Power carbon footprint accounting method and system based on product batch tracing, electronic equipment and medium

Abstract

The invention relates to the technical field of carbon emission monitoring, in particular to an electric power carbon footprint accounting method and system based on product batch tracing, electronic equipment and a medium. The method comprises the following steps: acquiring an original data set comprising batch production time and carbon intensity, identifying to-be-analyzed batches from the original data set, and calculating to obtain a carbon intensity amplification grade of each to-be-analyzed batch; selecting to-be-analyzed batches which are not lower than a preset amplification grade threshold value from the carbon intensity amplification grades to form a target batch list; and obtaining the total production electricity consumption of each batch in the target batch list, inputting the total production electricity consumption into a preset carbon footprint dynamic accounting model, and outputting the adjusted carbon footprint accounting value and the carbon footprint accumulated total amount. Through the mode, the technical problem of carbon footprint accounting deviation caused by time period deviation in a production schedule dynamic adjustment environment of an existing electric power carbon footprint accounting technology is solved, and the accuracy and timeliness of carbon footprint accounting are improved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, and in particular to a method, system, electronic device and medium for calculating the carbon footprint of electricity based on product batch traceability. Background Technology

[0002] In modern industrial production, electricity carbon footprint accounting is a core component for enterprises to achieve energy conservation, emission reduction, and sustainable development strategies. Its accuracy directly impacts the effectiveness of carbon emission management. By tracking the carbon footprint generated by electricity consumption during production, enterprises can assess their environmental impact and optimize production strategies to reduce carbon emissions. However, with the dynamic changes in market demand and the increasing complexity of production scheduling, accurately calculating the carbon footprint in frequently adjusted production environments has become a significant challenge for the industry.

[0003] Currently, the technology for calculating the carbon footprint of electricity has reached a certain level. Most existing methods focus on carbon emission calculations under static conditions, relying on the carbon factor of electricity within a fixed time period for calculation, but fail to adequately address the interference caused by flexible changes in production schedules. For example, prior art document 1 (application publication number CN120822976A) discloses a method and system for collaborative monitoring and tracing of carbon emissions of electricity. It achieves dynamic allocation and tracing of carbon emission responsibilities by using real-time electricity consumption data of each production unit in the industrial park and information related to the industrial chain. This method improves the accuracy of responsibility allocation under the collaboration of multiple production units at the macro level, and is particularly suitable for industrial park-level carbon emission management. However, prior art document 1 mainly optimizes the linkage effect and load fluctuation of the upstream and downstream of the industrial chain. Its calculation basis still relies on the overall electricity consumption ratio of the production unit, without deeply considering the batch-level carbon footprint deviation caused by scheduling adjustments within a single production unit. Specifically, existing technologies suffer from two prominent problems: First, when production plans change due to emergency orders or order adjustments, the originally scheduled production period for a batch may shift from a low-carbon factor range to a high-carbon factor range. However, existing methods lack a dynamic sensing mechanism for the magnitude of this shift, failing to capture the temporal changes in the carbon footprint. Second, existing accounting models are mostly based on preset carbon factor averages or fixed time periods, making it difficult to adapt to the high-frequency fluctuations of time-of-use carbon factors. This leads to systematic errors between pre-calculated carbon footprint estimates and actual post-calculation results. These problems are intertwined, making it difficult for existing carbon footprint accounting technologies to balance accuracy and production responsiveness in highly flexible scheduling scenarios. This not only affects the reliability of corporate carbon emission data but may also mislead emission reduction decisions. Therefore, existing power carbon footprint accounting technologies suffer from technical problems in dynamic production scheduling environments where time period shifts cause carbon footprint accounting deviations. Summary of the Invention

[0004] To address the aforementioned shortcomings or deficiencies, this invention provides a method, system, electronic device, and medium for calculating the carbon footprint of electricity based on product batch traceability. This solves the technical problem of carbon footprint calculation deviation caused by time period offsets in existing electricity carbon footprint calculation technologies in dynamic production scheduling environments.

[0005] In a first aspect, the present invention provides a method for calculating the carbon footprint of electricity based on product batch traceability, comprising:

[0006] The raw dataset, including batch production time and carbon intensity, is obtained from the pre-set production management system and the power grid time-sharing carbon intensity database.

[0007] One or more batches to be analyzed are identified from the original dataset. The carbon intensity increase level of each batch to be analyzed is calculated by the time period span identification algorithm. The batch to be analyzed refers to the production batch whose time period delay exceeds the preset upper limit. The carbon intensity increase level is used to characterize the carbon intensity difference range of the corresponding batch to be analyzed.

[0008] In each carbon intensity increase level, one or more batches to be analyzed that are not lower than the preset increase level threshold are selected to form a target batch list.

[0009] The total electricity consumption of each batch in the target batch list is obtained from the production management system. The total electricity consumption is input into the preset carbon footprint dynamic accounting model. The carbon footprint dynamic accounting model identifies the carbon footprint increment of each batch due to time delays. The carbon footprint increment is added to the original carbon footprint estimate. The adjusted carbon footprint accounting value of each batch and the total cumulative carbon footprint of the overall production plan are output.

[0010] The original carbon footprint estimate refers to the baseline value of the carbon footprint calculated in advance based on the original production period of the batch and the corresponding carbon intensity.

[0011] Secondly, the present invention provides an electricity carbon footprint accounting system based on product batch traceability, comprising: The raw dataset acquisition module is configured to acquire raw datasets including batch production time and carbon intensity from the preset production management system and the power grid time-sharing carbon intensity database.

[0012] The intensity increase level calculation module is configured to identify one or more batches to be analyzed from the original dataset, and calculate the carbon intensity increase level of each batch to be analyzed through a time period span identification algorithm. The batch to be analyzed refers to the production batch whose time period delay exceeds the preset upper limit. The carbon intensity increase level is used to characterize the carbon intensity difference range of the corresponding batch to be analyzed.

[0013] The batch list construction module is configured to select one or more batches to be analyzed that are not lower than the preset increase level threshold in each carbon intensity increase level, in order to form the target batch list.

[0014] The carbon footprint accounting value generation module is configured to obtain the total electricity consumption of each batch in the target batch list from the production management system, input the total electricity consumption into the preset carbon footprint dynamic accounting model, identify the carbon footprint increment of each batch due to time delay through the carbon footprint dynamic accounting model, add the carbon footprint increment to the original carbon footprint estimate, and output the adjusted carbon footprint accounting value of each batch and the total cumulative carbon footprint of the overall production plan.

[0015] The original carbon footprint estimate refers to the baseline value of the carbon footprint calculated in advance based on the original production period of the batch and the corresponding carbon intensity.

[0016] Thirdly, the present invention provides an electronic device, comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to execute any of the electricity carbon footprint accounting methods based on product batch traceability of the present invention.

[0017] Fourthly, the present invention provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute any of the electricity carbon footprint accounting methods based on product batch traceability of the present invention.

[0018] The present invention provides a method for calculating the carbon footprint of electricity based on product batch traceability. This method is achieved through five core steps: raw data acquisition, batch identification, target batch list construction, dynamic carbon footprint calculation, and result output. Specifically, the method involves: acquiring raw datasets of batch production times and carbon intensities from the production management system and the power grid time-of-use carbon intensity database to establish a unified data foundation covering the time-series characteristics of production scheduling and power grid carbon emissions; identifying batches from the raw dataset whose time delay exceeds a preset upper limit and calculating their carbon intensity increase level using a time-span identification algorithm to accurately locate batches whose carbon intensity has changed significantly due to production plan adjustments; selecting batches with an increase level not lower than a preset threshold from each carbon intensity increase level to form a target batch list, focusing on the subset of batches most critical to the overall carbon footprint; acquiring the total electricity consumption of each batch in the target batch list from the production management system and inputting it into the dynamic carbon footprint calculation model to identify carbon footprint increments; finally, accumulating these increments to the original carbon footprint estimate and outputting the adjusted calculated value and cumulative total to achieve accurate and dynamic carbon footprint calculation.

[0019] In this technical solution, addressing the lack of a dynamic sensing mechanism for time-period offset in existing methods described in the background, this invention identifies batches under analysis with excessive time-period delays and calculates their carbon intensity increase levels. This achieves a quantitative assessment of the degree of carbon intensity change caused by production time variations, providing crucial input for subsequent accounting and overcoming the technical deficiency of existing technologies in capturing the dynamic changes in carbon footprint over time. Furthermore, addressing the problem that existing accounting models struggle to adapt to high-frequency fluctuations in time-of-use carbon factors, leading to systematic errors between pre-estimation and post-evaluation results, this invention constructs a dynamic carbon footprint accounting model. Based on the actual total electricity consumption and carbon intensity increase of the target batch, it dynamically identifies the carbon footprint increment, achieving accurate measurement of the carbon emission impact caused by scheduling changes. This overcomes the drawback of large deviations in traditional static accounting methods. Therefore, this invention solves the technical problem of carbon footprint accounting deviation caused by time-period offsets in existing power carbon footprint accounting technologies within a dynamically adjusted production scheduling environment, improving the accuracy and timeliness of carbon footprint accounting. Attached Figure Description

[0020] Figure 1 This is a flowchart of an embodiment of the present invention for calculating the carbon footprint of electricity based on product batch traceability; Figure 2 This is a flowchart illustrating a batch time period optimization allocation according to another embodiment of the present invention; Figure 3 This is a flowchart illustrating the logical process of production planning carbon footprint optimization decision-making according to another embodiment of the present invention. Figure 4 This is a flowchart illustrating the construction of an original dataset according to another embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electricity carbon footprint accounting system based on product batch traceability according to an embodiment of the present invention; Figure 6 This is a block diagram of an electronic device used to implement embodiments of the present invention. Detailed Implementation

[0021] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] During the development of this invention, the inventors, through extensive experiments and data analysis, revealed the intrinsic relationship between the magnitude of production scheduling shifts and carbon footprint accounting deviations: the greater the delay in batch production periods, the more significant the corresponding change in grid carbon intensity, leading to a systematic deviation between pre-production carbon footprint estimates and post-production actual accounting results. Based on this relationship, the inventors innovatively proposed this technical solution, which uses a time period span identification algorithm to quantify the increase in carbon intensity caused by scheduling changes, accurately calculates the carbon footprint increment through a dynamic accounting model, and combines an optimized allocation mechanism to reallocate production periods to low-carbon zones. This achieves a synergistic optimization of scheduling flexibility and carbon footprint accounting accuracy, embodying the core concept of data-driven dynamic correction.

