Green household appliance whole life cycle environmental impact assessment method based on carbon footprint tracking

Abstract

The application discloses a green household appliance whole life cycle environmental impact evaluation method based on carbon footprint tracking, relates to the technical field of life cycle assessment, and comprises the following steps: assigning a unique digital identity to the green household appliance, binding product bill of materials, production process energy consumption and transportation information, and generating an initial life cycle inventory; based on the initial life cycle inventory, importing carbon emission data of raw material acquisition, component manufacturing and transportation stages, and forming a complete life cycle inventory after standardization processing; based on the complete life cycle inventory, continuously collecting actual running power curve and running time period information of the green household appliance according to time slices, obtaining marginal carbon emission factor data corresponding to each time slice, and pairing to generate time sequence carbon flow data set. The application realizes quantitative characterization of the whole life cycle carbon flow distribution characteristics, generates whole life cycle environmental impact evaluation results, and improves the time and space accuracy and reliability of the evaluation results.

Description

Technical Field

[0001] This invention relates to the field of life cycle assessment technology, and in particular to a method for assessing the environmental impact of green home appliances throughout their entire life cycle based on carbon footprint tracking. Background Technology

[0002] The home appliance industry is undergoing a critical transformation from simple energy efficiency optimization to low-carbon development throughout its entire lifecycle. Standard-based life cycle assessment is a common technique for evaluating the environmental impact of products. It establishes a static life cycle inventory by accessing industry average databases and performs a product calculation using the regional power grid's annual average carbon emission factor and the estimated rated power. This method has a mature data structure and is widely used in macro-level product benchmarking analysis.

[0003] There are limitations in characterizing the dynamic operation of green home appliances. Existing technologies often ignore the temporal fluctuations of electricity production and simplify carbon emissions during the usage phase into a static total value without a time dimension. This results in the evaluation results failing to reflect the marginal benefits of operation during low-emission periods and the actual carbon footprint bias caused by differences in user behavior. The coarsening of the time scale causes lag and distortion between the evaluation model and the actual environmental load, making it difficult to meet the needs of high-precision carbon footprint tracking. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for environmental impact assessment of green home appliances throughout their entire life cycle based on carbon footprint tracking. This solves the problem that existing technologies simplify carbon emissions during the usage phase to a static total value by ignoring the temporal fluctuations of electricity production, resulting in an inability to reflect the marginal benefits during low-emission periods and the distortion caused by differences in user behavior.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for environmental impact assessment of the entire life cycle of green home appliances based on carbon footprint tracking, which includes assigning a unique digital identity to green home appliances and binding product bill of materials, production process energy consumption and transportation information to generate an initial life cycle list; Based on the initial life cycle inventory, carbon emission data from the raw material acquisition, component manufacturing, and transportation stages are imported and standardized to form a complete life cycle inventory. Based on a complete lifecycle inventory, the actual operating power curves and operating time information of green home appliances are continuously collected according to time slices. Marginal carbon emission factor data corresponding to each time slice are obtained and paired to generate time-series carbon flow datasets. Based on a time-series carbon flow dataset, the energy consumption value of each time slice is associated with the corresponding marginal carbon emission factor, and injected into the usage stage unit processes in the complete life cycle inventory in chronological order. This reconstructs the intermediate life cycle inventory and obtains the full life cycle carbon flow density index. The full life cycle environmental impact assessment results are obtained by using the full life cycle carbon flow density index.

[0007] As a preferred embodiment of the carbon footprint tracking-based environmental impact assessment method for the entire life cycle of green home appliances described in this invention, the method for assigning unique digital identifiers to green home appliances includes: During the production and off-line stage of green home appliances, a unique digital identity is burned into the control chip of the green home appliance, a digital identity writing completion signal is generated, the product bill of materials in the product database is called, an index relationship is established between the product bill of materials and the digital identity, and a digital identity mapping table bound to the product bill of materials is generated.

[0008] As a preferred embodiment of the green home appliance life-cycle environmental impact assessment method based on carbon footprint tracking described in this invention, the initial life-cycle inventory includes: Based on the digital identity mapping table of the bound product bill of materials, production process energy consumption and transportation information are extracted from the manufacturing execution unit. The production process energy consumption and transportation information are associated with the corresponding fields in the digital identity mapping table of the bound product bill of materials to generate a bound data package. By using pre-defined life cycle assessment data parsing rules, the hierarchical relationship and physical quantity units of each data item in the bound data package are parsed. The parsed data is encapsulated and arranged so that each row represents an input or output substance and each column represents a process flow label, including the flow direction, substance name, physical quantity unit, and data source, to generate an initial life cycle list.

[0009] As a preferred embodiment of the green home appliance life-cycle environmental impact assessment method based on carbon footprint tracking described in this invention, the complete life-cycle inventory includes: Based on the initial lifecycle inventory, the carbon emission database and process energy consumption records of the raw material acquisition stage, component manufacturing stage and transportation stage are called up to extract carbon emission data that matches the product bill of materials, production process energy consumption and transportation information, and generate carbon emission dataset. Based on the carbon emission dataset, and in accordance with the physical quantity unit conversion rules specified in the life cycle assessment standard, the energy consumption units from different sources are unified into standard energy units to generate a standardized carbon emission dataset. The values ​​from the standardized carbon emission dataset are filled into the corresponding positions in the initial life cycle inventory structure according to the unit process format specified in the life cycle assessment standard to generate a complete life cycle inventory.

