A forward and reverse closed-loop enterprise carbon emission source deep identification method

Abstract

The present application relates to the technical field of enterprise carbon emission accounting and carbon source identification, in particular to a positive and reverse closed loop enterprise carbon emission source deep identification method. The present application adopts a double mode closed loop mechanism of forward topological derivation and reverse energy and mass balance verification, which can not only identify industry standardization conventional carbon emission sources, but also accurately capture unconventional emission sources caused by process transformation, equipment aging, non-standard operation, as well as implicit carbon emission loss paths such as gaseous carbon emission, wastewater carbon carrying and process heat carbon loss, completely solve the problems of missed judgment and misjudgment of traditional methods, and realize the deep identification of enterprise full process and full type carbon emission sources.

Description

Technical Field

[0001] This invention relates to the field of corporate carbon emission accounting and carbon source identification technology, specifically, to a method for deep identification of corporate carbon emission sources using a closed-loop system. Background Technology

[0002] Accurate carbon emission accounting and comprehensive carbon emission source investigation are the core foundational work for carbon asset management, carbon emission reduction transformation, and carbon verification and filing. Currently, the mainstream carbon emission source identification methods in the industry mostly adopt a single positive identification mode of template matching, experience-based investigation, and fixed-point monitoring. That is, they rely on industry-standard emission source templates and combine them with publicly available production data of enterprises to sort out explicit and conventional carbon emission sources.

[0003] Existing technologies have several inherent drawbacks: First, their identification scope is limited, covering only conventional emission sources under standardized production conditions. They cannot adapt to differentiated production scenarios such as enterprise process modifications, equipment aging, and non-standard employee operations, making it difficult to identify unconventional carbon emission sources arising from these scenarios. Second, their identification depth is insufficient. Existing technologies cannot investigate hidden carbon emission loss paths such as gaseous carbon escape, wastewater carbon carryover, and process heat carbon loss, leading to omissions and underestimations in enterprise carbon emission accounting, resulting in low accounting accuracy. Third, they rely on specialized monitoring equipment. Most accurate identification solutions require real-time carbon emission monitoring equipment and online sensor data support, which involves high equipment investment costs and deployment difficulties, making them unsuitable for small and medium-sized enterprises and old production lines. Fourth, they lack a closed-loop iterative optimization mechanism. Traditional identification methods are one-time static investigations and cannot dynamically update the emission source list according to changes in enterprise production conditions, resulting in poor timeliness and accuracy of emission source identification results.

[0004] In summary, existing corporate carbon emission source identification technologies suffer from technical pain points such as narrow coverage, lack of ability to identify hidden sources, high cost, and inability to dynamically iterate and optimize, making it difficult to meet the current industry needs of enterprises for refined carbon accounting and comprehensive carbon emission reduction. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a method for deep identification of corporate carbon emission sources through a closed-loop system of forward and reverse directions. This method solves the technical problems of traditional carbon emission source identification methods, which cannot identify unconventional and hidden carbon emission sources, rely on specialized monitoring equipment, and cannot be dynamically updated. It enables the identification of various corporate carbon emission sources with full coverage and high precision based solely on static production ledger data, thereby significantly improving the completeness and accuracy of corporate carbon emission accounting.

[0006] To achieve the above objectives, this invention provides a method for deep identification of enterprise carbon emission sources in both forward and reverse closed loops, the specific steps of which are as follows:

[0007] S1. Construct a full-process process topology model for the enterprise and complete the initial derivation of forward emission sources; Based on the target enterprise's production process flow, process division, equipment parameters, bill of materials and energy flow data, build a process topology network that includes process relationships, material conversion paths and energy flow links. According to the industry's carbon emission accounting standards, combined with the material and energy flow nodes of the topology network, forward reasoning identifies the enterprise's conventional carbon emission sources and non-conventional emission sources derived from process variations, equipment aging, and non-standard operations, and generates an initial carbon emission source list;

[0008] S2. Establish process energy and quality balance constraint equations and construct reverse verification benchmarks; for each production process in the process topology model, construct material input-output balance equations and energy input-output balance equations for each process, and set physical allowable residual thresholds in combination with enterprise production condition parameters to form a multi-dimensional process energy and quality balance constraint verification system.