[0023] Specifically, through comparative experiments, the invention team discovered that traditional static carbon footprint accounting methods suffer from a technical deficiency: they rely solely on the average carbon factor over a fixed period, lacking the ability to detect batch-level time-period shifts. These technical deficiencies lead to a significant increase in the discrepancy between the calculated results and actual emissions in high-penetration distributed power generation scenarios due to frequent temporal fluctuations in grid carbon intensity. However, the dynamic accounting method based on product batch traceability proposed in this invention improves the timeliness and accuracy of carbon footprint accounting; by constructing a target batch list focusing on key impact batches, it enables optimized allocation of accounting resources; and by triggering the reallocation of low-carbon periods through a closed-loop optimization mechanism, it ensures that the total carbon footprint remains under control despite schedule changes.

[0024] Therefore, this invention provides a method for calculating the carbon footprint of electricity based on product batch traceability, according to the first aspect. This method is based on a pre-set production management system and a grid time-of-use carbon intensity database, and can be applied to a system for calculating the carbon footprint of electricity based on product batch traceability (hereinafter referred to as the "system"). This system can run on electronic devices via software programs or firmware to perform dynamic calculation, optimized allocation, and corrective calculation of batch-level carbon footprints. Specifically, this system can be deployed in various hardware environments, including but not limited to: industrial servers, cloud computing platforms, edge computing devices, and embedded industrial controllers. This flexible deployment architecture enables the system to meet both enterprise-level real-time data processing and accurate carbon footprint management requirements, and to adapt to dynamic adjustments in production scheduling and high-frequency fluctuations in grid carbon intensity.

[0025] like Figure 1 As shown, the method may include: Step S110: Obtain the raw dataset including batch production time and carbon intensity from the preset production management system and the power grid time-sharing carbon intensity database.

[0026] The original dataset refers to a structured data set formed by integrating batch production scheduling data and grid carbon intensity time series data. It includes fields such as batch number, original production start and end time, delayed production start and end time, average carbon intensity of the original period, average carbon intensity of the delayed period, and original carbon footprint estimate, which are used to provide a complete input basis for dynamic carbon footprint accounting.

[0027] Specifically, the system can read the original start and end times and time delays of each batch from the scheduling records of the production management system through the data interface module. At the same time, it can extract the carbon intensity values ​​of the corresponding time period from the real-time data stream of the power grid time-sharing carbon intensity database, and calculate the arithmetic mean of the carbon intensity values ​​within the time period based on the original start and end times and the delayed start and end times respectively. Finally, the above fields are associated and integrated according to the batch number to generate the original dataset.

[0028] For example, the system processes a metal processing batch: the original production start and end times were from 02:00 to 06:00, with a delay of 180 minutes, changing the start and end times to 05:00 to 09:00; a query from the power grid database shows that the average carbon intensity for the original period was 320 grams per kilowatt-hour, and the average carbon intensity for the delayed period was 520 grams per kilowatt-hour; the original carbon footprint estimate was 1200 kilograms of CO2 equivalent. The system integrates this batch of data into a single record in the original dataset, with the batch number "BATCH-001", the original start and end times being "02:00~06:00", the delayed start and end times being "05:00~09:00", the original average carbon intensity being 320 grams per kilowatt-hour, the delayed average carbon intensity being 520 grams per kilowatt-hour, and the original carbon footprint estimate being 1200 kilograms of CO2 equivalent.

[0029] Step S120: Identify one or more batches to be analyzed from the original dataset, and calculate the carbon intensity increase level of each batch to be analyzed using a time period span identification algorithm.

[0030] Among them, the batch to be analyzed refers to the production batch whose time delay exceeds the preset upper limit. The carbon intensity increase level is used to characterize the carbon intensity difference range of the corresponding batch to be analyzed. The carbon intensity difference range is divided into three levels of low increase, medium increase and high increase by preset threshold to quantify the degree of carbon intensity change of the batch due to the time delay.

[0031] Specifically, the system can read the time delay amount of each batch in the original dataset through the data filtering module. If the time delay amount exceeds the preset upper limit (such as 180 minutes), it is marked as a batch to be analyzed. For each batch to be analyzed, the difference between the average carbon intensity of the original time period and the average carbon intensity of the delayed time period is calculated. If the difference is between 100 grams per kilowatt-hour and 200 grams per kilowatt-hour, it is defined as a medium increase level. If the difference is greater than 200 grams per kilowatt-hour, it is defined as a high increase level. If the difference is less than 100 grams per kilowatt-hour, it is defined as a low increase level.

[0032] For example, the system processes batch "BATCH-001": the time period is delayed by 180 minutes (exceeding the preset upper limit), the original average carbon intensity of the time period is 320 grams per kilowatt-hour, the average carbon intensity of the delayed time period is 520 grams per kilowatt-hour, and the carbon intensity difference is 200 grams per kilowatt-hour; according to the threshold rule, the difference of 200 grams per kilowatt-hour belongs to the medium increase level, so the carbon intensity increase level of this batch is marked as "medium increase".

[0033] In some embodiments, the system can calculate the carbon strength difference using the following formula (a): (a) Formula (a) is the formula for calculating the carbon intensity difference over time periods, which is used to quantify the change in average carbon intensity of a batch due to the delay in production time. This represents the carbon intensity value (unit: grams per kilowatt-hour) for the original production period of the batch, which is obtained by calculating the arithmetic mean of the carbon intensity values ​​for each hour within the original production period. This represents the representative carbon intensity value (unit: grams per kilowatt-hour) for the same batch during the delayed production period. The calculation method is the same as... same; This represents the carbon intensity difference (unit: grams per kilowatt-hour). A positive difference indicates that the production period has been delayed, resulting in a higher carbon intensity range; a negative difference indicates that the production has fallen into a lower carbon intensity range.

[0034] Next, in this embodiment, the system can also calculate the carbon footprint increment using formula (b): (b) Formula (b) is the formula for calculating carbon footprint increment, which is used to quantify the additional carbon emissions caused by time delay. The carbon intensity difference (in grams per kilowatt-hour) is calculated using formula (a). This indicates the total electricity consumption for the corresponding batch (unit: kilowatt-hours), which is obtained directly from the production management system. This represents the increase in carbon footprint (unit: kilograms of CO2 equivalent). Dividing by 1000 (one thousand) in the formula is used to complete the unit conversion from grams to kilograms.

[0035] Furthermore, in this embodiment, the system can also calculate the adjusted carbon footprint accounting value using formula (c): (c) Formula (c) is the formula for correcting the carbon footprint accounting value. It is used to add the impact of time period changes to the original estimated carbon footprint value of the batch to obtain the corrected accounting result. The estimated carbon footprint (unit: kilograms of carbon dioxide equivalent) of the batch is calculated in advance based on the original production period and corresponding carbon intensity, and is used as the accounting benchmark. The carbon footprint increment calculated using formula (b) (unit: kg CO2 equivalent). This indicates the adjusted carbon footprint accounting value (unit: kilograms of carbon dioxide equivalent).

[0036] Therefore, by combining formulas (a) to (c) above, the system can dynamically and quantitatively calculate the specific impact of production schedule changes on the carbon footprint of a single batch. The system first determines the direction and magnitude of carbon intensity changes using formula (a), then calculates the resulting increase (or decrease) in carbon emissions using formula (b) in conjunction with production energy consumption, and finally completes the accurate correction of the original calculated value using formula (c). This calculation chain ensures that the carbon footprint calculation results can respond to dynamic adjustments to the production plan in real time and accurately.

[0037] In some embodiments, the system can calculate the representative value of the time period carbon intensity of a batch using the following formula (e): (e) Formula (e) is the formula for calculating the weighted average carbon intensity of electricity consumption, which is used to measure the overall carbon emission intensity level of a batch during a specific production period. These represent the electricity consumption for the first hour, second hour, and so on up to the nth hour within the production period, respectively, in kilowatt-hours. These represent the grid carbon intensity values ​​corresponding to the above hours, in grams per kilowatt-hour; This represents the calculated carbon intensity value for the time period, expressed in grams per kilowatt-hour. This value is not a simple arithmetic average, but a weighted average based on the actual electricity consumption of each hour, providing a more accurate reflection of the average carbon intensity of the electricity consumed during that production batch.

[0038] Step S130: Select one or more batches to be analyzed that are not lower than the preset increase level threshold in each carbon intensity increase level to form a target batch list.

[0039] The target batch list refers to a set of batches that include carbon intensity increase levels that reach or exceed preset thresholds (such as medium or high increase levels). It is used to focus on a subset of batches that have a significant impact on carbon footprint in order to optimize accounting efficiency.

[0040] Specifically, the system can use a priority filtering module to traverse the carbon intensity increase level of all batches to be analyzed. If the level is "medium increase" or "high increase", the batch number, carbon intensity difference and increase level are added to the target batch list; if the level is "low increase", it is excluded.

[0041] For example, the system filters from 10 batches to be analyzed: 3 batches are of high amplification level (carbon intensity difference greater than 200 g / kWh), 5 batches are of medium amplification level (difference between 100 and 200 g / kWh), and 2 batches are of low amplification level (difference less than 100 g / kWh). The preset amplification level threshold is medium amplification, so the target batch list contains 8 batches (3 high amplification + 5 medium amplification). The list records the batch number, such as "BATCH-001", and its corresponding carbon intensity difference of 200 g / kWh and amplification level of "medium amplification".

