to the regional supervision conclusion.

[0040] In this embodiment S3, the computation-level self-traceability ledger is a regional computing process record structure synchronously constructed by the regional computing autonomous supervision node after completing a regional collaborative consistency verification and generating a regional-level supervision conclusion. It is stored as a record table, with each record corresponding to a regional collaborative consistency verification process within a specific region, rule version, and time period. It is locally stored in the persistent storage of the regional computing autonomous supervision node, and the verification process is jointly identified by the region identifier field, rule version identifier field, and supervision conclusion identifier field. The difference between the computation-level self-traceability ledger and the historical self-traceability ledger is that the computation-level self-traceability ledger records the process-level information corresponding to a single regional collaborative consistency verification, while the historical self-traceability ledger... The ledger records a collection of historical records accumulated over multiple time periods. In the computation-level self-traceable ledger, the time dimension is indirectly linked to the time period identifier field stored in the supervision conclusion through the supervision conclusion identifier field of the regional supervision conclusion. In the historical self-traceable ledger, it is directly reflected through the time period identifier field. The data structure of the computation-level self-traceable ledger includes a regional identifier field, a rule version identifier field, an edge node identifier set field, an intermediate quantity summary index field, a computation declaration index field, and a supervision conclusion identifier field. Each record corresponds to one regional supervision conclusion, and the smallest granularity of the record is "single regional collaborative consistency verification," which is created synchronously by the regional computational autonomous supervision node when generating the regional supervision conclusion.

[0041] In this embodiment S3, the construction method of the computation-level self-traceable ledger is as follows: After the regional computational autonomous supervision node completes the regional collaborative consistency verification within a certain region and a certain time period and generates the corresponding regional-level supervision conclusion, it reads the edge node identifier set, intermediate quantity summary record set, and computation declaration record set that participated in the verification process, generates a new computation-level self-traceable ledger record, sets the regional identifier field to the regional identifier corresponding to this verification, sets the rule version identifier field to the rule version identifier of the regional rule mirror used when executing this verification, sets the edge node identifier set field to the set of all edge node identifiers that participated in this verification, and sets the supervision... The conclusion identifier field is set as a unique identifier for the regional-level supervision conclusion; the intermediate quantity summary index field is used to store a set of reference identifiers pointing to the target record in the intermediate quantity summary storage structure inside the regional computing autonomous supervision node. Each reference identifier can uniquely locate an intermediate quantity summary record. The computation declaration index field is used to store a set of reference identifiers pointing to the target record in the computation declaration storage structure inside the regional computing autonomous supervision node. Each reference identifier can uniquely locate a computation declaration record. The computation-level self-traceable ledger does not directly repeat the specific content of the intermediate quantity summary and computation declaration, but establishes a reference relationship with the intermediate quantity summary storage structure and the computation declaration storage structure through the index field.

[0042] In this embodiment S3, the computational-level self-traceability ledger establishes a reference relationship with the regional-level supervision conclusion through the supervision conclusion identifier field, and establishes a corresponding relationship with the regional rule mirror through the rule version identifier field, so that the regional-level supervision conclusion can be traced back to the edge nodes, intermediate quantity summaries and computational claims that participated in the regional collaborative consistency verification through the computational-level self-traceability ledger.

[0043] In this embodiment S3, the specific method by which the computational-level self-traceability ledger establishes a reference relationship between the supervision conclusion identifier field and the regional-level supervision conclusion is as follows: Regional-level supervision conclusions are centrally stored in the regional-level supervision conclusion record table. Each supervision conclusion record has a unique supervision conclusion identifier field. The computational-level self-traceability ledger stores the value of this supervision conclusion identifier field, thereby logically forming a one-to-one or one-to-many reference relationship from the regional-level supervision conclusion record table to the computational-level self-traceability ledger record table. When a regional computational autonomous supervision node performs traceability or statistical processing, it can retrieve the corresponding record in the computational-level self-traceability ledger starting from a certain supervision conclusion identifier, realizing a one-way jump from the supervision conclusion to the computation process record; the computational-level self-traceability ledger... The specific method for establishing the correspondence between the rule version identifier field and the regional rule mirror is as follows: Each rule status record in the regional rule mirror has a rule version identifier field. The rule version identifier field in the computation-level self-traceable ledger record has the same value as the rule version identifier field in the regional rule mirror. When the regional computational autonomous supervision node needs to restore the rule status used in a certain verification, it uses the rule version identifier field in the computation-level self-traceable ledger record as the query condition to locate the corresponding rule status record in the regional rule mirror. Thus, during the tracing process, the set of rule parameters used at that time is restored at the same time. The correspondence between the rule version identifier field and the regional rule mirror is used to ensure that the intermediate quantity summary and computation declaration can be interpreted in the correct rule version context when tracing the computation process.