[0009] S3. Residual analysis locates hidden emission sources and completes reverse precision verification; substitute the emission accounting data corresponding to the initial carbon emission source list in step S1 into the energy and mass balance constraint equation of each process, calculate the material balance residual and energy balance residual of each process, screen out abnormal processes whose residual values ​​exceed the corresponding physical allowable residual threshold, and determine that there are unidentified carbon emission loss paths in abnormal processes.

[0010] S4. Automatically identify emission loss type and process location based on residual attributes; extract residual material composition and energy loss characteristics of abnormal processes, match preset emission loss type identification rules, automatically distinguish three types of hidden emission types: gaseous carbon escape, wastewater carbon carryover, and process heat carbon loss. At the same time, combine the process node coordinates and material energy loss links of the topology network to accurately locate the specific process location and loss range of the hidden emission source.

[0011] S5. Closed-loop iterative update of the emission source inventory to achieve deep identification; the hidden unconventional emission source information identified in step S4 is injected back into the process topology model in step S1 to supplement and improve the emission nodes and flow paths of the topology network, iteratively update the complete list of corporate carbon emission sources, and form a deep identification mechanism for carbon emission sources that includes forward deduction to check for omissions, reverse verification to fill gaps, and closed-loop data iteration.

[0012] Preferably, the data sources for constructing the process topology model in step S1 include: production process flow chart, equipment operating parameters, raw and auxiliary material input ledger, product and by-product output data, energy consumption data, process modification records, equipment aging parameters, and daily non-standard operation records.

[0013] Preferably, the specific construction process of the process topology model in step S1 includes: decomposing multi-level process nodes of the entire production process based on the collected raw production data of the enterprise, defining the material input port, material output port, energy input port, and energy loss port of each process node, sorting out the material transfer relationship and energy coupling relationship between adjacent process nodes, and constructing a visualized and quantifiable multi-level process topology network.

[0014] Preferably, the method for generating the initial carbon emission source inventory in step S1 includes: based on the node flow characteristics of the process topology network, identifying fixed conventional emission sources by matching the industry's conventional emission source template, and simultaneously deriving and generating branch unconventional emission sources not covered by the template for material and energy flow transformation caused by process modification, equipment aging, and non-standard operation, and integrating them to form an initial inventory containing conventional emission sources and preliminary unconventional emission sources.

[0015] Preferably, in step S2, the material balance equation for a single process satisfies:

[0016] Total carbon content of input materials = Carbon content of output products + Carbon content of conventional emissions + Carbon content of residual materials + Material balance residual;

[0017] The energy balance equation for a single process satisfies:

[0018] Total energy input for a process = energy effectively utilized in the process + energy lost due to conventional heat loss + energy balance residual.

[0019] Preferably, the calibration method for the physical allowable residual threshold in step S2 includes: combining the fluctuation range of the enterprise's production conditions, equipment operating errors, and metering equipment accuracy parameters, and statistically calibrating the independent material balance residual threshold and energy balance residual threshold for each process through historical big data. The physical allowable residual threshold is the maximum residual value within the normal fluctuation range of the production conditions. Exceeding the threshold indicates abnormal carbon emission loss.

[0020] Preferably, the emission loss type discrimination rules in step S4 specifically include: if the proportion of energy residual in abnormal process is higher than that of material residual and there are characteristics of unorganized energy loss, it is judged as process thermal carbon loss; if the proportion of volatile carbon components in the material residual of abnormal process exceeds the standard and there is no corresponding increase in solid and liquid material output, it is judged as gaseous carbon emission; if the difference between the wastewater output flow rate and carbon content accounting value and the ledger record of abnormal process exceeds the standard and the material residual is concentrated in water-soluble carbon components, it is judged as wastewater carbon carryover.