[0042] Step S140: Obtain the total electricity consumption of each batch in the target batch list from the production management system, input the total electricity consumption into the preset carbon footprint dynamic accounting model, identify the carbon footprint increment of each batch due to time delay through the carbon footprint dynamic accounting model, add the carbon footprint increment to the original carbon footprint estimate, and output the adjusted carbon footprint accounting value of each batch and the total cumulative carbon footprint of the overall production plan.

[0043] Among them, the original carbon footprint estimate refers to the carbon footprint baseline value pre-calculated based on the original production period and corresponding carbon intensity of the batch. The carbon footprint dynamic accounting model refers to the algorithm module that uses mathematical operations to correlate the carbon intensity difference with the total electricity consumption of production to calculate the carbon footprint increment. Its core formula is: carbon footprint increment (kg CO2 equivalent) = carbon intensity difference (g per kilowatt-hour) × total electricity consumption of production (kilowatt-hour) / 1000.

[0044] Specifically, the system can read the total electricity consumption of each batch in the target batch list from the metering module of the production management system through the carbon footprint calculation engine, and call the carbon footprint dynamic accounting model to perform the following operations: First, calculate the carbon footprint increment based on the batch carbon intensity difference and the total electricity consumption; second, add the increment value to the original carbon footprint estimate to obtain the adjusted carbon footprint accounting value; finally, summarize the carbon footprint values ​​of all batches (including the target batch and the unchanged batch) to obtain the total cumulative carbon footprint.

[0045] For example, the system processes batch "BATCH-001" in the target batch list: the total electricity consumption is 5000 kWh, the carbon intensity difference is 200 grams per kWh, and the carbon footprint increment is calculated as: 200 × 5000 / 1000 = 1000 kg CO2 equivalent; the original carbon footprint estimate is 1200 kg CO2 equivalent, and the adjusted carbon footprint calculation is: 1200 + 1000 = 2200 kg CO2 equivalent. If the overall production plan includes 100 batches, of which 8 batches in the target batch list have a total adjusted carbon footprint of 15000 kg CO2 equivalent, and the remaining 92 batches retain the original estimate of 80000 kg CO2 equivalent, then the total cumulative carbon footprint is: 15000 + 80000 = 95000 kg CO2 equivalent.

[0046] In another embodiment, such as Figure 2 The diagram shown is an example of the process flow for batch time slot optimization allocation in this invention. After obtaining the total accumulated carbon footprint, this method further reduces overall carbon emissions through intelligent optimization scheduling. Specifically, it includes the following steps: obtaining the original start and end times of each batch's production from the production management system, the time slot delay caused by order insertion, and the original carbon footprint estimate for each batch; obtaining the carbon intensity for each time slot from the grid time-sharing carbon intensity database to obtain the original dataset of batch production times and carbon intensity; identifying batches whose time slot delay exceeds a preset upper limit; analyzing the span of the batch's production time slot from a low-carbon intensity zone to a high-carbon intensity zone using a time slot span identification algorithm; extracting the carbon intensity of the two zones before and after the span; determining the carbon intensity increase level based on the difference in carbon intensity before and after the span; and determining the target batch list based on the carbon intensity increase level; obtaining the total electricity consumption of each batch in the target batch list from the production management system; and using a preset carbon footprint dynamic accounting model to compare the carbon intensity increase with the total electricity consumption... Electricity consumption is analyzed to identify the carbon footprint increase caused by time delays in each batch. The carbon footprint increase is added to the original carbon footprint estimate to obtain the adjusted carbon footprint accounting value for each batch and the total cumulative carbon footprint of the overall production plan. The optimization upper limit and optimization target reduction ratio of the total cumulative carbon footprint are obtained from the carbon emission management system. It is analyzed whether the total cumulative carbon footprint exceeds the optimization upper limit. The production time of each batch is reallocated through the batch time period optimization allocation algorithm. The target batch list is prioritized to be allocated to the low carbon intensity zone. The reduction ratio of the total cumulative carbon footprint after reallocation is evaluated to see if it reaches the optimization target reduction ratio, resulting in the optimized production plan. The start and end times of new production for each batch and the carbon intensity of the carbon intensity zone in which they are located are extracted from the optimized production plan. Combined with the original dataset of batch production time and carbon intensity, the final carbon footprint of each batch is recalculated to achieve synchronous correction and accounting of electricity carbon footprint under scheduling changes.

[0047] For example, an electronics assembly company calculated its current production plan's cumulative carbon footprint to be 120,000 kg of CO2 equivalent using the method of this invention. This value exceeded the optimization upper limit of 100,000 kg of CO2 equivalent read from the carbon emission management system, and the system's preset target reduction rate was 15%. The system then initiated the optimization allocation process. The target batch list contained 20 batches to be optimized, with their carbon intensity increase levels quantified as values ​​from 1 to 5. The system sorted the batches from highest to lowest level, generating a batch priority sequence. A query revealed that there were three available time slots in the low-carbon intensity zone of the power grid (carbon intensity below 380 grams of CO2 per kilowatt-hour) for the next 36 hours, with a total capacity of 18 hours. The system allocated according to priority: the first three batches at level 5 (each requiring 3 hours) were successfully allocated to the first low-carbon time slot; subsequent batches were matched according to the remaining capacity. After allocation, the system recalculated the total carbon footprint, reducing it to 85,000 kg of CO2 equivalent, a decrease of 29.2%, far exceeding the 15% target. Therefore, the system ultimately generated and output this optimized production plan, guiding the company's actual production scheduling and achieving significant carbon emission reduction.

[0048] In another embodiment, such as Figure 3 The diagram illustrates a logical flow example of the carbon footprint optimization decision-making process for production planning in this invention. After completing dynamic carbon footprint accounting, this process provides an automated decision-making and optimization method based on total constraints and target orientation, specifically including the following steps: First, the system obtains a preset optimization upper limit and the expected target reduction percentage of the total carbon footprint from the carbon emission management system. Next, the calculated current cumulative carbon footprint is compared with the optimization upper limit. If the current total exceeds the upper limit, a batch time period reallocation process is triggered. This process first prioritizes batches in the target batch list according to their carbon intensity increase level from high to low. Then, the system queries the grid time-sharing carbon intensity database to obtain available time period capacity information for low-carbon intensity zones within the future planning cycle. Next, based on the generated batch priority sequence, the system sequentially attempts to allocate each batch to the available time period of the low-carbon intensity zone. After completing all batch time period reallocation attempts, the system recalculates the total carbon footprint of the entire production plan based on the new production start and end times and corresponding carbon intensity of each batch, and calculates the reduction percentage compared to before optimization. Finally, the system determines whether the recalculated decrease reaches the preset target decrease ratio; if it does, the optimized production plan is generated and output.

[0049] For example, the monthly production plan of a chemical company, calculated using the method of this invention, shows a cumulative carbon footprint of 85,000 kg of CO2 equivalent, exceeding the company's set monthly optimization limit of 80,000 kg of CO2 equivalent. The system's preset target reduction rate is 10%. The system then starts. Figure 3The optimization decision-making process is illustrated. The target batch list contains 15 batches to be optimized, with their carbon intensity increase levels calculated to be between 1 (low increase) and 5 (high increase). The system first sorts the batches from highest to lowest level. Next, it queries the grid and finds three main low-carbon intensity zones (average carbon intensity below 350 grams of CO2 per kilowatt-hour) within the next 72 hours, with a total available time slot capacity of 22 hours. The system allocates according to priority: batches with a carbon intensity increase level of 5 (requiring a total production time of 8 hours) are prioritized for allocation to the first low-carbon time slot; the remaining batches are matched sequentially according to capacity. After allocation, the system recalculates the optimized carbon footprint to 72,000 kg of CO2 equivalent, a reduction of 15.3%, exceeding the 10% target. Therefore, the system determines the optimization is successful and outputs the final optimized production plan, providing the company with an executable low-carbon production scheduling solution.

[0050] In another embodiment, such as Figure 4 The diagram illustrates the flowchart for constructing the original dataset in this invention. This flowchart details the specific steps for acquiring and calculating key fields from multiple data sources to ultimately generate a structured original dataset, providing a precise data input foundation for subsequent dynamic carbon footprint accounting. Specifically, it includes the following steps: First, the original start and end times, time delay, and original carbon footprint estimate of the target batch are read from the scheduling records of the production management system. Next, based on the original start and end times of the batch, the time-sharing carbon intensity database of the power grid is queried to obtain the hourly carbon intensity value sequence within that time period. Subsequently, based on the obtained carbon intensity value sequence, the arithmetic mean of the hourly carbon intensity values ​​within the original production time period is calculated to obtain the average carbon intensity for the original time period. Then, based on the time delay, the start and end times of the delayed production of the batch are calculated, and the database is queried again to obtain the carbon intensity values ​​for the new time period, and their arithmetic mean is calculated to obtain the average carbon intensity for the delayed time period. Finally, all fields obtained in the above steps, including the batch number, the original and delayed start and end times, the two carbon intensity averages, and the original carbon footprint estimate, are correlated and integrated to generate a structured original dataset.

[0051] For example, the system processes batch "BATCH-2024-05A". First, it reads from the production management system that the original production start and end time for this batch was from 01:00 to 05:00, with a delay of 120 minutes, and the original estimated carbon footprint was 800 kg CO2 equivalent. Next, the system queries the power grid database for the carbon intensity values ​​for the corresponding 4 hours during the original time period (01:00~05:00): 300, 295, 290, and 310 grams per kilowatt-hour. Subsequently, the system calculates the average carbon intensity for the original time period as: (300+295+290+310) / 4 = 298.75 grams per kilowatt-hour. The system then calculates the delayed start and end times as 03:00 to 07:00 (120 minutes beyond the original time) and queries the carbon intensity values ​​for the new time period as 310, 305, 410, and 460 grams per kilowatt-hour. The average carbon intensity for the delayed period is calculated as (310 + 305 + 410 + 460) / 4 = 371.25 grams per kilowatt-hour. Finally, the system integrates this information to generate a complete record containing the batch number, time information, average carbon intensity, and the original carbon footprint estimate. This record is added to the original dataset, providing a complete data foundation for subsequent identification of the "batch to be analyzed" and calculation of the "carbon intensity increase level."