[0010] As a preferred embodiment of the green home appliance life-cycle environmental impact assessment method based on carbon footprint tracking described in this invention, the time-series carbon flow dataset includes: Based on a complete lifecycle inventory, periodic wake-up instructions and zero-crossing synchronization interrupt service routines are hard-coded into the firmware of the main control chip for green home appliances. The periodic wake-up command activates the current sampling channel according to a preset time slice, monitors the mains voltage zero-crossing signal and the load enable status bit, and when it is determined that there is a complete mains cycle within the time slice and the load enable status bit is high, the instantaneous power value sampled within the corresponding time slice is taken as the effective operating power. The start and end timestamps of the time slice are attached. If the conditions are not met within the time slice, the time slice is marked as invalid and the next adjacent time slice is used for supplementary sampling. The valid time slice operating power values ​​after validity judgment are arranged in time order to form an operating power curve. The operating power curve and the corresponding operating time period information of each time slice are encapsulated together to generate the original energy consumption record of the operating power curve and operating time period information. Based on the original energy consumption records of the operating power curve and operating time period information, a timestamped query request is sent to the regional power grid dispatch center to obtain the marginal carbon emission factor data corresponding to each time slice in the operating time period information, and generate a power grid response record of the marginal carbon emission factor data. Based on the grid response record of marginal carbon emission factor data, the energy consumption value of each time slice in the operating power curve and operating time period information is paired with the marginal carbon emission factor data of the corresponding time slice in the grid response record to generate a time-series carbon flow dataset.

[0011] As a preferred embodiment of the green home appliance life-cycle environmental impact assessment method based on carbon footprint tracking described in this invention, the intermediate life-cycle inventory includes: Based on the time-series carbon flow dataset, the dataset is unpacked in ascending order of time using the starting timestamp of the sampling interval as the index key. The cumulative energy consumption value and marginal carbon emission factor are separated, and time attribution tuples are constructed and pushed into the execution queue in sequence. Based on the execution queue, time attribution tuples are popped out in the queue order. The cumulative energy consumption value and marginal carbon emission factor in the tuple are subjected to the same group attribution operation to obtain the carbon flow contribution of the sampling interval marked with the start time stamp. They are arranged in order to form a time index carbon flow sequence. Based on the time-indexed carbon flow sequence, the usage phase unit process node in the complete life cycle list is located. The carbon flow contribution of each sampling interval is attached as an independent sub-entry to the usage phase unit process node according to the time-indexed carbon flow sequence. After verification by timestamp and unit system, the carbon flow value injected into the usage phase unit process is reconstructed into a dynamic set form. Based on the usage phase unit processes with injected carbon flow values, extract the existing raw material acquisition phase unit processes, component manufacturing phase unit processes, and transportation phase unit processes from the complete life cycle inventory, physically splice and align them hierarchically according to the life cycle phase sequence, and reorganize them to generate an intermediate life cycle inventory.

[0012] As a preferred embodiment of the green home appliance life-cycle environmental impact assessment method based on carbon footprint tracking described in this invention, wherein: the construction of the life-cycle carbon flux density index includes, Based on the intermediate life cycle inventory, the carbon flow values ​​marked in each unit process are extracted and connected in sequence according to the life cycle stage to form a continuous carbon flow trajectory. Based on continuous carbon flow trajectories, the carbon flow density distribution characteristics corresponding to unit function output are identified. The carbon flow density distribution characteristics are mapped to a two-dimensional coordinate system of time axis and function output axis to generate a carbon flow density spatial distribution map. The centroid coordinates of the carbon flow density distribution are selected as the whole life cycle carbon flow density index.

[0013] As a preferred embodiment of the green home appliance life cycle environmental impact assessment method based on carbon footprint tracking described in this invention, the life cycle environmental impact assessment results include:

[0014] Based on the life cycle carbon flux density index, a preset evaluation threshold range mapping table is invoked to map the life cycle carbon flux density index to the corresponding environmental impact level label. Based on the environmental impact level label and combined with the numerical characteristics of the life cycle carbon flow density index, the life cycle environmental impact assessment results are generated.

[0015] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in the first aspect of the present invention.

[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: By assigning a unique digital identity to green home appliances and binding it to the product's bill of materials, production process energy consumption, and transportation information, unique traceability of source data is achieved, providing a foundation for full lifecycle data association; by importing and standardizing multi-stage carbon emission data to form a complete lifecycle inventory, the heterogeneity of cross-stage data is eliminated; by collecting actual operating power in time slices and pairing it with corresponding marginal carbon emission factors to generate a time-series carbon flow dataset, the limitations of static evaluation methods in terms of time resolution are overcome, realizing a dynamic causal relationship between energy consumption and grid carbon emission intensity; by injecting carbon flow data into the unit processes of the usage stage in chronological order and reconstructing the intermediate lifecycle inventory, a structured upgrade from static total value to dynamic time series is achieved in the usage stage; by connecting carbon flow values ​​of each stage to generate a continuous carbon flow trajectory and mapping it to a carbon flow density spatial distribution map, and selecting the centroid coordinates as the full lifecycle carbon flow density index, a quantitative characterization of the full lifecycle carbon flow distribution characteristics is achieved, generating full lifecycle environmental impact assessment results and improving the spatiotemporal accuracy and reliability of the assessment results. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for environmental impact assessment of green home appliances throughout their entire life cycle based on carbon footprint tracking. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figure 1This is one embodiment of the present invention, which provides a method for environmental impact assessment of green home appliances throughout their entire life cycle based on carbon footprint tracking, including the following steps: S1. Assign a unique digital identity to green home appliances and bind the product bill of materials, production process energy consumption and transportation information to generate an initial life cycle list.