[0021] Preferably, the closed-loop iterative update process in step S5 includes: embedding the hidden emission sources identified by reverse verification as new emission nodes into the process topology model, correcting the material and energy flow parameters of the corresponding process, and updating the process association relationship of the topology network.

[0022] Preferably, the closed-loop iterative process in step S5 further includes: re-executing the forward emission source derivation based on the updated process topology model, optimizing the initial emission source inventory, synchronously updating the benchmark parameters of the energy-mass balance constraint equation, reducing subsequent residual identification errors, iterating multiple times until all process residuals are within the physically permissible threshold range, and outputting a fully covered enterprise carbon emission source inventory.

[0023] Preferably, the method is based on the enterprise's static production ledger, process parameters and energy and material consumption data to achieve accurate identification and location of unconventional hidden carbon emission sources caused by process changes, equipment aging and non-standard operations.

[0024] Compared with the prior art, the technical solution proposed in this application has the following beneficial effects:

[0025] This invention achieves comprehensive identification of emission sources, overcoming the limitations of traditional technologies. It employs a dual-mode closed-loop mechanism combining forward topology derivation and reverse energy-mass balance verification. This mechanism can not only identify standardized, conventional carbon emission sources within an industry, but also accurately capture unconventional emission sources caused by process modifications, equipment aging, and non-standard operations, as well as hidden carbon emission loss pathways such as gaseous carbon escape, wastewater carbon carryover, and process heat carbon loss. This completely solves the problems of missed and misjudged emissions associated with traditional methods, enabling in-depth identification of all types of carbon emission sources across the entire enterprise process.

[0026] No specialized monitoring equipment is required, reducing application costs. This invention relies entirely on existing static production records, process parameters, and material energy consumption data of enterprises to complete the identification work. There is no need to deploy special carbon emission monitoring equipment or collect real-time dynamic monitoring data, which greatly reduces the hardware investment and operation and maintenance costs of enterprises for carbon emission investigation. It is suitable for large, medium and small production enterprises and has extremely high universality.

[0027] With high identification accuracy, this invention enables precise location and traceability. Based on independent energy and mass balance constraint equations and personalized residual thresholds for each process, it accurately identifies the type of hidden emissions through residual characteristics. At the same time, relying on the process topology network, it locks the specific process location and loss range, solving the shortcomings of traditional methods that can only qualitatively identify emissions but cannot accurately locate them. This provides precise data support for enterprises' precise carbon emission reduction and carbon governance.

[0028] It possesses dynamic closed-loop iteration capabilities, resulting in highly timely results. This invention continuously updates the process topology model and emission source inventory through a closed-loop iteration mechanism involving multiple rounds of forward derivation and reverse verification, while simultaneously optimizing energy and mass balance benchmark parameters. This adapts to dynamic changes in enterprise production conditions, continuously reduces identification errors, and ensures the completeness, accuracy, and timeliness of the carbon emission source inventory. Attached Figure Description

[0029] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0030] Figure 1 This is a schematic diagram of the method of the present invention. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described and discussed below with reference to the accompanying drawings. Obviously, what is described here is only a part of the examples of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0032] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is considered "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is considered "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0034] This implementation provides a method for in-depth identification of enterprise carbon emission sources in both forward and reverse closed loops. The specific steps are as follows:

[0035] S1. Construct a full-process process topology model for the enterprise and complete the initial derivation of forward emission sources; Based on the target enterprise's production process flow, process division, equipment parameters, bill of materials and energy flow data, build a process topology network that includes process relationships, material conversion paths and energy flow links. According to the industry's carbon emission accounting standards, combined with the material and energy flow nodes of the topology network, forward reasoning identifies the enterprise's conventional carbon emission sources and non-conventional emission sources derived from process variations, equipment aging, and non-standard operations, and generates an initial carbon emission source list;

[0036] The data sources for constructing the process topology model include: production process flow chart, equipment operating parameters, raw and auxiliary material input ledgers, product and by-product output data, energy consumption data, process modification records, equipment aging parameters, and daily non-standard operation records.