[0052] Therefore, according to the above implementation method, the system achieves its goals through five core steps: raw data acquisition, batch identification, target batch list construction, dynamic carbon footprint calculation, and result output. Specifically, the system acquires raw datasets of batch production times and carbon intensity from the production management system and the power grid time-sharing carbon intensity database to establish a unified data foundation covering the time-series characteristics of production scheduling and power grid carbon emissions. It identifies batches from the raw dataset whose time delay exceeds a preset upper limit and calculates their carbon intensity increase level using a time-span identification algorithm to accurately locate batches whose carbon intensity has changed significantly due to production plan adjustments. From each carbon intensity increase level, it selects batches not lower than a preset increase level threshold to form a target batch list, focusing on the subset of batches most critical to the overall carbon footprint. It obtains the total electricity consumption of each batch in the target batch list from the production management system and inputs it into the dynamic carbon footprint calculation model to identify carbon footprint increments. Finally, it accumulates these increments to the original carbon footprint estimate and outputs the adjusted calculated value and cumulative total, achieving accurate and dynamic carbon footprint calculation.

[0053] Specifically, in this implementation, the technical solution addresses the lack of a dynamic sensing mechanism for time-period offset in existing methods described in the background section. By identifying batches to be analyzed with excessive time-period delays and calculating their carbon intensity increase levels, a quantitative assessment of the degree of carbon intensity change caused by production time variations is achieved. This provides crucial input for subsequent accounting and resolves the technical deficiency of existing technologies in failing to capture the dynamic changes in carbon footprint over time. Furthermore, addressing the problem that existing accounting models struggle to adapt to high-frequency fluctuations in time-of-use carbon factors, leading to systematic errors between pre-estimation and post-evaluation results, a dynamic carbon footprint accounting model is constructed. Based on the actual total electricity consumption and carbon intensity increase of the target batch, the carbon footprint increment is dynamically identified, achieving accurate measurement of the carbon emission impact caused by scheduling changes. This overcomes the drawback of large deviations in traditional static accounting methods. Therefore, this implementation solves the technical problem of carbon footprint accounting deviations caused by time-period offsets in existing power carbon footprint accounting technologies within a dynamically adjusted production scheduling environment, improving the accuracy and timeliness of carbon footprint accounting.

[0054] In other embodiments, Table 1 below shows detailed data records and calculation results of dynamic carbon footprint accounting for multiple batches in a specific production plan according to the scheme of the present invention. The table clearly presents the entire process from "identifying the batch to be analyzed" to "calculating the carbon footprint increment" and then to "deriving the final accounting value" through specific numerical values, confirming the practical application effect of the "time period span identification algorithm" and the "dynamic carbon footprint accounting model" in the scheme.

[0055]

[0056] Specifically, referring to the data in Table 1 and the solution of this invention: Batch identification to be analyzed: The system first identifies batches whose time delay exceeds a preset limit (e.g., set to 60 minutes). As shown in Table 1, all 5 batches (delayed by 60 to 420 minutes) are identified as "batches to be analyzed".

[0057] Carbon intensity increase level calculation: The system calculates the carbon intensity difference (actual carbon intensity - original carbon intensity) for each batch using a "time period span identification algorithm," and determines its "increase level" based on a preset threshold range (low: <100, medium: 100-200, high >200, unit: gCO2eq / kWh). For example, batch BATCH-002 has a difference of +200.0 gCO2eq / kWh and is classified as "high" increase level; batch BATCH-001 has a difference of +100.3 gCO2eq / kWh and is classified as "medium" increase level.

[0058] Target batch list construction: If the preset "increase level threshold" is "medium", the system will select batches with an increase level of "medium" or "high" (i.e., BATCH-001, BATCH-002, BATCH-004) to form a "target batch list" for key accounting and subsequent optimization.

[0059] Dynamic Calculation and Result Output: Based on the "Dynamic Carbon Footprint Calculation Model," the system calculates the increment for each batch using the formula: Carbon Footprint Increment = Carbon Intensity Reduction × Production Electricity Consumption / 1000. For example, the increment for BATCH-001 = 100.3 × 1500 / 1000 = 150.5 kg CO2eq. Ultimately, the "Calculated Carbon Footprint" is derived by adding the "Baseline Carbon Footprint" (pre-calculated based on the original time period and carbon intensity) to the "Carbon Footprint Increment." For example, the calculated value for BATCH-001 = 630.8 + 150.5 = 781.3 kg CO2eq.

[0060] As can be seen from the complete accounting chain shown in this table, the present invention can accurately quantify the additional carbon emissions (carbon footprint increment) caused by the delay in production time for each batch, and dynamically update this increment to the final accounting result, effectively solving the problem of inaccurate carbon footprint accounting caused by ignoring scheduling changes in the background technology.

[0061] In other embodiments, Table 2 below shows the summary effect of the carbon footprint optimization management of the overall production plan by the scheme described in this invention over multiple consecutive production cycles (May to September 2024). This table systematically presents the closed-loop management process of the scheme from "total quantity judgment" to "triggered optimization" and then to "effect verification" using specific and quantitative data, demonstrating the ability of the "batch time period optimization allocation algorithm" to continuously and effectively reduce carbon emissions in a real production environment.

[0062]

[0063] Specifically, referring to the data in Table 2 and the solution of this invention: Total Amount Judgment and Optimization Trigger: At the beginning of each production cycle, the system compares the "total amount before optimization" obtained from dynamic accounting with the "optimization upper limit" obtained from the carbon emission management system. As shown in the table, the "total amount before optimization" for all five cycles exceeded the corresponding "optimization upper limit," thus the system automatically triggered the optimization process.

[0064] Optimization Execution and Effect Calculation: The system uses a "batch time period optimization allocation algorithm" to prioritize batches in the "target batch list" based on "carbon intensity increase level" and reassign them to "low carbon intensity zones". The "Optimization Summary" column in Table 2 records the actual number of optimization actions performed in each cycle (e.g., 15 times, 18 times). After optimization, the system immediately recalculates based on the new production time period to obtain the "optimized total" and "actual reduction percentage".

[0065] Target Verification and Continuous Management: By comparing the "actual reduction rate" with the preset "target reduction rate" (5.0% in this example), the system can objectively determine whether the optimization target has been achieved. Table 2 shows that the target was successfully achieved in all cycles, and the actual reduction rate (10.0%) in most cycles (such as cycles 05 and 06) was significantly higher than the target value, demonstrating the effectiveness of the optimization algorithm. This series of periodic records forms a closed loop for carbon footprint management, providing continuous and reliable data support for the company's sustainable production and emission reduction decisions.

[0066] Therefore, the present invention can not only correct the carbon footprint in a single calculation, but also achieve continuous and proactive management of the overall carbon footprint of an enterprise through periodic intelligent optimization and effect verification, effectively supporting the achievement of carbon emission reduction targets.

[0067] In some embodiments, after outputting the adjusted carbon footprint calculation values ​​for each batch and the total cumulative carbon footprint of the overall production plan, the above method further includes: In response to the total accumulated carbon footprint exceeding the preset optimization limit, the batch time period optimization allocation algorithm prioritizes the batches in the target batch list according to the carbon intensity increase level, and reallocates the production time periods of each batch, so that the target batch list is preferentially allocated to the low carbon intensity zone, generating an optimized production plan.

[0068] Among them, the optimization upper limit refers to the threshold for the total cumulative carbon footprint dynamically set according to the enterprise's annual carbon emission quota and the current production cycle progress, which is usually set to 95% of the cycle carbon emission budget; the batch time period optimization allocation algorithm refers to the optimization method of prioritizing batches by carbon intensity increase level and reallocating production time periods based on the available time period capacity of the low carbon intensity zone of the power grid.

[0069] Specifically, the system reads the optimization upper limit from the carbon emission management system and compares it with the current cumulative carbon footprint. If the cumulative carbon footprint exceeds the optimization upper limit, the algorithm process is initiated: First, the batches in the target batch list are sorted from high to low according to the carbon intensity increase level to generate a batch priority sequence. Then, the available time slot capacity information of the low carbon intensity zone is obtained from the grid time-sharing carbon intensity database. The available time slot capacity is the total amount of unoccupied production time slots within the low carbon intensity zone. Next, according to the batch priority sequence, each batch is allocated to the available time slots of the low carbon intensity zone in turn. If the required production time slot of the current batch exceeds the current available time slot capacity, the current batch is attempted to be allocated to the next available low carbon time slot. If the available time slots of all low carbon intensity zones cannot accommodate the current batch, the original production time slot of the current batch is kept unchanged. Finally, the time slot allocation results of all batches are summarized to generate an optimized production plan.

[0070] For example, an auto parts manufacturer has 20 batches to be optimized. Eight of these batches are marked as high priority due to their high carbon intensity increase level (e.g., a difference greater than 200 grams per kilowatt-hour). The system identifies three low-carbon periods within the next 48 hours: 2:00 AM to 7:00 AM, 1:00 AM to 6:00 AM the next day, and 2:00 AM to 8:00 AM the third day, totaling 16 hours of available capacity. After sorting by priority, the first five high-priority batches are assigned to the first low-carbon period, the remaining three high-priority batches are assigned to the second period, and the medium and low-priority batches are arranged according to the remaining capacity. Batches that cannot be allocated remain in their original periods, and the optimized production plan is finally generated.

