Accurate carbon emission cost accounting method based on multi-source data fusion

Abstract

An accurate carbon emission cost accounting method based on multi-source data fusion, the method comprising: connecting to a heterogeneous data source by means of a standardized interface, so as to collect data; adding a unified identifier to the data and then transmitting the data to a central processing module for parsing key information; and dynamically updating an association weight and generating an accounting report. Preferably, an intelligent analysis engine is used as a virtual node, and efficient data transmission and processing are realized by means of links, a load balancing group and a broadcast group. The method can integrate multi-source data, improve the accounting precision and dynamic adaptability, and can also enhance the flexibility and cross-industry universality of a system.

Description

Multi-source data fusion carbon emission cost accurate accounting method TECHNICAL FIELD

[0001] The application belongs to the technical field of carbon emission accounting and management, and specifically relates to a multi-source data fusion carbon emission cost accurate accounting method. BACKGROUND

[0002] The application of carbon emission accounting methods in various industries is continuously deepening, becoming an important foundation for promoting the development of low-carbon economy. However, the existing accounting methods have certain limitations in data fusion, multi-source information integration, and cost accounting accuracy, and it is difficult to meet the demand for high-precision accounting in complex scenarios.

[0003] After searching, the patent with publication number CN118692610B discloses a recycled asphalt sand, its design method and its carbon emission accounting method, and the publication date is November 26, 2024. The patent calculates the blending ratio of old asphalt mixture, tailings sand, rock asphalt and SBS modified asphalt, and analyzes the film forming properties of different particle size aggregates in combination with the asphalt film thickness, to realize the determination of the best asphalt dosage of recycled asphalt sand and carbon emission accounting. However, this technical solution mainly aims at the carbon emission accounting of specific materials (such as recycled asphalt sand), lacks the comprehensive fusion capability of multi-source data such as energy consumption, process flow and supply chain links, and the application scope is limited. In addition, this method lacks consideration of dynamic changing factors (such as real-time monitoring data or market fluctuations), which may affect its accuracy and adaptability in complex scenarios. TECHNICAL PROBLEM

[0004] The above problems show that the existing carbon emission accounting methods still have room for improvement in multi-source data fusion, dynamic adaptability and cross-industry universality. Therefore, the present application aims to provide a multi-source data fusion carbon emission cost accurate accounting method, which integrates multiple data sources, combines real-time monitoring and dynamic adjustment mechanism, and further improves the accuracy and universality of carbon emission accounting, to meet the demand for efficient and intelligent accounting methods in modern low-carbon economy. TECHNICAL SOLUTION

[0005] The purpose of the present application is to provide a multi-source data fusion carbon emission cost accurate accounting method to solve the shortcomings of the prior art in data fusion, dynamic adaptability and cross-industry universality. By integrating multiple data sources and combining real-time monitoring and dynamic adjustment mechanism, the present application proposes a new accounting method that can meet the high-precision demand in complex scenarios.

[0006] The application provides a multi-source data fusion carbon emission cost accurate accounting method, characterized by comprising the following steps: firstly, connecting the data acquisition unit to a plurality of heterogeneous data sources through a standardized interface, wherein the heterogeneous data sources include energy consumption monitoring equipment, process flow control systems and supply chain management platforms; secondly, after adding a uniform identifier to the collected original data in the data acquisition unit, sending the data to a central processing module through a preset transmission protocol, wherein the central processing module analyzes the uniform identifier and extracts key accounting information; the key accounting information includes a timestamp, a source identifier and an association weight; thirdly, receiving the processing result returned by the central processing module, wherein the central processing module updates the association weight in the uniform identifier after completing data processing; and finally, generating a final accounting report by the data output unit according to the received processing result.

[0007] Preferably, the method further comprises the following steps: establishing a transmission path for each data acquisition unit to all heterogeneous data sources and the central processing module, and establishing a transmission path for each central processing module to all data acquisition units and heterogeneous data sources. The central processing module is set as an intelligent analysis engine, each intelligent analysis engine is regarded as an independent data processing node, is assigned a virtual node number and occupies a virtual interface range, and the transmission path to the intelligent analysis engine is regarded as the transmission path to the virtual interface; the uniform identifier contains a timestamp, a source identifier, an association weight and a data check code, and the transmission rule comprises: transmitting data to the intelligent analysis engine mounted by the data acquisition unit; specifically: according to the mapping relationship between the interface and the source identifier, transmitting data from the channel corresponding to the data acquisition unit to the intelligent analysis engine mounted by the data acquisition unit, and the association weight in the uniform identifier is the virtual interface number occupied by the intelligent analysis engine; the channel interconnecting the data acquisition units is called a link.