[0037] The specific construction process of the process topology model includes: breaking down the multi-level process nodes of the entire production process based on the collected raw production data of the enterprise, defining the material input port, material output port, energy input port, and energy loss port of each process node, sorting out the material transfer relationship and energy coupling relationship between adjacent process nodes, and constructing a visual and quantifiable multi-level process topology network.

[0038] The initial carbon emission source inventory is generated by: identifying fixed conventional emission sources based on the node flow characteristics of the process topology network and matching them with industry conventional emission source templates; and deriving unconventional emission sources not covered by the templates for material and energy flow transformation caused by process modification, equipment aging, and non-standard operations. The initial inventory is then integrated to form an initial inventory that includes conventional emission sources and preliminary unconventional emission sources.

[0039] S2. Establish process energy and quality balance constraint equations and construct reverse verification benchmarks; for each production process in the process topology model, construct material input-output balance equations and energy input-output balance equations for each process, and set physical allowable residual thresholds in combination with enterprise production condition parameters to form a multi-dimensional process energy and quality balance constraint verification system.

[0040] The material balance equation for a single process satisfies:

[0041] Total carbon content of input materials = Carbon content of output products + Carbon content of conventional emissions + Carbon content of residual materials + Material balance residual;

[0042] The energy balance equation for a single process satisfies:

[0043] Total energy input for a process = energy effectively utilized in the process + energy lost due to conventional heat loss + energy balance residual.

[0044] The calibration method for the physical allowable residual threshold includes: combining the fluctuation range of the enterprise's production conditions, equipment operating errors, and metering equipment accuracy parameters, and statistically calibrating the independent material balance residual threshold and energy balance residual threshold for each process through historical big data. The physical allowable residual threshold is the maximum residual value within the normal fluctuation range of the production conditions. Exceeding the threshold indicates abnormal carbon emission loss.

[0045] S3. Residual analysis locates hidden emission sources and completes reverse precision verification; substitute the emission accounting data corresponding to the initial carbon emission source list in step S1 into the energy and mass balance constraint equation of each process, calculate the material balance residual and energy balance residual of each process, screen out abnormal processes whose residual values ​​exceed the corresponding physical allowable residual threshold, and determine that there are unidentified carbon emission loss paths in abnormal processes.

[0046] S4. Automatically identify emission loss type and process location based on residual attributes; extract residual material composition and energy loss characteristics of abnormal processes, match preset emission loss type identification rules, automatically distinguish three types of hidden emission types: gaseous carbon escape, wastewater carbon carryover, and process heat carbon loss. At the same time, combine the process node coordinates and material energy loss links of the topology network to accurately locate the specific process location and loss range of the hidden emission source.

[0047] The specific rules for identifying emission loss types include: if the proportion of energy residual in abnormal processes is higher than that of material residual, and there are characteristics of unorganized energy loss, it is determined to be process thermal carbon loss; if the proportion of volatile carbon components in the material residual of abnormal processes exceeds the standard, and there is no corresponding increase in solid and liquid material output, it is determined to be gaseous carbon emission; if the difference between the wastewater output flow rate and carbon content accounting value and the ledger record of abnormal processes exceeds the standard, and the material residual is concentrated in water-soluble carbon components, it is determined to be wastewater carbon carryover.

[0048] S5. Closed-loop iterative update of the emission source inventory to achieve deep identification; the hidden unconventional emission source information identified in step S4 is injected back into the process topology model in step S1 to supplement and improve the emission nodes and flow paths of the topology network, iteratively update the complete list of corporate carbon emission sources, and form a deep identification mechanism for carbon emission sources that includes forward deduction to check for omissions, reverse verification to fill gaps, and closed-loop data iteration.