[0071] In some embodiments, the system can calculate the comprehensive priority score for batch optimization allocation using the following formula (f): (f) Formula (f) is the comprehensive priority score calculation formula, which is used to quantify the importance of batches in time period optimization allocation in order to determine the order of reallocation. The carbon intensity increase level of a batch is a quantitative value that is divided according to the size of the difference in carbon intensity (for example, the value range is 1 to 5, where 5 represents the highest increase level). It indicates the urgency of batch delivery and is a quantitative value set according to the urgency of the customer's delivery requirements (for example, the value ranges from 1 to 3, with 3 representing the most urgent). and respectively to give and The weighting coefficients satisfy . This represents the calculated overall score; a higher value indicates a higher priority in the optimized allocation. For example, the carbon strength increase level of a stamping batch from an automotive parts manufacturing company. Level 5 (highest level), delivery urgency Set to 2 (Intermediate). , Then its overall score .

[0072] Next, in this embodiment, the system can also calculate the carbon footprint value of the batch using formula (g): (g) Formula (g) is the basic formula for calculating carbon footprint, which is used to convert electricity consumption into carbon emissions. This indicates the carbon intensity corresponding to the production period, expressed in grams per kilowatt-hour. This indicates the total electricity consumption for this batch of production, expressed in kilowatt-hours. This represents the calculated carbon footprint value, expressed in kilograms of CO2 equivalent. Dividing by 1000 (one thousand) in the formula is used for unit conversion from grams to kilograms. For example, if a batch of production consumes 5000 kWh of electricity, and the carbon intensity during its production period is 280 grams per kWh, then its carbon footprint value is... Kilograms of carbon dioxide equivalent.

[0073] Furthermore, in this embodiment, the system can also calculate the final carbon footprint accounting value of the batch after optimized allocation using formula (h): ;(h) Formula (h) is the final carbon footprint calculation formula, used to determine the carbon emissions of a batch after it has been optimized and allocated to a new time period. This represents the average carbon intensity of the batch during the new production period to which it was reassigned, expressed in grams per kilowatt-hour. This indicates the total electricity consumption for this batch of production, expressed in kilowatt-hours. This represents the final carbon footprint calculation, expressed in kilograms of CO2 equivalent. This value will replace any previous estimates or interim values ​​as the authoritative result for this batch. For example, the average carbon intensity after optimizing the allocation of a batch of electricity consumption (4500 kWh) to the new time period. If the carbon footprint is 310 grams per kilowatt-hour, then its final carbon footprint accounting value is... Kilograms of carbon dioxide equivalent.

[0074] Furthermore, in this embodiment, the system can also calculate the reduction in carbon footprint before and after the optimization of the entire production plan using formula (i) to verify the optimization effect: (i) Formula (i) is the formula for calculating the rate of decrease in carbon footprint. This represents the cumulative total of carbon footprint values ​​for all batches before optimization (unit: kilograms of carbon dioxide equivalent). This represents the cumulative total of the final carbon footprint accounting value for all batches (including adjusted and unadjusted batches) after optimization (unit: kilograms of carbon dioxide equivalent). This indicates the calculated percentage reduction in carbon footprint. For example, if a production plan's cumulative carbon footprint before optimization was 85,000 kg CO2 equivalent, and after optimization it decreased to 68,000 kg CO2 equivalent, then the percentage reduction is [percentage missing]. .

[0075] Therefore, through the combination of formulas (f) to (i) above, the system can achieve full-process quantitative management from intelligent scheduling optimization to accurate carbon footprint accounting and verification. The system first quantifies the multi-dimensional priorities of each batch to be adjusted using formula (f), providing a ranking basis for optimized allocation; after allocation, it calculates the final carbon footprint for each batch based on the new production period using formula (h); then, by accumulating the carbon footprints of all batches (for unchanged batches, using their original estimated value or the value calculated according to formula (g)), the optimized total is obtained. Finally, by comparing the total amount before and after optimization using formula (i), the exact carbon emission reduction margin is calculated, thereby scientifically and quantitatively evaluating the overall effectiveness of this optimized allocation.

[0076] Extract the new production start and end times and the carbon intensity of each batch from the optimized production plan. The new production start and end times refer to the actual production start time and actual production end time of each batch in the optimized production plan.

[0077] Among them, the new production start and end times refer to the actual start and end times of production arranged in the optimized production plan for each batch; carbon intensity zoning refers to the time period categories divided based on the range of carbon intensity values ​​of the power grid, including low carbon intensity zoning and high carbon intensity zoning. The time period with carbon intensity below the preset threshold (such as 350 grams of carbon dioxide per kilowatt-hour) is classified as low carbon intensity zoning, and the time period with carbon intensity above the preset threshold (such as 500 grams of carbon dioxide per kilowatt-hour) is classified as high carbon intensity zoning.

[0078] Specifically, the system uses a data extraction module to read the new start and end times of each batch of production from the optimized production plan. It then queries the grid time-of-use carbon intensity database to determine the carbon intensity zone based on the new production start and end times, obtains the carbon intensity value sequence for that time period, and calculates the arithmetic mean of the carbon intensity values ​​within that time period as the new time period's average carbon intensity. For example, if a textile company's dyeing batch has an optimized production period from 3:00 AM to 7:00 AM, the system finds that this time period belongs to a low-carbon intensity zone, with a carbon intensity value sequence of 300, 305, 310, and 315 grams per kilowatt-hour. The calculated average carbon intensity for the new time period is: (300 + 305 + 310 + 315) / 4 = 307.5 grams per kilowatt-hour.

[0079] Based on the average carbon intensity corresponding to the start and end times of new production and the total electricity consumption of each batch, the final carbon footprint of each batch is recalculated, and the carbon footprint is synchronously corrected under scheduling changes.

[0080] Among them, recalculation refers to recalculating the carbon footprint value of the batch based on the new production period and the corresponding carbon intensity; the final carbon footprint refers to the total carbon emissions of the batch during the optimized production period, in kilograms of carbon dioxide equivalent.

[0081] Specifically, the system reads the total electricity consumption of each batch of production through the carbon footprint calculation engine, multiplies the total electricity consumption by the average carbon intensity of the new period, and then divides by 1000 to convert the unit (from grams to kilograms) to obtain the final carbon footprint value for each batch. The calculation formula is: Final carbon footprint (kilogram CO2 equivalent) = Average carbon intensity of the new period (grams per kilowatt-hour) × Total electricity consumption of production (kilograms) / 1000. For example, if the total electricity consumption of a batch of production is 4500 kWh and the average carbon intensity of the new period is 310 grams per kilowatt-hour, then the final carbon footprint is calculated as: 310 × 4500 / 1000 = 1395 kilograms of CO2 equivalent. The system updates the final carbon footprint value of all batches to the production management database to achieve synchronous correction of carbon footprint data.

[0082] Therefore, according to the above implementation method, the system can realize synchronous correction and accounting of the electricity carbon footprint under scheduling changes, thereby improving the accuracy and timeliness of carbon footprint management.

[0083] In some embodiments, a raw dataset including batch production time and carbon intensity is obtained from a preset production management system and a grid time-of-use carbon intensity database, including: Retrieve the original start and end times of production, the amount of time delay, and the estimated original carbon footprint for each batch from the production management system.

[0084] Among them, the original production start and end times refer to the specific times when the batch production is scheduled to begin and end; the time delay refers to the duration (in minutes) of the delay in the start time of the batch production caused by the order insertion operation; and the original carbon footprint estimate refers to the carbon footprint baseline value (in kilograms of carbon dioxide equivalent) pre-calculated based on the original production time period of the batch and the corresponding carbon intensity.

[0085] Specifically, the system reads the scheduling information of each batch in real time from the scheduling records of the production management system through the data interface module. This includes the original start and end times, the time delay, and the original carbon footprint estimate, and verifies the data integrity. For example, the system reads that the original production start and end times for batch "BATCH-001" are from 02:00 to 06:00, the time delay is 180 minutes, and the original carbon footprint estimate is 1200 kg of CO2 equivalent.

[0086] The carbon intensity values ​​for each batch and corresponding time period are obtained from the grid time-sharing carbon intensity database.

[0087] Carbon intensity refers to the amount of carbon emissions generated by a unit of electricity consumption in the power grid at different time periods, expressed in grams per kilowatt-hour.

[0088] Specifically, the system retrieves the carbon intensity data sequence for the corresponding time period from the grid time-of-use carbon intensity database based on the production time period of the batch through a database query interface, and sorts it chronologically. For example, for the original production time period of batch "BATCH-001" from 02:00 to 06:00, the system obtains the hourly carbon intensity value sequence within that time period, such as 320 grams per kilowatt-hour, 310 grams per kilowatt-hour, 305 grams per kilowatt-hour, and 315 grams per kilowatt-hour.

[0089] Based on the original start and end times of each batch's production, the average carbon intensity during the original production period is calculated to obtain the average carbon intensity for the original production period.

[0090] The mean carbon intensity refers to the arithmetic mean of the carbon intensity values ​​for each hour during the production period, and is used to characterize the average carbon emission level during that period.

[0091] Specifically, the system uses an arithmetic mean method, summing all carbon intensity values ​​within a time period and dividing by the number of hours to calculate the average carbon intensity. For example, the average carbon intensity for the originally scheduled production period of batch "BATCH-001" is: (320+310+305+315) / 4=312.5 grams per kilowatt-hour.

[0092] Based on the time delay of each batch, the start and end times of production after the delay and the average carbon intensity of the corresponding time period are calculated to obtain the average carbon intensity of the delayed time period.

[0093] Among them, the delayed production start and end times refer to the actual start and end times of production after the batch is adjusted due to the delay, and the average carbon intensity of the delayed period is the average carbon intensity calculated based on the new period.