[0008] The transmission rule further comprises: transmitting data to the intelligent analysis engine mounted by the remote data acquisition unit; specifically: according to the mapping relationship between the interface and the transmission port, transmitting data from the link 0 of the data acquisition unit to the central switching module, and the association weight in the uniform identifier is the virtual interface number occupied by the intelligent analysis engine; after receiving the data, the central switching module analyzes the virtual interface number occupied by the intelligent analysis engine in the uniform identifier, and according to the mapping relationship between the interface and the transmission port on the central switching module, transmits the data from the corresponding link to the remote data acquisition unit; after receiving the data, the remote data acquisition unit transmits the data from the corresponding link except the link 0 to the intelligent analysis engine mounted by the remote data acquisition unit according to the mapping relationship between the interface and the transmission port on the remote data acquisition unit and the virtual interface number occupied by the intelligent analysis engine in the uniform identifier.

[0009] The transmission rule further comprises: transmitting data to the intelligent analysis engine mounted by the plurality of data acquisition units; wherein the intelligent analysis engine mounted by the plurality of data acquisition units is converted into a virtual interface occupied by the plurality of intelligent analysis engines to form a load balancing group, so as to realize load sharing among the plurality of intelligent analysis engines, and the associated weight in the uniform identifier is the virtual interface number occupied by the intelligent analysis engine.

[0010] The transmission rule further comprises: transmitting data to the intelligent analysis engine mounted by the plurality of data acquisition units, converting and transmitting to a broadcast group, and the member interface of the broadcast group is the virtual interface occupied by the plurality of intelligent analysis engines; the data acquisition unit broadcast group only replicates one copy of the data to the remote central switching module except for the interface of the unit replicating the data, the remote central switching module replicates the data to the remote data acquisition unit according to the broadcast group, and the remote data acquisition unit continues to execute the broadcast replication; and the associated weight in the uniform identifier is the broadcast group.

[0011] Updating the uniform identifier comprises: the intelligent analysis engine mounted by the data acquisition unit updating the associated weight in the uniform identifier to the virtual interface occupied by the other intelligent analysis engine of the data acquisition unit, and the central switching module transmitting data to the other intelligent analysis engine of the data acquisition unit through the corresponding link according to the mapping relationship between the interface and the transmission port.

[0012] Updating the uniform identifier further comprises: the intelligent analysis engine mounted by the data acquisition unit updating the associated weight in the uniform identifier to the virtual interface occupied by the intelligent analysis engine mounted by the remote data acquisition unit, and the central switching module transmitting data to the central switching module through the corresponding link 0 according to the mapping relationship between the interface and the transmission port; the central switching module transmits to the remote data acquisition unit through the corresponding link according to the mapping relationship between the interface and the transmission port, and the remote data acquisition unit transmits to the intelligent analysis engine through the corresponding link according to the mapping relationship between the interface and the transmission port.

[0013] Updating the uniform identifier further comprises: the intelligent analysis engine mounted by the data acquisition unit updating the associated weight in the uniform identifier to the load balancing group, and the member interface of the load balancing group is the virtual interface occupied by the intelligent analysis engine of the data acquisition unit; the central switching module executes the member interface selection of the load balancing group; and the associated weight in the uniform identifier is the load balancing group.

[0014] Updating the uniform identifier further comprises: the intelligent analysis engine mounted by the data acquisition unit updating the associated weight in the uniform identifier to the broadcast group, and the member interface of the broadcast group is the virtual interface occupied by the intelligent analysis engine of the data acquisition unit; the central switching module of each data acquisition unit executes the data replication of the member interface; and the associated weight in the uniform identifier is the broadcast group. Advantageous effects

[0015] In summary, the data acquisition unit connects multiple heterogeneous data sources through a standardized interface, and the intelligent analysis engine is regarded as a virtual data processing node. The data processed by the intelligent analysis engine is returned to the data acquisition unit, which can continue to use the uniform identifier for data transmission, reducing resource waste while enhancing the flexibility and adaptability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a system architecture diagram of the multi-source data fusion carbon emission cost accurate accounting method according to an embodiment of the present application, which shows the connection relationship between the data acquisition unit, the heterogeneous data source and the central processing module.