[0024] S1.1 During the green home appliance production line stage, a unique digital identity is burned into the control chip of the green home appliance, a digital identity writing completion signal is generated, the product bill of materials in the product database is called, an index relationship is established between the product bill of materials and the digital identity, and a digital identity mapping table bound to the product bill of materials is generated.

[0025] Furthermore, after a green home appliance completes production and rolls off the assembly line, a unique digital identity is written into the appliance's control chip via a programming process. Once written, the control chip sends a signal confirming the completion of the digital identity writing. The production end then verifies that the digital identity has established a fixed correspondence with the green home appliance entity. It then retrieves the product bill of materials (BOM) from the product database and establishes an index relationship between the component names, material attributes, specifications, supply sources, and assembly affiliations recorded in the BOM and the digital identity. This creates a corresponding data association between the green home appliance entity, the control chip's identity information, and the BOM. After completing the index relationship, a digital identity mapping table bound to the BOM is formed.

[0026] S1.2 Based on the digital identity mapping table of the bound product bill of materials, extract the production process energy consumption and transportation information from the manufacturing execution unit, associate the production process energy consumption and transportation information with the corresponding fields in the digital identity mapping table of the bound product bill of materials, and generate a bound data package.

[0027] Furthermore, production process energy consumption is associated with corresponding components or assembly stages in the product's bill of materials (BOM) according to the process flow, and transportation information is matched with corresponding fields in the BOM according to the material flow path, transportation stage, and delivery object. During the matching process, digital identity identifiers are used as the basis for data attribution, and production process energy consumption and transportation information are written into the corresponding fields in the digital identity identifier mapping table bound to the BOM, so that material composition, processing, and transportation processes are associated within the same data structure. After the association is completed, a bound data package is generated.

[0028] S1.3. Using the pre-set life cycle assessment data parsing rules, the hierarchical relationship and physical quantity units of each data item in the bound data package are parsed. The parsed data is encapsulated and arranged so that each row represents an input or output substance and each column represents a process flow label, including flow direction, substance name, physical quantity unit and data source, to generate an initial life cycle list.

[0029] Furthermore, the pre-defined life cycle assessment data parsing rules are established by pre-defining the hierarchical relationships, physical quantity units, and mapping logic of various data fields within the life cycle assessment process. These parsing rules include input / output substance classification, process flow identification, flow direction relationships, standardized substance names, unified physical quantity units, and data source labeling. They exist in the form of configuration tables or structured templates and are automatically parsed and encapsulated according to these rules when the initial life cycle inventory is generated. This ensures that the data is arranged so that each row represents one type of input / output substance and each column represents one process flow, guaranteeing that the parsed inventory meets the life cycle assessment calculation requirements and forms the initial life cycle inventory.

[0030] S2. Based on the initial life cycle inventory, import carbon emission data from the raw material acquisition, component manufacturing and transportation stages, and form a complete life cycle inventory after standardization.

[0031] S2.1 Based on the initial lifecycle inventory, call the carbon emission database and process energy consumption records of the raw material acquisition stage, component manufacturing stage and transportation stage, extract carbon emission data that matches the product bill of materials, production process energy consumption and transportation information, and generate a carbon emission dataset.

[0032] Furthermore, based on the initial lifecycle inventory structure, the material categories, component composition relationships, and process flow information in the product bill of materials are located. Then, according to the process fields corresponding to production process energy consumption and transportation information, corresponding carbon emission data are extracted from the carbon emission databases and process energy consumption records of the raw material acquisition stage, component manufacturing stage, and transportation stage. During the extraction process, carbon emission data at different stages are matched according to the material names, process flow, transportation routes, and energy consumption attribution relationships in the product bill of materials, ensuring that carbon emissions at the raw material acquisition stage, component manufacturing stage, and transportation stage can be mapped to their respective positions in the initial lifecycle inventory structure. After matching is completed, the carbon emission data at each stage are aggregated according to the lifecycle stage sequence to form a carbon emission dataset.

[0033] S2.2 Based on the carbon emission dataset, and in accordance with the physical quantity unit conversion rules specified in the life cycle assessment standard, the energy consumption units from different sources are unified into standard energy units to generate a standardized carbon emission dataset.

[0034] Furthermore, based on the carbon emission dataset, a unified conversion process is performed on energy consumption units and carbon emission measurement units from different sources. During this process, the units of electrical energy, thermal energy, and transportation energy consumption in the process energy consumption records are uniformly converted according to the physical quantity unit conversion rules stipulated in the life cycle assessment standard. The material measurement forms in carbon emission databases from different sources are also standardized, ensuring that energy consumption and carbon emissions at different life cycle stages can be expressed under a unified dimension. After the unit conversion is completed, the conversion results are rewritten into the corresponding fields of the carbon emission dataset to generate a standardized carbon emission dataset.

[0035] S2.3. Fill the values ​​in the standardized carbon emission dataset into the corresponding positions in the initial life cycle inventory structure according to the unit process format specified in the life cycle assessment standard to generate a complete life cycle inventory.

[0036] Furthermore, based on the correspondence between life cycle stages, carbon emission data from the raw material acquisition stage is entered into the corresponding raw material input unit process, carbon emission data from the component manufacturing stage is entered into the corresponding manufacturing process unit process, and carbon emission data from the transportation stage is entered into the corresponding logistics and transportation unit process. The flow direction, substance name, physical quantity unit, and data source in each unit process are arranged accordingly, ensuring that the standardized carbon emission data forms a unified life cycle expression form with the initial life cycle inventory structure. After completion, a complete life cycle inventory is generated.

