A method and system for calculating the carbon footprint of stretch wrap film, and a storage medium

Abstract

The application discloses a kind of carbon footprint tracking measurement methods, system and storage medium of stretch wrapping film, it is related to environmental protection method technical field, the method includes: receiving the logistics order number of packing target, and sending the operation start instruction bound with logistics order number to winding machine;Real-time monitoring the mass change data of winding machine supply end film roll, after packing target completes packing operation, determine material consumption absolute mass according to the mass difference of film roll before and after packing;After the display of logistics order number is signed, determine the geographic location and cargo description information of receipt;Determine the waste disposal combination weight corresponding to the geographic location and cargo description information of receipt;Obtain the material production carbon emission factor of wrapping film, according to material consumption absolute mass, material production carbon emission factor and waste disposal combination weight, calculate to obtain the carbon emission data result of packing target.The application can improve the accuracy of carbon footprint tracking measurement of stretch wrapping film.

Description

Technical Field

[0001] This application relates to the field of environmental protection methods and technologies, and in particular to a method, system and storage medium for carbon footprint tracking and calculation of stretch wrap films. Background Technology

[0002] With the increasing severity of global climate change, carbon emission control and carbon neutrality goals have become important issues for countries worldwide. As a crucial component of manufacturing and logistics, industrial packaging is receiving increasing attention for carbon footprint tracking and management. Due to its widespread use in securing goods packaging, accurate calculation of the carbon footprint of stretch wrap is of great significance for enterprises to achieve their carbon reduction targets.

[0003] In related technologies, companies commonly use the Life Cycle Assessment (LCA) method to calculate the carbon footprint of stretch wrap film. This method collects statistical data such as the company's annual electricity consumption and raw material consumption, and combines this data with standard carbon emission factors for calculation. Specifically, fixed calculation parameters are usually set, such as calculating the average energy consumption per unit of product based on the company's total annual output and total energy consumption, and setting the average transportation distance and disposal method of the product based on industry experience.

[0004] However, unlike packaging materials such as courier bags with fixed usage, the unit usage of stretch film in actual packaging operations varies significantly depending on factors such as the volume, weight, shape, and transportation method of the goods. Different batches and specifications of products from the same company may require stretch film of varying thicknesses and numbers, and their transportation distances and storage conditions will also differ. Traditional carbon footprint calculations based on fixed parameters are often inaccurate, making it difficult for companies to accurately assess and optimize their actual carbon emission levels, thus affecting the targeting and effectiveness of carbon reduction measures. Summary of the Invention

[0005] This application provides a method, system, and storage medium for calculating the carbon footprint of stretch wrap film, which improves the accuracy of carbon footprint calculation for stretch wrap film.

[0006] Firstly, this application provides a method for tracking and calculating the carbon footprint of stretch wrap film, applied to a data processing system. The method includes: receiving the tracking number of the packaging target and sending an operation start command bound to the tracking number to the wrapping machine; real-time monitoring of the quality change data of the film roll at the feeding end of the wrapping machine; generating the absolute mass of material consumption of the packaging target based on the difference in film roll mass before and after packaging, after the packaging target completes the packaging operation; determining the geographical location of receipt and the description of goods after the tracking number shows receipt; querying a preset regional waste disposal strategy database to determine the waste disposal combination weight corresponding to the geographical location of receipt and the description of goods; obtaining the carbon emission factor of the stretch wrap film's material production; and calculating the carbon emission data result of the packaging target based on the absolute mass of material consumption, the carbon emission factor of material production, and the waste disposal combination weight.