[0017] Figure 2 is a logical structure diagram of the data transmission path according to an embodiment of the present application, which details the transmission rules and link allocation between the data acquisition unit, the intelligent analysis engine and the central switching module.

[0018] Figure 3 is a uniform identifier update flowchart according to an embodiment of the present application, which describes how the intelligent analysis engine updates the associated weights in the uniform identifier after processing data.

[0019] Figure 4 is a working mechanism diagram of the load balancing group according to an embodiment of the present application, which shows the process of load sharing through the virtual interface binding of multiple intelligent analysis engines.

[0020] Figure 5 is a data replication transmission diagram of the broadcast group according to an embodiment of the present application, which illustrates how data is efficiently replicated and distributed through the broadcast group member interface.

[0021] Figure 6 is an accounting report generation flowchart according to an embodiment of the present application, which presents the overall process from data processing to final accounting report output.

[0022] The reference signs are as follows:

[0023] 1. Data acquisition unit; 2. Heterogeneous data source; 3. Central processing module; 4. Intelligent analysis engine; 5. Central switching module; 6. Link; 7. Uniform identifier; 8. Load balancing group; 9. Broadcast group; 10. Accounting report. Best mode of the present application

[0024] The application provides a multi-source data fusion carbon emission cost accurate accounting method, and the specific implementation manner is as follows. Fig. 1 shows a system architecture schematic diagram, wherein a data acquisition unit 1 is connected with a heterogeneous data source 2 through a standardized interface, the heterogeneous data source 2 includes energy consumption monitoring equipment, process flow control system and supply chain management platform. A transmission path is established between the data acquisition unit 1 and a central processing module 3 through a link 6, the central processing module 3 internally includes multiple intelligent analysis engines 4, each intelligent analysis engine 4 is assigned a virtual node number and occupies a virtual interface range. An interconnection channel is established between the data acquisition unit 1 and a central switching module 5 through a link 0, the central switching module 5 is responsible for analyzing the associated weight in the uniform identifier 7 and distributing data to the corresponding data acquisition unit 1 or intelligent analysis engine 4 according to the mapping relationship.

[0025] In the actual operation process, the data acquisition unit 1 first collects original data from the heterogeneous data source 2, and adds a uniform identifier 7 to each piece of data. The uniform identifier 7 includes a timestamp, a source identifier, an associated weight and a data check code. The data acquisition unit 1 sends the data with the uniform identifier 7 to the central processing module 3 according to the preset transmission protocol. After receiving the data, the central processing module 3 analyzes the key information in the uniform identifier 7 and extracts the timestamp, source identifier and associated weight required for accounting. The intelligent analysis engine 4 updates the associated weight in the uniform identifier 7 after processing the data, and then returns the processing result to the data acquisition unit 1. The data acquisition unit 1 generates a final accounting report 10 according to the return result, and the process is as shown in Fig. 6.

[0026] Fig. 2 details the logical structure of the data transmission path. The transmission rules between the data acquisition unit 1 and the intelligent analysis engine 4 include three main scenarios. The first scenario is to transmit data to the intelligent analysis engine 4 mounted by the data acquisition unit 1, at this time the associated weight in the uniform identifier 7 is the virtual interface number occupied by the intelligent analysis engine 4. The second scenario is to transmit data to the intelligent analysis engine 4 mounted by the remote data acquisition unit 1, at this time the data acquisition unit 1 sends data to the central switching module 5 through the link 0, the central switching module 5 analyzes the virtual interface number in the uniform identifier 7 to parse the remote data acquisition unit 1 where the target intelligent analysis engine 4 is located, and transmits data to the remote data acquisition unit 1 through the corresponding link 6. The third scenario is to transmit data to the intelligent analysis engine 4 mounted by multiple data acquisition units 1, at this time multiple intelligent analysis engines 4 form a load balancing group 8 through virtual interface binding, as shown in Fig. 4, the member interface of the load balancing group 8 is the virtual interface occupied by the multiple intelligent analysis engines 4, and the associated weight in the uniform identifier 7 is the load balancing group 8.