[0037] S3. Based on a complete lifecycle inventory, continuously collect the actual operating power curves and operating time periods of green home appliances according to time slices, obtain marginal carbon emission factor data corresponding to each time slice, and generate time-series carbon flow datasets by pairing them.

[0038] S3.1 Based on the complete lifecycle list, periodic wake-up instructions and zero-crossing synchronization interrupt service routines are hard-coded into the firmware of the main control chip for green home appliances.

[0039] Furthermore, a time slice refers to a continuous operating time unit divided according to a preset sampling period. Each time slice has a clearly defined start and end time, used to carry out the power acquisition, validity determination, and subsequent marginal carbon emission factor matching within the corresponding time range. Periodic wake-up instructions are used to ensure that the green appliance main control chip firmware enters the sampling preparation state when each time slice arrives. The zero-crossing synchronization interrupt service routine is used to capture the mains voltage zero-crossing signal, ensuring that the power sampling process remains synchronized with the mains cycle. Furthermore, the periodic wake-up command and the zero-crossing synchronization interrupt service routine jointly define the sampling trigger timing, making the time slice no longer just a normal time division, but a carbon footprint collection unit associated with the mains power cycle, load operating status, and lifecycle inventory usage stage. By moving the energy consumption collection during the usage stage to the firmware level of the green appliance main control chip, the operating power collection has a time boundary and an electrical synchronization basis. For example, compressor loads, heating loads, and motor loads experience power fluctuations during startup and shutdown; zero-crossing synchronization can reduce sampling deviations caused by incomplete mains power cycles. Extending to intermittent operation, periodic operation, and variable power operation scenarios, the time slice can maintain the consistency and traceability of operating power collection.

[0040] S3.2 The periodic wake-up command activates the current sampling channel according to a preset time slice, monitors the mains voltage zero-crossing signal and the load enable status bit, and when it is determined that there is a complete mains cycle within the time slice and the load enable status bit is high, the instantaneous power value sampled within the corresponding time slice is taken as the effective operating power.

[0041] Furthermore, the system continuously monitors the mains voltage zero-crossing signal and the load enable status bit within the time slice. The mains voltage zero-crossing signal is used to determine whether a complete mains power cycle is covered within the time slice, while the load enable status bit is used to determine whether the green appliance is actually in operation within the time slice. When a complete mains power cycle is covered within the time slice and the load enable status bit is high, the green appliance's main control chip firmware uses the instantaneous power value sampled within the corresponding time slice as the effective operating power.

[0042] Specifically, effective operating power is not solely determined by current sampling results, but rather by a complete mains cycle and the load enable status bit, ensuring both electrical integrity and operational accuracy in the sampling results. Binding power sampling conditions to the actual load state of green appliances prevents standby, transient disturbances, or incomplete mains cycles from being misjudged as energy consumption during use. For example, a green appliance may have a weak current when in standby mode; the load enable status bit can eliminate interference from non-operational energy consumption on the effective operating power. Extending this to scenarios involving frequent start-stop, power regulation, and load switching, effective operating power more accurately reflects the actual operation of green appliances.

[0043] S3.3, and attach the start and end timestamps of the time slice. If the conditions are not met within the time slice, mark the time slice as invalid and trigger the next adjacent time slice to supplement the sampling.

[0044] Furthermore, after confirming the valid operating power, a start timestamp and an end timestamp are added to the corresponding time slice, so that each segment of valid operating power has a clear time boundary. If the complete mains power cycle condition is not met or the load enable state bit does not reach the high level condition within the time slice, the time slice is marked as invalid, and the next adjacent time slice is used for supplementary sampling. The invalid mark is used to exclude sampling segments that are not meaningful for evaluation, and the supplementary sampling of the next adjacent time slice is used to ensure that the continuous acquisition process of the operating power curve is not interrupted by a single invalid time slice, thereby forming a valid time slice operating power value with time boundaries.

[0045] Specifically, the start and end timestamps transform effective operating power from a single power value into operating segments with time attribution, providing a foundation for accurate matching of subsequent marginal carbon emission factor data. Invalidity markers and supplementary sampling in the next adjacent time slice together form a quality control mechanism, enabling the sampling process to identify and eliminate abnormal segments while maintaining the continuity of the time series. For example, when there are grid disturbances, brief load disconnections, or the sampling window does not cover the complete grid cycle, and when this extends to scenarios involving grid fluctuations, frequent appliance start-ups and shutdowns, and unstable operating conditions, supplementary sampling in the next adjacent time slice can enhance the integrity of the operating power curve.

[0046] S3.4 Arrange the valid time slice operating power values ​​after validity judgment in chronological order to form an operating power curve. Encapsulate the operating power curve and the corresponding operating time period information of each time slice together to generate the original energy consumption record of the operating power curve and operating time period information.

[0047] Furthermore, the operating power curve is used to represent the actual power change process of green home appliances within a continuous time slice, and the operating time period information is used to record the time boundary corresponding to each time slice. Subsequently, the operating power curve and the operating time period information corresponding to each time slice are encapsulated together, so that the operating power value of each set of valid time slices can correspond to a specific operating time period, generating the original energy consumption record of the operating power curve and operating time period information.