[0044] In this embodiment S3, the specific process of tracing the regional-level supervision conclusion to the edge nodes, intermediate quantity summaries, and computational declarations participating in the regional collaborative consistency verification through the computational-level self-traceability ledger is as follows: The regional computational autonomous supervision node starts from the supervision conclusion identifier field of the target regional-level supervision conclusion, searches the computational-level self-traceability ledger for ledger records whose supervision conclusion identifier field is equal to the identifier, obtains the edge node identifier set field, intermediate quantity summary index field, computational declaration index field, and rule version identifier field from the record, uses the edge node identifier set field to determine the set of edge nodes participating in this verification, uses the intermediate quantity summary index field to locate all intermediate quantity summary records participating in this verification in the intermediate quantity summary storage structure, and uses computational... The declaration index field locates all computation declaration records involved in this verification in the computation declaration storage structure. The edge node identifier field associates the located intermediate quantity summary and computation declaration with the corresponding edge nodes. When it is necessary to further restore the rule context at that time, the regional computation autonomous supervision node queries the corresponding rule status record in the regional rule mirror according to the rule version identifier field, thereby obtaining the set of rule parameters used at that time. This realizes the step-by-step jump from the regional supervision conclusion to the computation level self-traceable ledger, and then to the intermediate quantity summary and computation declaration and the rule status. The entire traceability process is completed based on the index field and the identifier field. The carbon emission calculation results are not recalculated. The calculation process and rule environment at that time are restored only by reading and referencing.

[0045] In this embodiment S4, the cloud does not participate in the carbon emission calculation of edge nodes and the aggregated calculation of regional carbon emissions. Based on the result evidence chain of Fog nodes, and combined with the regional rule mirror of the regional computing autonomous supervision node and the computational self-traceability ledger, the carbon emission calculation results of all regions are monitored and traced in real time.

[0046] In this embodiment S4, the cloud serves as a centralized management and query node deployed at the top level of the urban carbon emission monitoring system. Through data communication channels established with Fog nodes and regional computing autonomous supervision nodes, it receives the regional carbon emission summary results, result evidence chains, regional supervision conclusions, computing-level self-traceability ledgers, and regional rule mirror version information corresponding to each region. In the system structure, it does not perform local carbon emission calculations at edge nodes or perform regional carbon emission summary calculations at Fog nodes. Instead, it performs supervision and traceability processing based on the above-mentioned structured result data, and stores and provides the supervision processing results and traceability query responses in the cloud.

[0047] In this embodiment S4, the cloud-based supervision processing is a process triggered by the cloud at a preset time period or when it receives data update events from Fog nodes and regional computing autonomous supervision nodes. In the supervision processing, the cloud first selects the regional carbon emission summary results and their corresponding result evidence chain record set for the target region from the data set reported by the Fog nodes based on the region identifier field and time period identifier field. Then, based on the identifier information of the same region and time period, it selects the corresponding regional-level supervision conclusion from the regional-level supervision conclusion record set, and further searches for the computational-level self-traceable ledger record corresponding to the supervision conclusion identifier field of the regional-level supervision conclusion. After completing the above data selection... The cloud platform uses the edge node identifier field and the region identifier field in the result evidence chain as a basis to verify whether the set of edge nodes participating in the regional carbon emission aggregation calculation is consistent with the edge node identifier set field in the computational self-traceability ledger. Based on the rule version identifier field in the computational self-traceability ledger, it verifies whether it matches the rule version identifier field recorded in the regional supervision conclusion and the regional rule mirror. By verifying the consistency of the edge node set and the rule version, a supervision result record is generated and written into the cloud supervision record structure to characterize the structural consistency status between the carbon emission calculation results, result evidence chain, regional supervision conclusion and computational self-traceability ledger of each region.