[0027] Further, when data needs to be replicated and transmitted to multiple data collection units 1 mounted smart analysis engines 4, the broadcast group 9 is used to achieve efficient replication and distribution. As shown in Figure 5, the member interface of the broadcast group 9 is a virtual interface occupied by multiple smart analysis engines 4, and the data collection unit 1 sends a copy of the data to the central switching module 5, which replicates the data to the remote data collection unit 1 according to the member interface of the broadcast group 9. The remote data collection unit 1 continues to perform broadcast replication until all target smart analysis engines 4 have received the data. The association weight in the uniform identifier 7 is updated to the broadcast group 9 during this process.

[0028] Figure 3 describes the update process of the uniform identifier 7. After processing the data, the smart analysis engine 4 updates the association weight in the uniform identifier 7 according to the processing result. For example, when the data needs to be transmitted to other smart analysis engines 4 of the data collection unit 1, the smart analysis engine 4 updates the association weight in the uniform identifier 7 to the virtual interface number occupied by the other smart analysis engines 4 of the data collection unit 1, and transmits the data to the target smart analysis engine 4 through the link 6. When the data needs to be transmitted to the smart analysis engine 4 mounted on the remote data collection unit 1, the smart analysis engine 4 updates the association weight in the uniform identifier 7 to the virtual interface number occupied by the smart analysis engine 4 mounted on the remote data collection unit 1, and sends the data to the central switching module 5 through the link 0. The central switching module 5 analyzes the uniform identifier 7 and transmits the data to the remote data collection unit 1. For the load balancing group 8 and the broadcast group 9 scenarios, the smart analysis engine 4 updates the association weight in the uniform identifier 7 to the load balancing group 8 or the broadcast group 9, respectively, and the central switching module 5 performs load balancing selection or broadcast replication operation.

[0029] In practical applications, assume that a manufacturing enterprise needs to account for the carbon emission cost in its production process. The energy consumption monitoring devices, process flow control systems, and supply chain management platforms of the enterprise are used as heterogeneous data sources 2, which are connected to the data collection unit 1 through standardized interfaces. The raw data collected by the data collection unit 1 includes energy consumption, production process parameters, and supply chain logistics information, and a uniform identifier 7 is added to each piece of data. The data collection unit 1 sends the data to the central processing module 3 through the link 6, and the smart analysis engine 4 in the central processing module 3 extracts key accounting information according to the timestamp and source identifier in the uniform identifier 7, and dynamically adjusts the accounting model in combination with real-time monitoring data. After the smart analysis engine 4 completes data processing, it updates the association weight in the uniform identifier 7, and returns the processing result to the data collection unit 1. The data collection unit 1 generates an accounting report 10 according to the processing result, providing accurate carbon emission cost accounting basis for the enterprise.

[0030] In the above embodiments, the connection relationship between the data acquisition unit 1 and the heterogeneous data source 2 is realized through a standardized interface, ensuring the compatibility of different data sources. The link 6 between the data acquisition unit 1 and the central processing module 3 adopts a preset transmission protocol, ensuring the stability and reliability of data transmission. The intelligent analysis engine 4 in the central processing module 3 realizes the function of an independent data processing node through a virtual interface number, enhancing the flexibility and scalability of the system. The central exchange module 5 realizes precise control of data distribution by analyzing the association weight in the uniform identifier 7, supporting data transmission requirements in various complex scenarios. The design of the load balancing group 8 and the broadcast group 9 further improves the resource utilization and data transmission efficiency of the system, meeting the needs of large-scale data processing. Embodiments of the present application

[0031] In order to better enable relevant persons in the technical field to fully understand and implement the present application, the specific implementation principles of the present application are further supplemented below in conjunction with a specific application scenario.

[0032] In the production process of a certain manufacturing enterprise, it is necessary to accurately account for its carbon emission cost. Energy consumption monitoring equipment, process flow control system and supply chain management platform as heterogeneous data source 2 are connected with data acquisition unit 1 through standardized interface. The data acquisition unit 1 first collects raw data from these heterogeneous data sources, and adds a uniform identifier 7 to each data. The uniform identifier 7 contains timestamp, source identification, association weight and data check code, ensuring the uniqueness and traceability of the data. Subsequently, the data acquisition unit 1 sends the data with uniform identifier 7 to the central processing module 3 according to the preset transmission protocol, and the link 6 plays a key role in this process, ensuring the stability and reliability of data transmission.