[0048] Specifically, the operating power curve and operating time period information are encapsulated synchronously, so that energy consumption during the usage phase is no longer expressed as a cumulative amount, but rather as a power trajectory expression with a time sequence. This preserves the temporal variation characteristics of green appliances during actual operation, providing a time dimension for matching marginal carbon emission factor data. For example, when the same green appliance consumes the same amount of energy at different operating time periods, the marginal carbon emission factor data of the regional power grid may differ; the operating time period information can distinguish the carbon emission attribution within different time ranges. Extending to peak-valley electricity consumption, intermittent operation, and long-cycle operation scenarios, the original energy consumption records of the operating power curve and operating time period information can provide a continuous basis for generating time-series carbon flow datasets.

[0049] S3.5 Based on the original energy consumption records of the operating power curve and operating time period information, a timestamped query request is initiated to the regional power grid dispatch center to obtain the marginal carbon emission factor data corresponding to each time slice in the operating time period information, and generate a power grid response record of the marginal carbon emission factor data.

[0050] Furthermore, based on the raw energy consumption records of the operating power curve and operating time period information, a timestamped query request is initiated to the regional power grid dispatch center according to the start and end timestamps of each time slice. The regional power grid dispatch center returns the marginal carbon emission factor data corresponding to each time slice in the operating time period information based on the query request. The returned marginal carbon emission factor data is recorded in time slice order, and the corresponding timestamp relationship is retained to generate a power grid response record for the marginal carbon emission factor data.

[0051] Specifically, timestamped query requests enable a correlation between the energy consumption of green appliances during their usage phase and the marginal carbon emission factor data of the regional power grid in the actual time dimension, avoiding the smoothing out of time differences caused by using static average emission factors. The operating time period information of green appliances is time-aligned with the marginal carbon emission factor data provided by the regional power grid dispatch center. For example, when green appliances operate during periods of high renewable energy output, the corresponding marginal carbon emission factor data differs from those during periods of high fossil fuel output. Extending this to scenarios of fluctuations in the regional power grid's energy structure, changes in peak load, and cross-time period operation, the grid response records can reflect the differences in carbon emission attributes across different time slices.

[0052] S3.6. Based on the power grid response record of marginal carbon emission factor data, pair the energy consumption value of each time slice in the operating power curve and operating time period information with the marginal carbon emission factor data of the corresponding time slice in the power grid response record to generate a time-series carbon flow dataset.

[0053] Furthermore, based on the grid response records using marginal carbon emission factor data, and according to the time-slice correspondence, the energy consumption values ​​of each time slice in the operating power curve and operating time period information are paired with the marginal carbon emission factor data of the corresponding time slice in the grid response records. During the pairing process, the start and end timestamps of the time slice are used as the matching criteria to establish a correspondence between the energy consumption values ​​of the time slice and the marginal carbon emission factor data within the same operating time range. After the pairing is completed, each set of time-slice energy consumption values ​​and marginal carbon emission factor data is arranged in chronological order to generate a time-series carbon flow dataset.

[0054] Specifically, time-slice energy consumption values ​​are paired with the corresponding marginal carbon emission factor data for each time slice, transforming the static accounting of carbon emissions during the use of green home appliances into a time-series attribution expression. Using time slices as a common index for both energy consumption values ​​and marginal carbon emission factor data allows each segment of operational energy consumption to acquire carbon emission attributes within its corresponding time range. For example, adjacent time slices in the operating power curve may have similar energy consumption values, but different corresponding marginal carbon emission factor data; pairing them distinguishes the carbon flow contribution of different time slices. When extended to long-cycle operation, multiple start-stop cycles, and cross-peak-valley operation scenarios, the time-series carbon flow dataset can retain the temporal variation characteristics of carbon emissions during the usage phase.

[0055] S4. Based on the time-series carbon flow dataset, the energy consumption value of each time slice is associated with the corresponding marginal carbon emission factor, and injected into the usage stage unit process in the complete life cycle inventory in chronological order to reconstruct the intermediate life cycle inventory and obtain the full life cycle carbon flow density index.

[0056] S4.1 Based on the time-series carbon flow dataset, using the starting timestamp of the sampling interval as the index key, unpack the time-series carbon flow dataset in ascending order of time, separate the cumulative energy consumption value and marginal carbon emission factor, construct time attribution tuples and push them into the execution queue in sequence.

[0057] Furthermore, the time-series carbon flow dataset is unpacked line by line in ascending chronological order. During unpacking, the cumulative energy consumption value and marginal carbon emission factor are extracted from the data records corresponding to each time slice, while preserving the correspondence between the start timestamp and the cumulative energy consumption value and marginal carbon emission factor. After extraction, the cumulative energy consumption value, marginal carbon emission factor, and start timestamp within the same sampling interval are combined to form a time attribution tuple, and then pushed into the execution queue in the chronological order corresponding to the start timestamp. This ensures that the time attribution tuples in the execution queue maintain a temporal arrangement consistent with the actual operation of green home appliances, thus completing the construction of the time attribution tuples and forming the execution queue.

[0058] Specifically, the cumulative energy consumption value and marginal carbon emission factor are reorganized into data units with a unified attribution structure, enabling subsequent attribution calculations to be performed independently around a single sampling interval. The time dimension and carbon emission attributes are simultaneously embedded into the same data structure, giving each sampling interval independent traceability capabilities. For example, when adjacent time slices correspond to different marginal carbon emission factors, the time attribution tuples can retain the correspondence without cross-time slice mixing. Extending this to long-cycle operation, multiple start-stop operation, and continuous variable load operation scenarios, the execution queue can still maintain a complete time sequence. S4.2 Based on the execution queue, pop time attribution tuples in the queue order, perform group attribution operation on the cumulative energy consumption value and marginal carbon emission factor in the tuple, obtain the carbon flow contribution of the sampling interval marked with the start time stamp, and arrange them in order to form a time index carbon flow sequence.