[0049] The closed-loop iterative update process includes: embedding the hidden emission sources identified by reverse verification as new emission nodes into the process topology model, correcting the material and energy flow parameters of the corresponding process, and updating the process association relationship of the topology network.

[0050] The closed-loop iterative process also includes: re-executing the forward emission source derivation based on the updated process topology model, optimizing the initial emission source inventory, synchronously updating the benchmark parameters of the energy-mass balance constraint equation, reducing subsequent residual identification errors, iterating multiple times until all process residuals are within the physically permissible threshold range, and outputting a full-coverage enterprise carbon emission source inventory.

[0051] This method does not require dedicated carbon emission monitoring equipment or real-time emission monitoring data. It can accurately identify and locate unconventional hidden carbon emission sources caused by process changes, equipment aging, and non-standard operations based solely on the company's static production ledgers, process parameters, and energy and material consumption data.

[0052] Specifically, the implementation process is as follows:

[0053] 1.1 Modeling Data Source: The modeling of this invention relies solely on the enterprise's static ledgers and process parameters, specifically including: production process flow charts, equipment rated operating parameters and real-time operating parameters, raw and auxiliary material input ledgers, intermediate product / product / by-product output ledgers, various energy consumption ledgers, annual process modification records, equipment aging and degradation parameters, and daily non-standard operation records, without requiring any online monitoring equipment or real-time emission data.

[0054] 1.2 Topology Model Construction Method: The entire enterprise production process is hierarchically decomposed, separating independent yet coupled multi-level production process nodes. For each process node, four types of ports are standardized and defined: material input port, material output port, energy input port, and energy loss port. The material transfer paths and energy coupling relationships between adjacent and cross-processes are analyzed to construct a multi-level, visualized process topology network with quantifiable parameters. Each process node, material link, and energy link corresponds to statistically significant and calculable numerical parameters.

[0055] 1.3 Initial Emission Source Inventory Generation Logic: Based on the characteristics of material and energy flow nodes in the topology network, and matching the industry-standard carbon emission source template, standardized conventional visible emission sources such as fuel combustion, material decomposition, and exhaust gas emissions are quickly identified. At the same time, for nodes with changes in material throughput and variations in energy loss caused by process modification, equipment aging, and non-standard manual operations, the fixed template limitation is broken, and various unconventional derivative emission sources are derived and generated. The two types of emission sources are integrated to form an initial carbon emission source inventory, completing the positive preliminary identification.

[0056] The second step involves constructing a set of energy and mass balance equations at the process level, calibrating the physical allowable residual threshold, and establishing a unified reverse verification benchmark. This step is the core foundation for reverse leak detection. By standardizing material and energy balance formulas and combining them with operating condition errors to calibrate a reasonable residual range, it provides a quantitative basis for subsequent identification of hidden emissions.

[0057] 2.1 Core formula for carbon balance of materials in a single process:

[0058] For any independent process in the process topology network, under steady-state production conditions, the total carbon input and output of materials are conserved. Considering normal residues, routine emissions, and statistical errors, the formula is as follows:

[0059]

[0060] In the formula: Input the total carbon content of materials for the process; The solid and liquid carbon content of the product / byproduct of the process; The carbon emissions from routine processes are recorded in the ledger. This represents the residual carbon content of materials during normal processing. Material balance residuals represent unknown carbon content differences that cannot be explained by conventional ledgers.

[0061] 2.2 Core formula for energy balance in a single process:

[0062] For any independent process, the input energy follows the law of energy conservation. Taking into account effective energy utilization, conventional heat loss, and statistical error, the formula is as follows:

[0063]

[0064] In the formula: Input total energy into the process; To ensure efficient energy utilization in production processes; This refers to the energy lost through routine heat during the process. The energy balance residual represents the difference in energy loss due to unknown abnormalities.