[0007] In the above embodiments, the data processing system binds a single packaging operation to a logistics tracking number, enabling precise tracking of the consumption of packaging materials for each operation. It also dynamically calculates carbon emissions by combining the actual waste disposal situation at the destination of the goods. This breaks away from the traditional calculation model that relies on industry averages and fixed parameters, ensuring that the carbon footprint accounting for each packaging operation is based on its complete and specific lifecycle data, resulting in higher accounting accuracy.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of real-time monitoring of the mass change data of the film roll at the feeding end of the wrapping machine, and generating the absolute mass of material consumption of the packaging target based on the mass difference of the film roll before and after packaging, specifically includes: upon receiving the operation start command, reading the first value of the weighing sensor installed on the film frame of the wrapping machine as the initial film roll mass; upon detecting that the wrapping machine has stopped operating and the film cutting signal is triggered, reading the second value of the weighing sensor as the final film roll mass; calculating the mass difference between the initial film roll mass and the final film roll mass, and correcting the mass difference in combination with a preset film frame vibration compensation coefficient to obtain the absolute mass of material consumption.

[0009] In the above embodiments, the data processing system effectively eliminates the influence of physical interference during equipment operation on the quality reading by accurately weighing before and after operation and introducing a membrane frame vibration compensation coefficient. By measuring the physical consumption of materials, it provides more reliable and accurate material consumption data compared to estimation based on theoretical parameters.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of querying a preset regional waste disposal strategy database to determine the waste disposal combination weights corresponding to the receipt location and cargo description information specifically includes: obtaining annual waste disposal statistics for the administrative region to which the receipt location belongs; the statistics include the initial landfill ratio, the initial incineration ratio, and the initial recycling ratio; determining the pollution risk level of the stretch film based on the cargo description information, and setting the initial recycling ratio to zero when the pollution risk level is higher than a preset risk threshold; allocating the zeroed initial recycling ratio to the initial landfill ratio and the initial incineration ratio according to their relative weights to obtain the corrected landfill ratio and incineration ratio; matching the corresponding carbon emission factors to the corrected landfill ratio and incineration ratio respectively, and calculating the waste disposal combination weights.

[0011] In the above embodiments, the data processing system makes the carbon emission calculation in the waste disposal stage more realistic. For example, the wrapping film used to package chemicals cannot be recycled. This method can accurately reflect this scenario, thereby dynamically adjusting the disposal weight and improving the precision and accuracy of the carbon footprint life cycle assessment.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after real-time monitoring of the quality change data of the film roll at the feeding end of the wrapping machine, and generating the absolute mass of material consumption of the packaging target based on the difference in film roll mass before and after packaging, the method further includes: reading the set pre-stretch ratio, number of wrapping turns, and film frame lifting stroke data of the wrapping machine during the packaging process; combining the perimeter data of the packaging target, the standard density and standard thickness of the wrapping film, to calculate the theoretical mass of material consumption of the packaging target; calculating the tensile performance deviation rate between the absolute mass of material consumption and the theoretical mass of material consumption; and determining that the pre-stretch roller of the wrapping machine has a slippage abnormality when the tensile performance deviation rate exceeds the preset mechanical performance threshold, and generating an equipment maintenance instruction.

[0013] In the above embodiments, the data processing system can diagnose the decline in equipment efficiency caused by problems such as slippage of the pre-stretch roller in real time, and realize online monitoring of the operating status of packaging equipment. This not only avoids material waste and packaging quality decline caused by equipment abnormalities, but also provides a triggering mechanism for preventive maintenance, ensuring the stability and economy of production.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of calculating the tensile performance deviation rate between the absolute mass of material consumption and the theoretical mass of material consumption, the method further includes: when the tensile performance deviation rate does not exceed a preset mechanical performance threshold, acquiring reverse logistics feedback data associated with the logistics tracking number; the reverse logistics feedback data includes records of spillage, scattering, or damage of goods during transportation; when the carbon emission data result is in a preset high range and the reverse logistics feedback data has zero records within a preset historical period, determining that the current packaging process is in a redundant packaging state; generating a micro-decreasing test instruction, used to reduce the number of wrapping layers or increase the pre-stretch ratio by a preset step size in subsequent packaging operations of similar goods, until a preset reverse logistics feedback critical threshold is detected.