[0059] Furthermore, based on the execution queue, time attribution tuples are popped one by one according to the order in which they enter the queue, and the cumulative energy consumption value and marginal carbon emission factor in the time attribution tuples are subjected to the same group attribution operation. During the attribution process, the cumulative energy consumption value is used to characterize the actual energy consumption level within the corresponding sampling interval, the marginal carbon emission factor is used to characterize the carbon emission attribute corresponding to the power grid supply structure within the corresponding sampling interval, and a predefined grid benchmark marginal carbon emission factor is used to provide a unified comparison benchmark. After the attribution operation is completed, the carbon flow contribution of the sampling interval marked with the start timestamp is obtained, and these are arranged sequentially according to the time order corresponding to the time attribution tuples to form a time-indexed carbon flow sequence.

[0060] The expression for the carbon flow contribution of the sampling interval is: ; in, For the carbon flow contribution of the sampling interval marked with the start timestamp, For the time attribution tuple, the first The cumulative energy consumption value of each sampling interval. For the time attribution tuple, the first Marginal carbon emission factor corresponding to each sampling interval This is a predefined grid baseline marginal carbon emission factor.

[0061] S4.3. Based on the time-indexed carbon flow sequence, locate the usage phase unit process node in the complete life cycle list, and attach the carbon flow contribution of each sampling interval as an independent sub-entry to the usage phase unit process node according to the time-indexed carbon flow sequence. After timestamp and unit system verification, reconstruct the usage phase unit process with injected carbon flow values ​​in a dynamic set form.

[0062] Furthermore, based on the time-indexed carbon flow sequence, the usage phase unit process node in the complete lifecycle inventory is located. Then, according to the arrangement order corresponding to the time-indexed carbon flow sequence, the carbon flow contribution of each sampling interval is attached as an independent sub-entry to the usage phase unit process node. During the attachment process, the starting timestamp information corresponding to the carbon flow contribution of each sampling interval is retained, and the continuity of timestamps and the consistency of physical quantity units are verified. When the timestamp relationship is continuous and the physical quantity units remain consistent, the usage phase unit process is transformed from a fixed structure into a dynamic set form containing carbon flow contributions from multiple sampling intervals, forming a usage phase unit process with injected carbon flow values.

[0063] Specifically, a dynamic aggregate form composed of carbon flow contributions from multiple sampling intervals is constructed using time-indexed carbon flow sequences, enabling the usage phase unit process to have a time-dimensional expressive capability. This preserves the carbon flow differences formed by green appliances under different operating time slices. For example, low-load, high-load, and intermittent operation phases can all exist as independent sub-entries in the usage phase unit process. Extending to long-term operation scenarios, multi-condition operation scenarios, and complex power consumption scenarios, the usage phase unit process with injected carbon flow values ​​can completely record the carbon flow evolution process during the usage phase, improving the time attribution accuracy in the life cycle assessment process.

[0064] S4.4 Based on the usage stage unit processes with injected carbon flow values, extract the existing raw material acquisition stage unit processes, component manufacturing stage unit processes, and transportation stage unit processes from the complete life cycle inventory, and physically splice and align them hierarchically according to the life cycle stage sequence to reorganize and generate an intermediate life cycle inventory.

[0065] Furthermore, based on the usage phase unit processes with injected carbon flow values, existing raw material acquisition, component manufacturing, and transportation phase unit processes are extracted from the complete lifecycle inventory and arranged according to the corresponding order of lifecycle phases. During the arrangement process, physical splicing is performed on the raw material acquisition, component manufacturing, and transportation phase unit processes, as well as the usage phase unit processes with injected carbon flow values. Simultaneously, the hierarchical structure between each lifecycle phase is uniformly aligned to maintain a continuous correspondence between lifecycle phases. After completing the physical splicing and hierarchical alignment, an intermediate lifecycle inventory is generated.

[0066] Specifically, the intermediate life cycle inventory is not simply an overlay of usage stage data. Instead, while maintaining a complete life cycle inventory structure, it embeds a dynamic carbon flow representation into the life cycle stage chain. This creates a unified life cycle representation structure for the raw material acquisition stage, component manufacturing stage, transportation stage, and usage stage (where carbon flow values ​​have already been incorporated). This allows for a continuous link between static life cycle stages and dynamic usage stages. For example, carbon emissions from material production, manufacturing, and time-varying carbon flows during usage can maintain a corresponding relationship within the same life cycle framework. When extended to different product types, operating cycles, and energy structure scenarios, the intermediate life cycle inventory still maintains clear life cycle stage boundaries, stable hierarchical relationships, and continuous carbon flow trajectories, achieving the goal of generating the intermediate life cycle inventory.

[0067] S5. Obtain the full life cycle environmental impact assessment results through the full life cycle carbon flow density index.

[0068] S5.1 Based on the intermediate life cycle inventory, extract the carbon flow values ​​that have been marked in each unit process, and string them together in the order of life cycle stages to form a continuous carbon flow trajectory.

[0069] Furthermore, based on the intermediate lifecycle inventory, carbon flow values ​​are extracted from unit processes in the raw material acquisition stage, component manufacturing stage, transportation stage, and the usage stage where carbon flow values ​​have been injected. The lifecycle stage identifiers and their order are preserved for each unit process. During extraction, the carbon flow values ​​in each unit process are sequentially connected according to the order of occurrence of the lifecycle stages, forming a continuous expression of carbon flow information originally scattered across different lifecycle stages. The hierarchical structure in the intermediate lifecycle inventory maintains the sequential relationship between each unit process, ensuring that carbon flow values ​​do not overlap or become out of order during the concatenation process. After concatenation, a continuous carbon flow trajectory covering the raw material acquisition stage, component manufacturing stage, transportation stage, and usage stage is formed.