[0094] Specifically, the system adjusts the original start and end times based on the time period delay (e.g., a delay of 180 minutes corresponds to adding 3 hours to the original time), then queries the carbon intensity data for the new time period and calculates the average. For example, if the time period delay for batch "BATCH-001" is 180 minutes, the production start and end times become 05:00 to 09:00 after the delay. The system obtains the carbon intensity value sequence for the new time period, such as 305 grams per kilowatt-hour, 315 grams per kilowatt-hour, 420 grams per kilowatt-hour, and 480 grams per kilowatt-hour, and calculates the average as: (305+315+420+480) / 4=380 grams per kilowatt-hour.

[0095] The batch number, original production start and end times, postponed production start and end times, average carbon intensity for the original period, average carbon intensity for the postponed period, and original carbon footprint estimate of each batch are integrated to generate the original dataset of batch production time and carbon intensity.

[0096] The original dataset refers to the set of basic data for batch carbon footprint accounting stored in the form of structured tables. Each record contains a unique batch identifier and associated time-series and carbon intensity information.

[0097] Specifically, the system associates the above fields by batch number and stores them as comma-separated values ​​(CSV) files or database tables to ensure data traceability. For example, the system generates a record for batch "BATCH-001", including the batch number "BATCH-001", the original scheduled production start and end times "02:00~06:00", the postponed production start and end times "05:00~09:00", the average carbon intensity for the original period of 312.5 grams per kilowatt-hour, the average carbon intensity for the postponed period of 380 grams per kilowatt-hour, and the original carbon footprint estimate of 1200 kilograms of CO2 equivalent.

[0098] Therefore, according to the above implementation method, the system can accurately acquire and integrate the raw data of batch production time and carbon intensity, providing a complete and reliable input basis for subsequent dynamic carbon footprint accounting.

[0099] In some embodiments, one or more batches to be analyzed are identified from the original dataset, and the carbon intensity increase level of each batch to be analyzed is calculated using a time-span identification algorithm, including: Read the time delay amount of each batch from the original dataset. If the time delay amount exceeds the preset upper limit, mark the corresponding batch as the batch to be analyzed.

[0100] The time delay refers to the duration of the delay in the start time of batch production caused by the order insertion operation, in minutes; the preset upper limit is a delay duration threshold set based on the intraday variation pattern of grid carbon intensity, used to screen batches with significant carbon footprint impact.

[0101] Specifically, the system uses a data filtering module to traverse each record in the original dataset, extracting the batch number and time delay fields. It then compares the time delay with a preset upper limit. If the time delay is greater than the preset upper limit, a "batch to be analyzed" label is added to the record for that batch. For example, if a metal processing batch has a time delay of 200 minutes and a preset upper limit of 180 minutes, since 200 minutes is greater than 180 minutes, the system marks this batch as a batch to be analyzed, with the batch number "BATCH-001".

[0102] For each batch to be analyzed, the original start and end times of production and the postponed start and end times of production are obtained. Based on the preset carbon intensity zoning threshold, the carbon intensity zoning to which the original start and end times of production belong and the carbon intensity zoning to which the postponed start and end times of production belong are determined through the grid time-sharing carbon intensity database. The carbon intensity zoning includes low carbon intensity zoning and high carbon intensity zoning.

[0103] Among them, the preset carbon intensity zoning threshold refers to the boundary value set based on the statistical quantile of historical carbon intensity data, which is used to divide low carbon intensity zoning and high carbon intensity zoning; carbon intensity zoning refers to the time period category that classifies the carbon intensity value of the power grid according to the range, with low carbon intensity zoning indicating the time period with lower carbon intensity and high carbon intensity zoning indicating the time period with higher carbon intensity.

[0104] Specifically, for each batch to be analyzed, the system reads the original and postponed start and end times of production from the original dataset, queries the grid time-of-use carbon intensity database to obtain the carbon intensity value sequence for the corresponding time period, calculates the arithmetic mean of the carbon intensity values ​​for each time period, and compares the average value with a preset carbon intensity zoning threshold. If the average value is lower than the threshold, it is determined to belong to a low carbon intensity zoning; if the average value is higher than the threshold, it is determined to belong to a high carbon intensity zoning. For example, the original start and end times of production for batch "BATCH-001" were from 02:00 to 06:00 AM, and the postponed start and end times were from 05:00 to 09:00 AM; the preset carbon intensity zoning threshold was 400 grams of carbon dioxide per kilowatt-hour; the average carbon intensity for the original time period was 320 grams per kilowatt-hour (lower than the threshold), so it was determined to be a low carbon intensity zoning; the average carbon intensity for the postponed time period was 520 grams per kilowatt-hour (higher than the threshold), so it was determined to be a high carbon intensity zoning.

[0105] The average carbon intensity within the time period corresponding to the original production start and end times is calculated as the representative value of carbon intensity before the crossing. The average carbon intensity within the time period corresponding to the production start and end times after the delay is calculated as the representative value of carbon intensity after the crossing. The difference between the representative value of carbon intensity after the crossing and the representative value of carbon intensity before the crossing is calculated to obtain the carbon intensity difference of each batch to be analyzed.

[0106] Among them, the mean carbon intensity refers to the arithmetic mean of the carbon intensity values ​​of each hour within the production period, which is used to characterize the average carbon emission level of that period; the carbon intensity difference refers to the result of subtracting the original carbon intensity representative value from the delayed carbon intensity representative value, which is used to quantify the magnitude of carbon intensity change caused by the time period delay.

[0107] Specifically, the system uses a carbon intensity calculation module to retrieve the corresponding carbon intensity numerical sequence from the grid time-sharing carbon intensity database for each batch to be analyzed, based on the original and postponed production start and end times. It then calculates the arithmetic mean of the carbon intensity for the original and postponed periods as representative values ​​of carbon intensity before and after the crossing, and finally calculates the difference between the two.

[0108] For example, the original carbon intensity sequence for batch "BATCH-001" was 320, 310, 305, and 315 grams per kilowatt-hour. The representative carbon intensity before the crossing was (320+310+305+315) / 4=312.5 grams per kilowatt-hour. The carbon intensity sequence after the delay was 305, 315, 420, and 480 grams per kilowatt-hour. The representative carbon intensity after the crossing was (305+315+420+480) / 4=380 grams per kilowatt-hour. The difference in carbon intensity was 380-312.5=67.5 grams per kilowatt-hour.

[0109] The carbon intensity increase level of each batch to be analyzed is determined by comparing each carbon intensity difference with a preset threshold range.

[0110] Among them, the preset threshold range refers to the difference range set based on the industry's carbon emission reduction target, which is used to classify low, medium and high growth rates; the carbon intensity growth rate level refers to the classification label used to characterize the magnitude of the carbon intensity difference.

[0111] Specifically, the system compares the carbon intensity difference with a preset threshold range. If the difference is less than the first-level threshold (e.g., 100 grams per kilowatt-hour), it is determined to be a low-intensity level; if the difference is between the first and second-level thresholds (e.g., 200 grams per kilowatt-hour), it is determined to be a medium-intensity level; and if the difference is greater than the second-level threshold, it is determined to be a high-intensity level. For example, the carbon intensity difference of batch "BATCH-001" is 67.5 grams per kilowatt-hour, and the first-level threshold is 100 grams per kilowatt-hour. Since 67.5 is less than 100, the system determines that the carbon intensity increase level of this batch is low.

[0112] Therefore, according to the above implementation method, the system can automatically identify batches whose carbon intensity changes significantly due to scheduling changes and quantify their impact, providing accurate input for subsequent dynamic carbon footprint accounting.

[0113] In some embodiments, among each carbon intensity increase level, one or more batches to be analyzed that are not less than a preset increase level threshold are selected to constitute a target batch list, including: The carbon intensity increase level of each batch to be analyzed is compared with one or more preset increase level thresholds.

[0114] Among them, the preset increase level threshold refers to the boundary value of the carbon intensity difference range set based on the industry's carbon emission reduction target, which is used to divide the low increase, medium increase and high increase levels. For example, the first level threshold is set to 100 grams per kilowatt-hour, and the second level threshold is set to 200 grams per kilowatt-hour.

[0115] Specifically, the system uses a data comparison module to iterate through the carbon intensity increase levels of each batch to be analyzed, comparing the carbon intensity difference corresponding to each level with preset thresholds to determine the range to which the difference belongs. For example, if the carbon intensity difference of a certain batch is 150 grams per kilowatt-hour, the system compares it with the first-level threshold of 100 grams per kilowatt-hour and the second-level threshold of 200 grams per kilowatt-hour. Since 150 is between 100 and 200, the system determines that the difference belongs to the medium increase level range.

[0116] In response to the carbon intensity increase level reaching or exceeding the preset increase level threshold, the corresponding batch to be analyzed is marked as a batch that meets the screening criteria.

[0117] Among them, the batches that meet the screening criteria are those batches whose carbon intensity increase level reaches or exceeds the preset threshold, that is, those batches whose carbon intensity has increased significantly due to the time delay and need to be prioritized.

[0118] Specifically, after the comparison is completed, the system sets a filtering flag in the batch record for batches whose carbon intensity difference reaches or exceeds the first-level threshold (i.e., medium or high increase level), marking them as meeting the criteria. For example, the carbon intensity difference of batch number "BATCH-001" is 220 grams per kilowatt-hour, exceeding the second-level threshold of 200 grams per kilowatt-hour. The system marks it as a batch that meets the filtering criteria and updates its status in the database.

[0119] All batches marked as meeting the screening criteria are aggregated to generate a target batch list that includes batch identification information and the corresponding carbon intensity increase level.

[0120] The target batch list refers to a collection of all batches that meet the screening criteria. Each record includes a unique batch identifier (such as a number), carbon intensity difference, and amplification level, which are used for subsequent dynamic carbon footprint accounting.