[0015] In the above embodiments, when the data processing system finds that high carbon emissions do not bring about higher cargo safety, the system can determine that it is redundant packaging and start automatic optimization testing, thereby realizing continuous improvement of packaging technology. Under the premise of ensuring cargo safety, it can reduce material consumption and carbon emissions to the optimal level, achieving a balance between economic and environmental benefits.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after obtaining the carbon emission factor of the material production of the stretch film and calculating the carbon emission data result of the packaging target based on the absolute mass of material consumption, the carbon emission factor of the material production, and the combined weight of waste disposal, the method further includes: grouping the carbon emission data result according to geographical region and time period to generate regional time series data; calculating the amount of stretch film used per unit cargo volume and the carbon emission level in each region based on the regional time series data, and calculating the carbon emission standard deviation; and generating adjustment suggestions for stretching process parameters for multiple regions based on the carbon emission standard deviation.

[0017] In the above embodiments, the data processing system aggregates and analyzes isolated single carbon footprint data. By introducing geographical and temporal dimensions, it reveals the differences in carbon emission performance of packaging operations in different regions and at different times from a macro perspective. Calculating the standard deviation of carbon emissions can quantify this difference, providing managers with clear data insights, enabling them to identify key areas for standardization and optimization of packaging processes, and providing data support for the formulation of global energy conservation and emission reduction strategies.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the step of generating adjustment suggestions for wrapping process parameters for multiple regions based on carbon emission standard deviation specifically includes: obtaining the batch number and cargo type characteristics of the wrapping film used in different regions; grouping each region based on the batch number and cargo type characteristics to obtain multiple region comparison groups; selecting a certain region comparison group as the target comparison group; obtaining multiple regions within the target comparison group whose absolute value of carbon emission standard deviation is higher than a preset standard threshold as target optimization groups; determining the region with the lowest carbon emission level within the target optimization group as the optimal region; determining the optimal packaging process parameters corresponding to the packaging operation data in the optimal region; and generating adjustment suggestions for packaging process parameters for multiple regions other than the optimal region within the target comparison group based on the optimal packaging process parameters.

[0019] In the above embodiments, the data processing system compares comparable objects (same film batch, same type of goods) in groups, achieves the process parameters with the lowest carbon emissions under the same conditions, and promotes them to areas with poor performance, forming a data-driven and replicable optimization model that can systematically improve the packaging efficiency and environmental protection level of the entire organization.

[0020] In a second aspect, embodiments of this application provide a data processing system comprising: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein the one or more processors invoke the computer instructions to cause the data processing system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a data processing system, cause the data processing system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a data processing system, cause the data processing system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the data processing system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. By employing a technical solution that links the tracking number of the packaging target to the wrapping machine operation, monitors and generates the absolute mass of material consumption in real time, and determines the dynamic weighting of waste disposal combinations based on the delivery location and cargo description information, the data processing system can establish a full lifecycle data archive for each independent packaging operation, from material consumption to waste disposal. This solution effectively solves the problem of existing technologies using static and fixed parameters such as annual totals and industry averages for carbon footprint accounting, which leads to results that cannot reflect individual differences and have low accuracy. It enables accurate, dynamic, and traceable tracking and calculation of carbon emissions for each packaging target, providing a reliable data foundation for enterprises to conduct refined carbon management and fulfill their environmental responsibilities.

[0026] 2. By employing a technical solution that reads the stretch wrapping machine's set parameters and cargo dimensions to calculate the theoretical material consumption mass, and then compares this calculation with the absolute material consumption mass obtained through weighing to calculate the stretching performance deviation rate, the data processing system possesses the ability to diagnose the mechanical performance of the stretch wrapping machine online. This solution effectively solves the problem in existing technologies where it is difficult to detect hidden equipment faults such as pre-stretch roller slippage in real time, leading to unintentional material waste and inconsistent packaging quality. It enables preventative maintenance and early warning for the stretch wrapping equipment, ensuring maximum utilization of the stretch wrapping film, avoiding additional material consumption and carbon emissions caused by decreased equipment efficiency, and simultaneously guaranteeing the quality stability of packaging operations.