[0065] 2.3 Physically Tolerant Residual Threshold Calibration Method:

[0066] Two sets of thresholds are independently calibrated for each process: material residual tolerance threshold. Energy residual tolerance threshold .

[0067] Calibration basis: Enterprise historical production big data, fluctuation range of normal working conditions, inherent operating error of equipment, and measurement accuracy error of measuring instruments.

[0068] Threshold definition: The maximum reasonable value of process statistical residuals under normal production conditions.

[0069] Judgment Criteria: When and At that time, there was no abnormal carbon emission loss in the process; when or At that time, it was determined that there was abnormal carbon loss in the process and that there were unidentified hidden emission sources.

[0070] The third step involves residual quantification calculation and abnormal process screening to achieve reverse and precise verification of hidden emission sources. All accounting data and material energy consumption ledger data corresponding to the initial emission source list in step S1 are substituted into the energy-mass balance equation set established in step two, process by process, to calculate the emissions for each process in batches. and The calculated residuals are compared one by one with the allowable thresholds for the corresponding processes to identify abnormal processes with excessive residuals. Such excessive residuals cannot be explained by equipment errors, operating condition fluctuations, or statistical errors, proving that there are hidden carbon emission loss paths in the processes that have not been positively identified, thus completing the reverse leak detection and location.

[0071] The fourth step involves establishing a formula and method for identifying and locating latent emission types based on residual components and characteristics. This invention uniformly classifies all latent carbon emissions losses by enterprises into three categories: process thermal carbon loss, gaseous carbon escape, and wastewater carbon carryover. Quantitative discrimination rules are established based on residual proportions, component characteristics, and material output differences. The specific judgment logic is as follows:

[0072] 4.1 Rules for determining process heat carbon loss:

[0073] Satisfies the condition that the proportion of energy residual is much greater than the proportion of material residual, i.e. Furthermore, the process exhibits energy loss characteristics such as unorganized heat dissipation, high-temperature loss on equipment surfaces, and unorganized emission of flue gas, leading to the determination that the abnormality of this process is due to process heat carbon loss.

[0074] 4.2 Rules for determining the escape of gaseous carbon:

[0075] Satisfy: Material balance residual The process exceeded the limits, and the proportion of volatile organic carbon and gaseous carbon-containing components in the residual components exceeded the standard. At the same time, there was no corresponding increase in carbon content in the solid and liquid products and by-products of the process. The carbon content loss could not be explained by the solid and liquid material residues. Therefore, the abnormality of this process was determined to be the emission of gaseous carbon.

[0076] 4.3 Rules for determining carbon carryover in wastewater:

[0077] The following conditions must be met: the actual output flow rate of the process wastewater, the calculated value of the carbon content in the wastewater, and the deviation from the ledger records exceed the limits, and the material residuals are acceptable. The carbon was concentrated in water-soluble components, with no obvious gaseous loss characteristics, indicating that the abnormality of this process was due to carbon carryover in the wastewater.

[0078] 4.4 Precise Positioning Method: After identifying the emission type, based on the process node coordinates and material energy loss links of the process topology network, the starting node, transmission range, and ending node of carbon loss are traced in reverse to accurately pinpoint the specific process location and loss range of the hidden emission source.

[0079] The fifth step involves a multi-round closed-loop iterative update mechanism to achieve in-depth and comprehensive identification of carbon emission sources. This step continuously eliminates residual anomalies through a closed-loop logic of topology model updates, parameter corrections, and iterative verification, ultimately outputting a complete list of carbon emission sources without omissions.

[0080] 5.1 Topology Model Update: The hidden unconventional emission sources identified in step 4, such as thermal carbon loss, gaseous carbon escape, and wastewater carbon carryover, are embedded as new emission nodes into the original process topology network. New carbon loss links are added, the material carbon flow parameters and energy loss parameters of the corresponding processes are corrected, and the coupling relationship between processes is updated.