[0033] After receiving the data, the central processing module 3 parses the key information in the uniform identifier 7, including the timestamp, source identification and association weight. The intelligent analysis engine 4 as an independent data processing node is assigned a virtual node number and occupies a range of virtual interfaces. After receiving the data, the intelligent analysis engine 4 dynamically adjusts the accounting model according to the real-time monitoring data. For example, when the energy consumption fluctuates, the intelligent analysis engine 4 will update the parameters in the accounting model in combination with the real-time data to ensure the accuracy of the accounting result. After completing data processing, the intelligent analysis engine 4 updates the association weight in the uniform identifier 7 and returns the processing result to the data acquisition unit 1. This process is shown in Figure 3, the intelligent analysis engine 4 updates the association weight in the uniform identifier 7 according to the processing result, thereby guiding the transmission path of subsequent data.

[0034] In the process of data transmission, there are various scenarios. For the intelligent analysis engine 4 mounted by the data acquisition unit 1, the data is directly transmitted to the target intelligent analysis engine 4 through the link 6, and at this time the association weight in the unified identifier 7 is the virtual interface number occupied by the intelligent analysis engine 4. If the data needs to be transmitted to the intelligent analysis engine 4 mounted by the remote data acquisition unit 1, the data acquisition unit 1 sends the data to the central switching module 5 through the link 0. The central switching module 5 analyzes the virtual interface number in the unified identifier 7, determines the remote data acquisition unit 1 where the target intelligent analysis engine 4 is located, and transmits the data to the remote data acquisition unit 1 through the corresponding link 6. This process is shown in Figure 2, which details the logical structure of the data transmission path.

[0035] When data needs to be transmitted to multiple intelligent analysis engines 4 mounted by data acquisition units 1, the system uses the load balancing group 8 to improve resource utilization. Multiple intelligent analysis engines 4 are bundled through virtual interfaces to form a load balancing group 8, and the association weight in the unified identifier 7 is updated to the load balancing group 8. The central switching module 5 selects the optimal path according to the member interfaces of the load balancing group 8 to distribute data to multiple intelligent analysis engines 4. This mechanism is shown in Figure 4, which shows how multiple intelligent analysis engines 4 can achieve load sharing through virtual interface bundling.

[0036] In addition, when data needs to be transmitted to multiple intelligent analysis engines 4 mounted by data acquisition units 1, the system uses the broadcast group 9 to achieve efficient replication and distribution. The member interfaces of the broadcast group 9 are virtual interfaces occupied by multiple intelligent analysis engines 4, and the data acquisition unit 1 sends a copy of the data to the central switching module 5. The central switching module 5 replicates the data to the remote data acquisition unit 1 according to the member interfaces of the broadcast group 9, and the remote data acquisition unit 1 continues to perform broadcast replication until all target intelligent analysis engines 4 have received the data. This process is shown in Figure 5, which illustrates how data is efficiently replicated and distributed through the member interfaces of the broadcast group.

[0037] Finally, the data acquisition unit 1 generates an accounting report 10 based on the processing results returned by the intelligent analysis engine 4. The accounting report 10 not only integrates multi-source data from energy consumption monitoring equipment, process flow control systems, and supply chain management platforms, but also combines real-time monitoring data and dynamic adjustment mechanisms, thereby providing accurate carbon emission cost accounting basis for enterprises. This process is shown in Figure 6, which presents the overall process from data processing to final accounting report output.

[0038] In the above embodiments, the connection relationship between the data acquisition unit 1 and the heterogeneous data source 2 is realized through a standardized interface, ensuring the compatibility of different data sources. The intelligent analysis engine 4 in the central processing module 3 realizes the function of independent data processing nodes through a virtual interface number, enhancing the flexibility and scalability of the system. The central exchange module 5 realizes precise control of data distribution by analyzing the association weight in the unified identifier 7, supporting data transmission requirements in various complex scenarios. The design of the load balancing group 8 and the broadcast group 9 further improves the resource utilization and data transmission efficiency of the system, meeting the demand for large-scale data processing. Through the above steps, the present application realizes the accurate accounting of carbon emission cost through multi-source data fusion, solving the shortcomings of the prior art in data fusion, dynamic adaptability and cross-industry generality.

Claims

1. A multi-source data fusion carbon emission cost accurate accounting method, characterized in that, Comprising the following steps: The data acquisition unit (1) connects multiple heterogeneous data sources (2) through a standardized interface, including energy consumption monitoring devices, process control systems, and supply chain management platforms; The data acquisition unit (1) adds a uniform identifier (7) to the collected raw data, which contains a timestamp, a source identifier, an association weight, and a data verification code, and sends the data to the central processing module (3) through a preset transmission protocol; The central processing module (3) parses the uniform identifier (7) and extracts the key accounting information, including the timestamp, source identifier, and association weight; After the central processing module (3) completes data processing, it updates the association weight in the uniform identifier (7) and returns the processing results to the data acquisition unit (1); The data output unit generates a final accounting report (10) based on the received processing results.