[0048] In this embodiment S4, the cloud-based traceability processing is the process executed by the cloud when it receives a traceability request from an upper-layer application or management system. The traceability request includes at least a target area identifier field and a target time period identifier field. After receiving the traceability request, the cloud first locates the corresponding regional carbon emission summary result and its result evidence chain record set in the data set reported by the Fog node based on the area identifier field and the time period identifier field. Then, it locates the supervision result record and regional supervision conclusion identifier information corresponding to the regional carbon emission summary result in the cloud supervision record structure. Then, it obtains the corresponding regional supervision conclusion in the regional supervision conclusion record table using the regional supervision conclusion identifier information as the retrieval condition. Finally, it searches for the ledger record in the computational self-traceability ledger where the supervision conclusion identifier field is equal to the identifier. From this ledger record, it reads the edge node identifier set field, intermediate quantity summary index field, computational declaration index field, and rule version identifier field. The intermediate quantity summary index field and the calculation declaration index field locate all intermediate quantity summary and calculation declaration records participating in this regional collaborative consistency verification in the intermediate quantity summary storage structure and calculation declaration storage structure mapping set reported by the regional computing autonomous supervision node. A correspondence is established between the edge node identifier set field and the edge node identifier field in the above records, forming an end-to-end traceability path from the regional carbon emission summary result to the edge node carbon emission calculation result, intermediate quantity summary and calculation declaration. When it is necessary to restore the rule environment, the corresponding rule status record is located in the regional rule mirror with the rule version identifier field as the search condition. The rule parameter set field is returned to the requester as part of the traceability report. The entire traceability process is completed by jumping sequentially based on the result evidence chain, regional supervision conclusion, computing-level self-traceability ledger and the identifier field and index field in the regional rule mirror. No numerical recalculation is performed on the edge node carbon emission calculation result and the regional carbon emission summary result.

[0049] Example 2: The urban carbon emission monitoring system based on edge-cloud collaboration proposed in this invention is applied to the urban carbon emission monitoring method based on edge-cloud collaboration proposed in Example 1. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the urban carbon emission monitoring method based on edge-cloud collaboration in Example 1.

[0050] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for monitoring urban carbon emissions based on edge-cloud collaboration, characterized in that, Includes the following steps: S1. Edge nodes collect carbon emission-related data and perform local carbon emission calculations, generating carbon emission calculation results, intermediate quantity summaries, and calculation declarations. They then send the carbon emission calculation results to the Fog node and the intermediate quantity summaries and calculation declarations to the regional computing autonomous supervision node. S2 and Fog nodes perform regional summary calculations based on carbon emission calculation results and regional division rules to generate regional carbon emission summary results. Based on the regional carbon emission summary results, construct a result evidence chain that corresponds one-to-one with the edge nodes, and send the regional carbon emission summary results and result evidence chain to the cloud. S3. The regional computing autonomous supervision node receives intermediate quantity summaries and computation declarations, performs regional collaborative consistency checks on intermediate quantity summaries and computation declarations based on the regional rule mirror, generates regional-level supervision conclusions, and adaptively updates the regional rule mirror based on the historical self-traceable ledger. Based on the regional-level supervision conclusions, a computational-level self-traceable ledger corresponding to the edge nodes is constructed, and the regional-level supervision conclusions and the computational-level self-traceable ledger are sent to the cloud. S4, based on the result evidence chain of Fog nodes in the cloud, combined with the regional rule mirroring of regional computing autonomous supervision nodes and the computational self-traceability ledger, monitors and traces the carbon emission calculation results of all regions in real time.

2. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 1, characterized in that: In S2, the result evidence chain is a regional-level result mapping structure established by the Fog node based on the regional carbon emission summary result and the carbon emission calculation result of the corresponding edge node, which is used to describe the correspondence between the regional carbon emission summary result and the carbon emission calculation result of each edge node. The data structure of the result evidence chain includes: an edge node identifier field, an edge node carbon emission calculation result field, a region identifier field for participating in the regional summary calculation, and a summary index field corresponding to the regional carbon emission summary result; The result evidence chain links the carbon emission calculation results of each edge node according to the edge node identifier field and the region identifier field, so that the regional carbon emission summary result can be traced back to each edge node that participated in the regional summary calculation through the result evidence chain.

3. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 2, characterized in that: In S3, the regional computing autonomous supervision node is an independent computing supervision node deployed at the regional level. It is set up in parallel with the Fog node in logic and is used to perform regional-level computing legal supervision and self-traceability management.

4. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 3, characterized in that: In S3, the regional rule mirror is a set of regional rule states constructed by the regional computing autonomous supervision node based on the regional computing rule source from the cloud. It is used to limit the legality constraints of intermediate quantity summaries and calculation declarations in the carbon emission calculation process of edge nodes within the region. The regional rule mirror has a rule version identifier field, a regional identifier field, and a rule parameter set field, and distinguishes and calls the regional computing rule states of different time periods according to the rule version identifier field.

5. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 4, characterized in that: In S3, the historical self-traceability ledger is a collection of historical regional-level supervision conclusions and computational-level self-traceability ledger history records continuously stored by the regional computing autonomous supervision node over multiple time periods. It is used to record the historical correlation between the carbon emission calculation behavior of edge nodes within the region and the corresponding regional-level supervision conclusions. The method of adaptively updating the regional rule mirror based on the historical self-traceability ledger is as follows: extract the historical regional-level supervision conclusions and rule version identifier fields corresponding to the current regional rule mirror from the historical self-traceability ledger, and update the rule parameter set fields in the regional rule mirror based on the historical regional-level supervision conclusions.

6. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 5, characterized in that: In S3, the regional collaborative consistency verification is a method performed by the regional computing autonomous supervision node based on the regional rule mirror, which compares the intermediate quantity summaries and computational declarations from multiple edge nodes across nodes and matches them against rule constraints. This method is used to determine whether the carbon emission calculation behavior of multiple edge nodes meets the consistency and legality constraints under the regional rule mirror. The regional collaborative consistency verification uses the edge node identifier field, intermediate quantity summary field, computational declaration field, and rule version identifier field as verification inputs. The regional-level supervision conclusion is a regional computing legality judgment result generated by the regional computing autonomous supervision node based on the regional collaborative consistency verification. It is used to characterize the carbon emission calculation consistency status of multiple edge nodes in the region under the current rule version. The data structure of the regional-level supervision conclusion includes a regional identifier field, a rule version identifier field, an edge node identifier set field, and a consistency judgment result field, and is associated with the intermediate quantity summary and calculation declaration through the edge node identifier field.

7. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 6, characterized in that: In S3, the computational self-traceability ledger is a regional computation process record structure constructed by the regional computational autonomous supervision node based on the regional supervision conclusion. It is used to record the carbon emission calculation and verification process of multiple edge nodes in the region under the corresponding rule version. The data structure of the computational self-traceability ledger includes a region identifier field, a rule version identifier field, an edge node identifier set field, an intermediate quantity summary index field, a calculation declaration index field, and a supervision conclusion identifier field corresponding to the regional supervision conclusion.

8. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 7, characterized in that: In step S3, the computation-level self-traceability ledger establishes a reference relationship with the regional-level supervision conclusion through the supervision conclusion identifier field, and establishes a corresponding relationship with the regional rule mirror through the rule version identifier field, so that the regional-level supervision conclusion can be traced back to the edge nodes, intermediate quantity summaries and computational claims that participated in the regional collaborative consistency verification through the computation-level self-traceability ledger.

9. The urban carbon emission monitoring method based on edge-cloud collaboration according to claim 8, characterized in that: In S4, the cloud does not participate in the carbon emission calculation of edge nodes and the aggregated calculation of regional carbon emissions. Based on the result evidence chain of Fog nodes, and combined with the regional rule mirror of the regional computing autonomous supervision node and the computing-level self-traceable ledger, the cloud monitors and traces the carbon emission calculation results of all regions in real time.

10. An urban carbon emission monitoring system based on edge-cloud collaboration, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the urban carbon emission monitoring method based on edge-cloud collaboration as described in any one of claims 1-9.

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