[0121] Specifically, the system collects all batch records marked as meeting the filtering criteria through a data aggregation module, extracts fields such as batch number, carbon intensity difference, and amplification level, and generates a target batch list in the form of a structured data table or list. For example, if the system summarizes 10 batches that meet the filtering criteria, of which 3 are at the high amplification level (difference greater than 200 grams per kilowatt-hour) and 7 are at the medium amplification level (difference between 100 and 200 grams per kilowatt-hour), the generated target batch list includes batch numbers such as "BATCH-001" and "BATCH-002", as well as the corresponding carbon intensity difference (e.g., 220 grams per kilowatt-hour, 150 grams per kilowatt-hour) and amplification level (e.g., high amplification, medium amplification).

[0122] Therefore, according to the above implementation method, the system can automatically identify batches whose carbon intensity changes significantly due to scheduling changes, and construct a target batch list to provide accurate input for subsequent dynamic carbon footprint accounting.

[0123] In some embodiments, the dynamic carbon footprint accounting model is configured with a carbon footprint increment calculation module based on carbon intensity difference and total electricity consumption in production; the steps of identifying the carbon footprint increment caused by time delay in each batch, adding the carbon footprint increment to the original carbon footprint estimate, and outputting the adjusted carbon footprint accounting value for each batch and the total cumulative carbon footprint of the overall production plan include: Obtain the total electricity consumption for each batch from the target batch list.

[0124] The total electricity consumption for production refers to the cumulative total electricity consumption of a batch from the start to the end of production, expressed in kilowatt-hours.

[0125] Specifically, the system reads the total electricity consumption of each batch in the target batch list from the electricity metering module of the production management system through the data interface module. For example, the system obtains that the total electricity consumption of batch "BATCH-001" is 5000 kWh.

[0126] Based on the carbon intensity difference of each batch and the total electricity consumption in production, the carbon footprint increment of each batch is calculated through a dynamic carbon footprint accounting model.

[0127] Among them, carbon footprint increment refers to the additional carbon emissions caused by the postponement of production periods, and the unit is kilograms of carbon dioxide equivalent; the carbon footprint dynamic accounting model refers to the algorithm module that uses mathematical operations to correlate the carbon intensity difference with the total electricity consumption of production to calculate the carbon footprint increment.

[0128] Specifically, the system uses the following formula to calculate carbon footprint increment: Carbon footprint increment (kg CO2 equivalent) = carbon intensity difference (g per kilowatt-hour) × total electricity consumption in production (kWh) / 1000; Dividing by 1000 converts grams to kilograms. For example, the carbon intensity difference of batch "BATCH-001" is 200 grams per kilowatt-hour, and the total electricity consumption for production is 5000 kilowatt-hours. The carbon footprint increment is calculated as: 200 × 5000 / 1000 = 1000 kilograms of carbon dioxide equivalent.

[0129] The carbon footprint increment for each batch is added to the corresponding original carbon footprint estimate to obtain the adjusted carbon footprint accounting value for each batch.

[0130] The adjusted carbon footprint accounting value refers to the revised value after adding the carbon footprint increment to the original carbon footprint estimate, and the unit is kilograms of carbon dioxide equivalent.

[0131] Specifically, the system uses arithmetic addition to add the incremental carbon footprint value to the original estimated carbon footprint value to obtain the final carbon footprint accounting value for the batch. For example, the original estimated carbon footprint value for batch "BATCH-001" was 1200 kg CO2 equivalent, the incremental carbon footprint value was 1000 kg CO2 equivalent, and the adjusted carbon footprint accounting value was: 1200 + 1000 = 2200 kg CO2 equivalent.

[0132] By summing up the adjusted carbon footprint accounting values ​​for all batches, the total cumulative carbon footprint of the overall production plan is obtained.

[0133] The total cumulative carbon footprint of the overall production plan refers to the sum of the carbon footprint values ​​of all batches in the production plan, including the adjusted carbon footprint accounting value of batches in the target batch list and the original carbon footprint estimate of other batches.

[0134] Specifically, the system uses a data aggregation module to cumulatively calculate the carbon footprint values ​​of all batches. Batches in the target batch list use the adjusted carbon footprint calculation value, while batches without time delays use the original carbon footprint estimate. For example, a production plan includes 100 batches. The target batch list contains 15 batches with an adjusted total carbon footprint of 30,000 kg CO2 equivalent, and the remaining 85 batches have an original carbon footprint estimate of 70,000 kg CO2 equivalent. Therefore, the total cumulative carbon footprint is: 30,000 + 70,000 = 100,000 kg CO2 equivalent.

[0135] Therefore, according to the above implementation method, the system can accurately calculate the carbon footprint increase caused by scheduling changes and dynamically adjust the batch carbon footprint accounting value, providing enterprises with accurate carbon emission data support.

[0136] In some embodiments, a batch time slot optimization allocation algorithm is used to prioritize batches in the target batch list according to the carbon intensity increase level, and the production time slots of each batch are reallocated so that the target batch list is preferentially allocated to low carbon intensity zones, generating an optimized production plan, including: The batches in the target batch list are sorted from highest to lowest according to the carbon intensity increase level, generating a batch priority sequence.

[0137] The batch priority sequence refers to an ordered list of batches arranged from largest to smallest based on the carbon intensity increase level value, which is used to determine the order of batch allocation.

[0138] Specifically, the system uses a sorting algorithm (such as quicksort) to traverse the target batch list, extract the carbon intensity increase level value for each batch, and sort the batch numbers in descending order of the value to generate sequence data. For example, if the target batch list contains 10 batches with carbon intensity increase level values ​​of 5, 3, 4, 2, 5, 1, 4, 3, 2, 1 (a larger value indicates a higher increase level), the sorted batch priority sequence would be the batch numbers arranged in the order of level values: 5, 5, 4, 4, 3, 3, 2, 2, 1, 1.

[0139] Obtain available time-period capacity information for low-carbon intensity zones from the grid time-of-use carbon intensity database.

[0140] The available time slot capacity information refers to the total length of continuous time slots within the low-carbon intensity zone that are not occupied by production plans, in hours.

[0141] Specifically, the system accesses the power grid's time-of-use carbon intensity database through a database query interface (such as SQL query, or Structured Query Language, a standard domain-specific language for managing and manipulating relational databases). It retrieves time periods within a specified future period (e.g., 48 hours) where the carbon intensity is below a preset threshold (e.g., 400 grams of carbon dioxide per kilowatt-hour), and calculates the cumulative length of these time periods as available capacity. For example, the system queries and finds three low-carbon time periods within the next 48 hours: 02:00 to 07:00 (5 hours), 01:00 to 06:00 the next day (5 hours), and 02:00 to 08:00 the third day (6 hours), for a total available capacity of 16 hours.

[0142] Based on the batch priority sequence, each batch is assigned to the available time slots of the low-carbon intensity zone in turn.

[0143] The allocation operation refers to matching the start and end times of a batch of production to available time periods, ensuring that the time periods are continuous and do not overlap.

[0144] Specifically, the system iterates through the batch priority sequence. For each batch, it searches the list of available time slots for the first time slot with remaining capacity greater than or equal to the batch's production duration (in hours), assigns the batch to that time slot, and updates the remaining capacity of that time slot. For example, if the first batch in the batch priority sequence has a production duration of 4 hours, and the system matches the first low-carbon time slot (02:00 to 07:00, with 5 hours of remaining capacity), it assigns the batch to 02:00 to 06:00 and updates the remaining capacity of that time slot to 1 hour.

[0145] If the required production time for the current batch exceeds the capacity of the currently available time slot, the current batch will be allocated to the next available low-carbon time slot; if the available time slots in all low-carbon intensity zones are insufficient to accommodate the current batch, the original production time slot for the current batch will remain unchanged.

[0146] The required production period refers to the duration of a batch from the start to the end of production; the current available period capacity refers to the remaining available length of the low-carbon period currently being processed.

[0147] Specifically, the system employs a greedy allocation strategy: when the remaining capacity of the current low-carbon time slot is less than the batch production duration, the system jumps to the next available low-carbon time slot to attempt allocation; if the remaining capacity of all available low-carbon time slots is insufficient, the original production time slot for that batch remains unchanged. For example, if a batch has a production duration of 6 hours, but the remaining capacity of the current low-carbon time slot is only 4 hours, the system tries the next time slot (remaining capacity of 5 hours), which is still insufficient, and continues to try the third time slot (remaining capacity of 6 hours), which is successfully allocated; if another batch has a production duration of 8 hours, and the capacity of all low-carbon time slots is insufficient, the system keeps its original time slot (such as the daytime high-carbon time slot) unchanged.

[0148] Summarize the time slot allocation results for all batches to generate an optimized production plan.

[0149] The optimized production plan refers to a structured data table that records the start and end times of each batch of new production and the corresponding carbon intensity zone, which is used for subsequent carbon footprint accounting.

[0150] Specifically, the system integrates the allocation results of all batches, including batches already allocated to low-carbon time slots and new production start and end times, as well as batch information for batches maintaining their original time slots. The output is a standard format file (such as a CSV file; CSV stands for Comma-Sparated Values, a simple, universal plain text file format used for storing and exchanging tabular data). For example, the system summarizes the allocation results of 20 batches: 15 batches were successfully allocated to low-carbon time slots, and 5 batches maintained their original time slots, generating an optimized production plan file containing fields such as batch number, new start and end times, and the zone to which they belong.

[0151] Therefore, according to the above implementation method, the system can automatically optimize the allocation of batch production time periods, prioritizing the allocation of high carbon intensity increase batches to low carbon time periods, thereby reducing the total cumulative carbon footprint and improving the accuracy and efficiency of carbon footprint management.

[0152] Figure 5 This is a structural block diagram of an electricity carbon footprint accounting system based on product batch traceability according to an embodiment of the present invention.

[0153] like Figure 5 As shown, this electricity carbon footprint accounting system based on product batch traceability includes: The raw dataset acquisition module 210 is configured to acquire raw datasets including batch production time and carbon intensity from a preset production management system and a power grid time-sharing carbon intensity database.