[0081] 5.2 Iteration of Inventory and Benchmark Parameters: Based on the updated topology model, the S1 forward emission source derivation logic is re-executed to iteratively optimize the emission source inventory; the benchmark input parameters of the energy-mass balance equations of each process are simultaneously corrected to adapt to the updated production flow characteristics and reduce the system error of subsequent residual identification.

[0082] 5.3 Termination condition for multiple iterations: The entire process of "forward derivation - energy-mass balance calculation - residual verification - hidden source identification - model update" is executed cyclically until the enterprise... When all processes meet the requirements and all residuals are within the physically permissible threshold range, the iteration terminates, and the final output is a complete list of enterprise carbon emission sources that is fully covered, without omissions, and highly accurate.

[0083] This invention eliminates the need for dedicated carbon emission monitoring equipment and real-time emission monitoring data. It relies solely on static production records, process parameters, and material and energy consumption data from enterprises. Through standardized energy-mass balance formulas and quantified residual discrimination rules, it can reliably identify unconventional, hidden carbon emission sources caused by process changes, equipment aging, and non-standard operations that are not covered by traditional methods. It boasts high accuracy, broad applicability, and good iterative capabilities, making it suitable for in-depth carbon emission source identification scenarios in various manufacturing enterprises.

[0084] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, nor to combinations thereof. Those skilled in the art can make various changes, modifications, or combinations within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for deep identification of enterprise carbon emission sources in a positive and negative closed-loop manner, characterized in that, The specific steps are as follows: S1. Construct a full-process process topology model for the enterprise and complete the initial derivation of forward emission sources; Based on the target enterprise's production process flow, process division, equipment parameters, bill of materials and energy flow data, build a process topology network that includes process relationships, material conversion paths and energy flow links. According to the industry's carbon emission accounting standards, combined with the material and energy flow nodes of the topology network, forward reasoning identifies the enterprise's conventional carbon emission sources and non-conventional emission sources derived from process variations, equipment aging, and non-standard operations, and generates an initial carbon emission source list; S2. Establish process energy and quality balance constraint equations and construct reverse verification benchmarks; for each production process in the process topology model, construct material input-output balance equations and energy input-output balance equations for each process, and set physical allowable residual thresholds in combination with enterprise production condition parameters to form a multi-dimensional process energy and quality balance constraint verification system. S3. Residual analysis locates hidden emission sources and completes reverse precision verification; substitute the emission accounting data corresponding to the initial carbon emission source list in step S1 into the energy and mass balance constraint equation of each process, calculate the material balance residual and energy balance residual of each process, screen out abnormal processes whose residual values ​​exceed the corresponding physical allowable residual threshold, and determine that there are unidentified carbon emission loss paths in abnormal processes. S4. Automatically identify emission loss type and process location based on residual attributes; extract residual material composition and energy loss characteristics of abnormal processes, match preset emission loss type identification rules, automatically distinguish three types of hidden emission types: gaseous carbon escape, wastewater carbon carryover, and process heat carbon loss. At the same time, combine the process node coordinates and material energy loss links of the topology network to accurately locate the specific process location and loss range of the hidden emission source. S5. Closed-loop iterative update of the emission source inventory to achieve deep identification; the hidden unconventional emission source information identified in step S4 is injected back into the process topology model in step S1 to supplement and improve the emission nodes and flow paths of the topology network, iteratively update the complete list of corporate carbon emission sources, and form a deep identification mechanism for carbon emission sources that includes forward deduction to check for omissions, reverse verification to fill gaps, and closed-loop data iteration.

2. The method for deep identification of enterprise carbon emission sources in a positive and negative closed loop according to claim 1, characterized in that, The data sources for constructing the process topology model in step S1 include: production process flow chart, equipment operating parameters, raw and auxiliary material input ledger, product and by-product output data, energy consumption data, process modification records, equipment aging parameters, and daily non-standard operation records.