2. The multi-source data fusion-based carbon emission cost accurate accounting method according to claim 1, characterized in that, Also comprising the following steps: A transmission path is established for each data acquisition unit (1) to all heterogeneous data sources (2) and central processing modules (3), and for each central processing module (3) to all data acquisition units (1) and heterogeneous data sources (2). 3.The multi-source data fusion based carbon emission cost accurate accounting method according to claim 1, characterized in that, The central processing module (3) contains an intelligent analysis engine (4), each of which is assigned a virtual node number and occupies a range of virtual interfaces. The transmission path to the intelligent analysis engine (4) is considered as a transmission path to the virtual interface.

4. The multi-source data fusion-based carbon emission cost accurate accounting method according to claim 3, characterized in that, The transmission rules include: Transmit data to the intelligent analysis engine (4) mounted on the data acquisition unit (1), specifically: according to the mapping relationship between the interface and the source identifier, transmit from the corresponding link (6) of the data acquisition unit (1), and the association weight in the uniform identifier (7) is the virtual interface number occupied by the intelligent analysis engine (4).

5. The multi-source data fusion-based carbon emission cost accurate accounting method according to claim 3, characterized in that, The transmission rules also include: Transmit data to the intelligent analysis engine (4) mounted on the remote data acquisition unit (1), specifically: according to the mapping relationship between the interface and the transmission port, transmit data from link 0 of the data acquisition unit (1) to the central switching module (5), and the association weight in the uniform identifier (7) is the virtual interface number occupied by the intelligent analysis engine (4); After the central switching module (5) receives the data, it parses the virtual interface number in the uniform identifier (7), and according to the mapping relationship between the interface and the transmission port on the central switching module (5), it transmits from the corresponding link (6) to the remote data acquisition unit (1).

6. The multi-source data fusion-based carbon emission cost accurate accounting method according to claim 3, characterized in that, The transmission rules also include: Transmit data to the intelligent analysis engine (4) mounted on multiple data acquisition units (1), where multiple intelligent analysis engines (4) are bundled through virtual interfaces to form a load balancing group (8), achieving load sharing between multiple intelligent analysis engines (4), and the association weight in the uniform identifier (7) is the load balancing group (8).

7. The multi-source data fusion-based carbon emission cost accurate accounting method according to claim 3, characterized in that, The transmission rules also include: The data is transmitted to the intelligent analysis engine (4) mounted on the plurality of data acquisition units (1), and is converted and transmitted to the broadcast group (9). The member interface in the broadcast group (9) is a virtual interface occupied by the plurality of intelligent analysis engines (4). In addition to copying the data of the interface of the data acquisition unit (1), the broadcast group (9) only copies one copy of the data to the remote central switching module (5). 8.The multi-source data fusion based carbon emission cost accurate accounting method according to claim 1, characterized in that, The updating of the uniform identifier (7) includes: The intelligent analysis engine (4) mounted on the data acquisition unit (1) updates the associated weight in the uniform identifier (7) to the virtual interface number occupied by the other intelligent analysis engine (4), and transmits the data to the target intelligent analysis engine (4) through the corresponding link (6) according to the mapping relationship between the interface and the output port. 9.The multi-source data fusion based carbon emission cost accurate accounting method according to claim 1, characterized in that, The updating of the uniform identifier (7) further includes: The intelligent analysis engine (4) mounted on the data acquisition unit (1) updates the associated weight in the uniform identifier (7) to the virtual interface number occupied by the intelligent analysis engine (4) mounted on the remote data acquisition unit (1), and sends the data to the central switching module (5) through the link 0. The central switching module (5) analyzes the uniform identifier (7) and transmits the data to the remote data acquisition unit (1). 10.The multi-source data fusion based carbon emission cost accurate accounting method according to claim 1, characterized in that, The updating of the uniform identifier (7) further includes: The intelligent analysis engine (4) mounted on the data acquisition unit (1) updates the associated weight in the uniform identifier (7) to the load balancing group (8) or the broadcast group (9), and the central switching module (5) performs the member interface selection of the load balancing group (8) or the data replication operation of the broadcast group (9).

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