[0154] The intensity increase level calculation module 220 is configured to identify one or more batches to be analyzed from the original dataset, and calculate the carbon intensity increase level of each batch to be analyzed through a time period span identification algorithm. The batch to be analyzed refers to the production batch whose time period delay exceeds the preset upper limit. The carbon intensity increase level is used to characterize the carbon intensity difference range of the corresponding batch to be analyzed.

[0155] The batch list construction module 230 is configured to select one or more batches to be analyzed that are not lower than the preset increase level threshold in each carbon intensity increase level, in order to form a target batch list.

[0156] The carbon footprint accounting value generation module 240 is configured to obtain the total electricity consumption of each batch in the target batch list from the production management system, input the total electricity consumption into the preset carbon footprint dynamic accounting model, identify the carbon footprint increment of each batch due to time delay through the carbon footprint dynamic accounting model, add the carbon footprint increment to the original carbon footprint estimate, and output the adjusted carbon footprint accounting value of each batch and the total cumulative carbon footprint of the overall production plan.

[0157] The original carbon footprint estimate refers to the baseline value of the carbon footprint calculated in advance based on the original production period of the batch and the corresponding carbon intensity.

[0158] The specific functions and examples of each module and submodule of the device in this embodiment of the invention can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0159] According to embodiments of the present invention, the above-described method of the present invention can be applied to an electronic device and a readable storage medium.

[0160] Figure 6A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0162] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0163] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a method for calculating the carbon footprint of electricity based on product batch traceability. For example, in some embodiments, a method for calculating the carbon footprint of electricity based on product batch traceability can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of a method for calculating the carbon footprint of electricity based on product batch traceability described above can be performed. Alternatively, in other embodiments, the computing unit 601 may be configured, by any other suitable means (e.g., by means of firmware), to perform an electricity carbon footprint accounting method based on product batch traceability.

[0164] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0166] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0168] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0169] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0171] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for calculating the carbon footprint of electricity based on product batch traceability, characterized in that, include: The raw dataset, including batch production time and carbon intensity, is obtained from the pre-set production management system and the power grid time-sharing carbon intensity database; One or more batches to be analyzed are identified from the original dataset. The carbon intensity increase level of each batch to be analyzed is calculated by the time period span identification algorithm. The batch to be analyzed refers to the production batch whose time period delay exceeds the preset upper limit. The carbon intensity increase level is used to characterize the carbon intensity difference range of the corresponding batch to be analyzed. Among the various carbon intensity amplification levels, one or more batches to be analyzed that are not lower than the preset amplification level threshold are selected to form a target batch list; The total electricity consumption of each batch in the target batch list is obtained from the production management system. The total electricity consumption is input into the preset carbon footprint dynamic accounting model. The carbon footprint dynamic accounting model is used to identify the carbon footprint increment of each batch due to the time delay. The carbon footprint increment is added to the original carbon footprint estimate. The adjusted carbon footprint accounting value of each batch and the total carbon footprint of the overall production plan are output. The original carbon footprint estimate refers to the baseline carbon footprint value pre-calculated based on the original production period of the batch and the corresponding carbon intensity.

2. The method according to claim 1, characterized in that, After outputting the adjusted carbon footprint calculation values ​​for each batch and the total cumulative carbon footprint of the overall production plan, the method further includes: In response to the total accumulated carbon footprint exceeding the preset optimization limit, the batches in the target batch list are prioritized according to the carbon intensity increase level through a batch time period optimization allocation algorithm, and the production time periods of each batch are reallocated so that the target batch list is preferentially allocated to the low carbon intensity zone, thereby generating an optimized production plan. Extract the new production start and end times and the carbon intensity of each batch in the carbon intensity zone from the optimized production plan. The new production start and end times refer to the actual production start time and actual production end time of each batch in the optimized production plan. Based on the average carbon intensity corresponding to the new production start and end times and the total electricity consumption of each batch, the final carbon footprint of each batch is recalculated, and the carbon footprint synchronous correction calculation under scheduling changes is completed.

3. The method according to claim 1, characterized in that, The process of obtaining the raw dataset, including batch production time and carbon intensity, from the preset production management system and the power grid time-sharing carbon intensity database includes: Read the original start and end times, time delay amount, and original carbon footprint estimate for each batch from the production management system; The carbon intensity values ​​for each batch corresponding to the time period are obtained from the time-sharing carbon intensity database of the power grid; Based on the original start and end times of each batch of production, the average carbon intensity during the original production period is calculated to obtain the average carbon intensity during the original period. Based on the time delay of each batch, calculate the start and end times of production after the delay and the average carbon intensity of the corresponding time period to obtain the average carbon intensity of the delayed time period. The batch number, original production start and end times, delayed production start and end times, average carbon intensity for the original period, average carbon intensity for the delayed period, and original carbon footprint estimate of each batch are integrated to generate the original dataset of production time and carbon intensity for the batch.

4. The method according to claim 1, characterized in that, The process of identifying one or more batches to be analyzed from the original dataset and calculating the carbon intensity increase level of each batch to be analyzed using a time-span identification algorithm includes: Read the time delay amount of each batch from the original dataset. If the time delay amount exceeds the preset upper limit, mark the corresponding batch as a batch to be analyzed. For each batch to be analyzed, the original start and end times of production and the postponed start and end times of production are obtained. Based on the preset carbon intensity zoning threshold, the carbon intensity zoning to which the original start and end times of production belong and the carbon intensity zoning to which the postponed start and end times of production belong are determined through the grid time-sharing carbon intensity database. The carbon intensity zoning includes low carbon intensity zoning and high carbon intensity zoning. The average carbon intensity within the time period corresponding to the original production start and end times is calculated as the representative value of carbon intensity before the crossing. The average carbon intensity within the time period corresponding to the delayed production start and end times is calculated as the representative value of carbon intensity after the crossing. The difference between the representative value of carbon intensity after the crossing and the representative value of carbon intensity before the crossing is calculated to obtain the carbon intensity difference of each batch to be analyzed. The carbon intensity increase level of each batch to be analyzed is determined by comparing the carbon intensity difference with a preset threshold range.

5. The method according to claim 1, characterized in that, The step of selecting one or more batches to be analyzed from each of the carbon intensity increase levels, each batch having an increase level threshold not less than a preset threshold, to form a target batch list includes: The carbon intensity increase level of each batch to be analyzed is compared with one or more preset increase level thresholds; In response to the carbon intensity increase level reaching or exceeding the preset increase level threshold, the corresponding batch to be analyzed is marked as a batch that meets the screening criteria; All batches marked as meeting the screening criteria are aggregated to generate a target batch list that includes batch identification information and the corresponding carbon intensity increase level.

6. The method according to claim 1, characterized in that, The dynamic carbon footprint accounting model is configured with a carbon footprint increment calculation module based on carbon intensity difference and total electricity consumption in production; the steps of identifying the carbon footprint increment caused by time delay in each batch, adding the carbon footprint increment to the original carbon footprint estimate, and outputting the adjusted carbon footprint accounting value for each batch and the total cumulative carbon footprint of the overall production plan include: Obtain the total electricity consumption for each batch from the target batch list; Based on the carbon intensity difference of each batch and the total electricity consumption of production, the carbon footprint increment of each batch is calculated through the dynamic carbon footprint accounting model. The carbon footprint increment for each batch is added to the corresponding original carbon footprint estimate to obtain the adjusted carbon footprint accounting value for each batch. By summing up the adjusted carbon footprint accounting values ​​for all batches, the total cumulative carbon footprint of the overall production plan is obtained.

7. The method according to claim 2, characterized in that, The step involves using a batch time slot optimization allocation algorithm to prioritize batches in the target batch list according to their carbon intensity increase level, and then reallocating the production time slots for each batch. This ensures that the target batch list is preferentially allocated to low-carbon intensity zones, generating an optimized production plan, including: The batches in the target batch list are sorted from high to low according to the carbon intensity increase level to generate a batch priority sequence; Obtain available time-period capacity information for low-carbon intensity zones from the grid time-of-use carbon intensity database; According to the batch priority sequence, each batch is sequentially assigned to the available time slot of the low-carbon intensity zone; If the required production time for the current batch exceeds the capacity of the currently available time slot, the current batch will be allocated to the next available low-carbon time slot; if the available time slots in all low-carbon intensity zones are insufficient to accommodate the current batch, the original production time slot for the current batch will remain unchanged. The time slot allocation results for all batches are aggregated to generate the optimized production plan.

8. A power carbon footprint accounting system based on product batch traceability, characterized in that, include: The raw dataset acquisition module is configured to acquire raw datasets including batch production time and carbon intensity from the preset production management system and the power grid time-sharing carbon intensity database; The intensity increase level calculation module is configured to identify one or more batches to be analyzed from the original dataset, and calculate the carbon intensity increase level of each batch to be analyzed through a time period span identification algorithm. The batch to be analyzed refers to the production batch whose time period delay exceeds a preset upper limit. The carbon intensity increase level is used to characterize the carbon intensity difference range of the corresponding batch to be analyzed. The batch list construction module is configured to select one or more batches to be analyzed that are not lower than a preset increase level threshold from each of the carbon intensity increase levels, in order to form a target batch list; The carbon footprint accounting value generation module is configured to obtain the total electricity consumption of each batch in the target batch list from the production management system, input the total electricity consumption into a preset carbon footprint dynamic accounting model, identify the carbon footprint increment of each batch due to time delay through the carbon footprint dynamic accounting model, add the carbon footprint increment to the original carbon footprint estimate, and output the adjusted carbon footprint accounting value of each batch and the total cumulative carbon footprint of the overall production plan. The original carbon footprint estimate refers to the baseline carbon footprint value pre-calculated based on the original production period of the batch and the corresponding carbon intensity.

9. An electronic device, characterized in that, include: At least one processor; and a memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, Computer instructions are used to cause a computer to perform the method according to any one of claims 1-7.

Images