[0027] 3. By employing a technical solution that groups massive amounts of single-event carbon emission data according to geographical regions and time periods to generate regional time-series data, and based on this, calculates the carbon emission level per unit cargo volume and the standard deviation of carbon emissions for each region, the data processing system can quantitatively assess and horizontally compare the packaging carbon performance of different operating units from a macro perspective. This solution effectively solves the problem that existing technologies lack a systematic method to compare and optimize packaging operations distributed across different regions, resulting in regional process parameter optimization remaining at a local and experience-based level, making it difficult to form unified standards and continuous improvement. It achieves data-driven global process optimization, can identify low-performing areas and benchmarks in carbon emissions, and generate targeted adjustment suggestions, promoting the standardization of overall packaging levels and continuous carbon emission reduction for enterprises. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a method for calculating the carbon footprint of stretch wrap film in an embodiment of this application.

[0029] Figure 2This is another flowchart illustrating the carbon footprint tracking and calculation method for stretch wrap film in this application embodiment;

[0030] Figure 3 This is a schematic diagram of the physical device structure of a data processing system in an embodiment of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions "a," "an," "the," "the," and "this" are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the terms and / or as used in this application refer to any or all possible combinations that include one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0033] In the specific implementation scenario of this application, several technical terms are involved. The core of this method lies in establishing a unique carbon footprint profile for each packaging target using stretch wrap (e.g., a pallet filled with goods). The starting point is the tracking number, which serves as a bridge connecting the physical world (goods) and the digital world (data). This tracking number is bound when the wrapping machine begins operation, ensuring that all subsequent data is attributed to this goods. The calculation of the carbon footprint mainly includes two stages: the production and use stage and the disposal stage. For the production and use stage, the absolute mass of material consumed, obtained by real-time weighing of the film roll, is a key input, representing the actual amount of raw materials consumed to package the goods. For the disposal stage, since the final disposal method of the stretch wrap (landfill, incineration, or recycling) has a significant impact on carbon emissions, and policies and facilities vary across regions, the concept of a waste disposal combination weight is introduced. This weight is not a fixed value but is dynamically generated based on the final delivery location of the tracking number and cargo description information that may affect the recycling value (e.g., whether it is a hazardous material). By multiplying the absolute mass of material consumption by the carbon emission factors corresponding to the material production carbon emission factor and the combined weight of waste disposal, the carbon emissions at each stage can be obtained. The sum of these values ​​constitutes the total carbon footprint. Furthermore, the solution includes an optimized closed-loop system. This system monitors equipment status by calculating the tensile performance deviation rate and analyzes reverse logistics feedback data (such as damage records) to determine if over-packaging exists, thereby achieving continuous cost reduction, efficiency improvement, and carbon reduction.

[0034] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a method for calculating the carbon footprint of stretch wrap film in an embodiment of this application.

[0035] S101. Receive the tracking number of the packaging target and send the operation start command bound to the tracking number to the wrapping machine.

[0036] In this context, the packaging target refers to a unit of goods requiring stretch wrapping, such as one or more cartons stacked on a pallet. The tracking number represents a unique code generated by the logistics service provider that identifies the packaging target throughout the entire transportation and distribution process. The stretch wrapping machine refers to an automated or semi-automated device that performs the stretch wrapping operation. The operation start command is a data packet generated by the data processing system and sent to the stretch wrapping machine control system to trigger the packaging process, along with the associated tracking number information.

[0037] Specifically, before the packaging operation begins, operators or automated equipment (such as barcode scanners or RFID readers) typically obtain the tracking number of the goods to be packaged at the wrapping machine station. Upon receiving this tracking number, the data processing system first creates a new job session in its internal database, using the tracking number as the primary key. Then, the system sends a job start command to the programmable logic controller (PLC) of the wrapping machine associated with that station or its host computer. This command contains a unique identifier for this job, which is associated with the received tracking number in the data processing system's database. After receiving the command, the wrapping machine begins executing the preset packaging program.

[0038] In some embodiments, the instruction sending and binding for this step can be implemented in several ways: Optionally, the data processing system is deeply integrated with the warehouse management system (WMS). When the WMS schedules an outbound pallet to a designated wrapping machine station, it automatically pushes the pallet's tracking number to the data processing system, which then generates an instruction and sends it to the wrapping machine. Optionally, the wrapping machine is equipped with a human-machine interface (HMI). The operator manually enters or scans the tracking number on the HMI, which uploads the number to the data processing system for recording and binding. After verification, the HMI's internal logic triggers the wrapping machine to start. It is understood that image recognition technology can also be used to directly extract the tracking number from the electronic waybill of the package; this is not limited here.