[0070] S5.2 Based on the continuous carbon flow trajectory, identify the carbon flow density distribution characteristics corresponding to the unit function output, map the carbon flow density distribution characteristics to the two-dimensional coordinate system of the time axis and the function output axis, generate a carbon flow density spatial distribution map, and select the centroid coordinates of the carbon flow density distribution as the whole life cycle carbon flow density index.

[0071] Furthermore, based on continuous carbon flow trajectories, the changes in carbon flow corresponding to each stage of the life cycle are analyzed to identify the carbon flow density distribution characteristics corresponding to unit function output. During the identification process, the distribution state of carbon flow throughout the entire life cycle is determined based on the relationship between the carbon flow values ​​at each position in the continuous carbon flow trajectory and the unit function output. Subsequently, the carbon flow density distribution characteristics are mapped onto a two-dimensional coordinate system formed by the time axis and the function output axis, allowing the carbon flow contributions formed at different life cycle stages to be expressed in a two-dimensional space, thereby generating a spatial distribution map of carbon flow density. In the spatial distribution map of carbon flow density, the centroid coordinates of the carbon flow density distribution are obtained, and these centroid coordinates are used as the carbon flow density index for the entire life cycle.

[0072] S5.3 Based on the life cycle carbon flow density index, call the preset evaluation threshold range mapping table to map the life cycle carbon flow density index to the corresponding environmental impact level label.

[0073] Furthermore, the life-cycle carbon flux density index is matched with each evaluation interval in the evaluation threshold interval mapping table. During the matching process, the corresponding evaluation result is determined based on the interval range of the life-cycle carbon flux density index, and the environmental impact level label associated with the corresponding interval is extracted from the evaluation threshold interval mapping table. After the matching is completed, a correspondence is established between the environmental impact level label and the life-cycle carbon flux density index, thereby generating the environmental impact level label.

[0074] S5.4. Based on the environmental impact level label and combined with the numerical characteristics of the life cycle carbon flow density index, generate the life cycle environmental impact assessment results.

[0075] Furthermore, the environmental impact level of green home appliances throughout their entire lifecycle is comprehensively expressed. During the processing, environmental impact level labels are used as the evaluation criteria, and the lifecycle carbon flow density index is used as the quantitative evaluation criterion. The level information and numerical characteristics are correlated and output, ensuring that the evaluation results simultaneously include environmental impact level information and carbon flow distribution characteristics. After completing the correlation output, the corresponding lifecycle environmental impact assessment results for green home appliances are generated, thus achieving the goal of generating a full lifecycle environmental impact assessment result.

[0076] This embodiment also provides a computer device applicable to the environmental impact assessment method for the entire life cycle of green home appliances based on carbon footprint tracking, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the environmental impact assessment method for the entire life cycle of green home appliances based on carbon footprint tracking proposed in the above embodiment.

[0077] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0078] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for environmental impact assessment of green home appliances based on carbon footprint tracking, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0079] In summary, this invention achieves unique traceability of source data by assigning a unique digital identity to green home appliances and binding it to the product's bill of materials, production process energy consumption, and transportation information, providing a foundation for full lifecycle data association. By importing and standardizing multi-stage carbon emission data to form a complete lifecycle inventory, it eliminates the heterogeneity of cross-stage data. By collecting actual operating power in time slices and pairing it with corresponding marginal carbon emission factors to generate a time-series carbon flow dataset, it overcomes the limitations of static evaluation methods in terms of time resolution, realizing a dynamic causal relationship between energy consumption and grid carbon emission intensity. By injecting carbon flow data into the unit processes of the usage stage in chronological order and reconstructing the intermediate lifecycle inventory, it achieves a structured upgrade from static total values ​​to dynamic time series in the usage stage. By concatenating carbon flow values ​​from each stage to generate a continuous carbon flow trajectory and mapping it to a carbon flow density spatial distribution map, and selecting the centroid coordinates as the full lifecycle carbon flow density index, it achieves a quantitative characterization of the full lifecycle carbon flow distribution characteristics, generating full lifecycle environmental impact assessment results and improving the spatiotemporal accuracy and reliability of the assessment results.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for environmental impact assessment of green home appliances throughout their entire life cycle based on carbon footprint tracking, characterized by: This includes assigning a unique digital identity to green home appliances and binding product bill of materials, production process energy consumption, and transportation information to generate an initial lifecycle inventory; Based on the initial life cycle inventory, carbon emission data from the raw material acquisition, component manufacturing, and transportation stages are imported and standardized to form a complete life cycle inventory. Based on a complete lifecycle inventory, the actual operating power curves and operating time information of green home appliances are continuously collected according to time slices. Marginal carbon emission factor data corresponding to each time slice are obtained and paired to generate time-series carbon flow datasets. Based on a time-series carbon flow dataset, the energy consumption value of each time slice is associated with the corresponding marginal carbon emission factor, and injected into the usage stage unit processes in the complete life cycle inventory in chronological order. This reconstructs the intermediate life cycle inventory and obtains the full life cycle carbon flow density index. The full life cycle environmental impact assessment results are obtained by using the full life cycle carbon flow density index.

2. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 1, characterized in that: The unique digital identity assigned to green home appliances includes During the production and off-line stage of green home appliances, a unique digital identity is burned into the control chip of the green home appliance, a digital identity writing completion signal is generated, the product bill of materials in the product database is called, an index relationship is established between the product bill of materials and the digital identity, and a digital identity mapping table bound to the product bill of materials is generated.

3. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 2, characterized in that: The initial lifecycle inventory includes, Based on the digital identity mapping table of the bound product bill of materials, production process energy consumption and transportation information are extracted from the manufacturing execution unit. The production process energy consumption and transportation information are associated with the corresponding fields in the digital identity mapping table of the bound product bill of materials to generate a bound data package. By using pre-defined life cycle assessment data parsing rules, the hierarchical relationship and physical quantity units of each data item in the bound data package are parsed. The parsed data is encapsulated and arranged so that each row represents an input or output substance and each column represents a process flow label, including the flow direction, substance name, physical quantity unit, and data source, to generate an initial life cycle list.

4. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 3, characterized in that: The complete lifecycle inventory includes, Based on the initial lifecycle inventory, the carbon emission database and process energy consumption records of the raw material acquisition stage, component manufacturing stage and transportation stage are called up to extract carbon emission data that matches the product bill of materials, production process energy consumption and transportation information, and generate carbon emission dataset. Based on the carbon emission dataset, and in accordance with the physical quantity unit conversion rules specified in the life cycle assessment standard, the energy consumption units from different sources are unified into standard energy units to generate a standardized carbon emission dataset. The values ​​from the standardized carbon emission dataset are filled into the corresponding positions in the initial life cycle inventory structure according to the unit process format specified in the life cycle assessment standard to generate a complete life cycle inventory.

5. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 4, characterized in that: The time-series carbon stream dataset includes, Based on a complete lifecycle inventory, periodic wake-up instructions and zero-crossing synchronization interrupt service routines are hard-coded into the firmware of the main control chip for green home appliances. The periodic wake-up command activates the current sampling channel according to a preset time slice, monitors the mains voltage zero-crossing signal and the load enable status bit, and when it is determined that there is a complete mains cycle within the time slice and the load enable status bit is high, the instantaneous power value sampled within the corresponding time slice is taken as the effective operating power. The start and end timestamps of the time slice are attached. If the conditions are not met within the time slice, the time slice is marked as invalid and the next adjacent time slice is used for supplementary sampling. The valid time slice operating power values ​​after validity judgment are arranged in time order to form an operating power curve. The operating power curve and the corresponding operating time period information of each time slice are encapsulated together to generate the original energy consumption record of the operating power curve and operating time period information. Based on the original energy consumption records of the operating power curve and operating time period information, a timestamped query request is sent to the regional power grid dispatch center to obtain the marginal carbon emission factor data corresponding to each time slice in the operating time period information, and generate a power grid response record of the marginal carbon emission factor data. Based on the grid response record of marginal carbon emission factor data, the energy consumption value of each time slice in the operating power curve and operating time period information is paired with the marginal carbon emission factor data of the corresponding time slice in the grid response record to generate a time-series carbon flow dataset.

6. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 5, characterized in that: The intermediate lifecycle inventory includes, Based on the time-series carbon flow dataset, the dataset is unpacked in ascending order of time using the starting timestamp of the sampling interval as the index key. The cumulative energy consumption value and marginal carbon emission factor are separated, and time attribution tuples are constructed and pushed into the execution queue in sequence. Based on the execution queue, time attribution tuples are popped out in the queue order. The cumulative energy consumption value and marginal carbon emission factor in the tuple are subjected to the same group attribution operation to obtain the carbon flow contribution of the sampling interval marked with the start time stamp. They are arranged in order to form a time index carbon flow sequence. Based on the time-indexed carbon flow sequence, the usage phase unit process node in the complete life cycle list is located. The carbon flow contribution of each sampling interval is attached as an independent sub-entry to the usage phase unit process node according to the time-indexed carbon flow sequence. After verification by timestamp and unit system, the carbon flow value injected into the usage phase unit process is reconstructed into a dynamic set form. Based on the usage phase unit processes with injected carbon flow values, extract the existing raw material acquisition phase unit processes, component manufacturing phase unit processes, and transportation phase unit processes from the complete life cycle inventory, physically splice and align them hierarchically according to the life cycle phase sequence, and reorganize them to generate an intermediate life cycle inventory.

7. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 6, characterized in that: The construction of the life-cycle carbon flux density index includes, Based on the intermediate life cycle inventory, the carbon flow values ​​marked in each unit process are extracted and connected in sequence according to the life cycle stage to form a continuous carbon flow trajectory. Based on continuous carbon flow trajectories, the carbon flow density distribution characteristics corresponding to unit function output are identified. The carbon flow density distribution characteristics are mapped to a two-dimensional coordinate system of time axis and function output axis to generate a carbon flow density spatial distribution map. The centroid coordinates of the carbon flow density distribution are selected as the whole life cycle carbon flow density index.

8. The method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in claim 7, characterized in that: The results of the full life-cycle environmental impact assessment include: Based on the life cycle carbon flux density index, a preset evaluation threshold range mapping table is invoked to map the life cycle carbon flux density index to the corresponding environmental impact level label. Based on the environmental impact level label and combined with the numerical characteristics of the life cycle carbon flow density index, the life cycle environmental impact assessment results are generated.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for environmental impact assessment of green home appliances based on carbon footprint tracking as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the green home appliance full life cycle environmental impact assessment method based on carbon footprint tracking as described in any one of claims 1 to 8.

Images