3. The method for deep identification of enterprise carbon emission sources in a positive and negative closed loop according to claim 2, characterized in that, The specific construction process of the process topology model in step S1 includes: breaking down the multi-level process nodes of the entire production process based on the collected raw production data of the enterprise, defining the material input port, material output port, energy input port, and energy loss port of each process node, sorting out the material transfer relationship and energy coupling relationship between adjacent process nodes, and constructing a visualized and quantifiable multi-level process topology network.

4. The method for deep identification of enterprise carbon emission sources in a positive and negative closed loop according to claim 3, characterized in that, The initial carbon emission source inventory in step S1 is generated by: identifying fixed conventional emission sources based on the node flow characteristics of the process topology network and matching them with the industry's conventional emission source template; and deriving unconventional emission sources not covered by the template for material and energy flow transformation caused by process modification, equipment aging, and non-standard operation. These are then integrated to form an initial inventory that includes conventional emission sources and preliminary unconventional emission sources.

5. The method for deep identification of enterprise carbon emission sources in a forward and reverse closed loop according to claim 1, characterized in that, In step S2, the material balance equation for a single process satisfies: Total carbon content of input materials = Carbon content of output products + Carbon content of conventional emissions + Carbon content of residual materials + Material balance residual; The energy balance equation for a single process satisfies: Total energy input for a process = energy effectively utilized in the process + energy lost due to conventional heat loss + energy balance residual.

6. The method for deep identification of enterprise carbon emission sources in a positive and negative closed loop according to claim 5, characterized in that, The calibration method for the physical allowable residual threshold in step S2 includes: combining the fluctuation range of the enterprise's production conditions, equipment operating errors, and metering equipment accuracy parameters, and statistically calibrating the independent material balance residual threshold and energy balance residual threshold for each process through historical big data. The physical allowable residual threshold is the maximum residual value within the normal fluctuation range of the production conditions. Exceeding the threshold indicates abnormal carbon emission loss.

7. The method for deep identification of enterprise carbon emission sources in a positive and negative closed loop according to claim 1, characterized in that, The specific rules for determining the emission loss type in step S4 include: if the proportion of energy residual in the abnormal process is higher than that of material residual, and there are characteristics of unorganized energy loss, it is determined to be process thermal carbon loss; if the proportion of volatile carbon components in the material residual of the abnormal process exceeds the standard, and there is no corresponding increase in solid and liquid material output, it is determined to be gaseous carbon emission; if the difference between the wastewater output flow rate and carbon content accounting value and the ledger record of the abnormal process exceeds the standard, and the material residual is concentrated in water-soluble carbon components, it is determined to be wastewater carbon carryover.

8. The method for deep identification of enterprise carbon emission sources in a forward and reverse closed loop according to claim 1, characterized in that, The closed-loop iterative update process in step S5 includes: embedding the hidden emission sources identified by reverse verification as new emission nodes into the process topology model, correcting the material and energy flow parameters of the corresponding process, and updating the process association relationship of the topology network.

9. The method for deep identification of enterprise carbon emission sources in a positive and negative closed loop according to claim 8, characterized in that, The closed-loop iterative process in step S5 also includes: re-executing the forward emission source derivation based on the updated process topology model, optimizing the initial emission source inventory, synchronously updating the baseline parameters of the energy-mass balance constraint equation, reducing subsequent residual identification errors, iterating multiple times until all process residuals are within the physically permissible threshold range, and outputting a fully covered enterprise carbon emission source inventory.

10. The method for deep identification of enterprise carbon emission sources in a forward and reverse closed loop according to claim 1, characterized in that, The method, based on the company's static production ledger, process parameters, and energy and material consumption data, enables the accurate identification and location of unconventional, hidden carbon emission sources caused by process changes, equipment aging, and non-standard operations.

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