[0039] In some embodiments, network latency or interruption may cause the job start command to fail to be sent or the wrapping machine to fail to receive it correctly. To address this, the data processing system implements a timeout retransmission mechanism. After sending the command, the system starts a timer and waits for an acknowledgment signal from the wrapping machine. If no acknowledgment is received within a preset time (e.g., 5 seconds), the system will resend the command. If the retransmission fails after a preset number of attempts (e.g., 3 times), the system will generate an alarm, notifying on-site management personnel to check the equipment's network connection or the wrapping machine's status to ensure the reliability of data binding.

[0040] S102. Real-time monitoring of the quality change data of the film roll at the feeding end of the wrapping machine. After the packaging target completes the packaging operation, the absolute mass of material consumption of the packaging target is generated based on the quality difference of the film roll before and after packaging.

[0041] The feeding end film roll refers to the entire roll of film installed on the stretch wrapping machine for supplying stretch wrap film. Mass change data refers to the real-time weight reading sequence of the film roll collected by weighing equipment. Absolute mass of material consumed refers to the actual mass of film consumed from the film roll in a single packaging operation; this is the core foundational data for subsequent carbon emission calculations.

[0042] Specifically, while executing step S101 to send the job start command, the data processing system triggers the weighing sensor installed on the film holder (the component that carries the film roll) of the wrapping machine to take a reading and records this value as the initial film roll mass associated with the current logistics order number. After the wrapping machine completes the entire packaging process and performs the film cutting and clamping actions, the system receives a job completion signal. When this signal is triggered, the system again instructs the weighing sensor to take a reading and records this value as the final film roll mass. Finally, the system calculates the difference between the initial film roll mass and the final film roll mass to obtain a preliminary mass consumption value. After necessary corrections (such as vibration compensation), the final absolute mass of material consumption is generated.

[0043] In some embodiments, the replacement of a new membrane roll may occur precisely during a packaging operation. The data processing system is designed with corresponding logic to address this. The system monitors for a sudden increase in the membrane roll's mass, indicating that a new membrane roll has been replaced. When this event is detected, the system adds the mass consumption before the replacement to the current operation, then reads the initial mass of the new membrane roll and uses this as a baseline to continue calculating subsequent consumption until the operation is completed. Ultimately, the absolute mass of material consumed in this operation is the sum of the consumption before and after the replacement, ensuring the continuity and accuracy of metering.

[0044] S103. After the tracking number shows that the goods have been signed for, confirm the geographical location of the delivery and the description of the goods.

[0045] "Signed for receipt" refers to the logistics status being updated to "delivered" or "signed for," indicating that the goods have reached their final destination. "Signed for receipt location" refers to the detailed address information where the goods were finally received, usually accurate to the city or administrative district. "Goods description information" refers to the text or coded information associated with the tracking number that describes the contents, nature, or category of the items in the package, typically sourced from the order system or WMS.

[0046] Specifically, the data processing system periodically (e.g., once per hour) or through an API callback mechanism, initiates query requests to the logistics information service platform. During the query, the tracking number recorded in step S101 is used as an index. When the query result returns a "delivered" status, the data processing system parses the returned data, extracts detailed delivery address information, and at least parses it to the city level. Simultaneously, the system retrieves the corresponding goods description information for that order from its internal database or related business systems (such as ERP or OMS) based on the tracking number.

[0047] In some embodiments, information acquisition for this step can be achieved in several ways: Optionally, the data processing system can interface with the query APIs of multiple mainstream logistics companies, automatically selecting the corresponding query channel based on the format of the tracking number to achieve compatibility with different carriers. Optionally, a self-built logistics tracking platform can act as middleware, aggregating logistics information for all outbound shipments. The data processing system only needs to interact with this internal platform, simplifying the management of external interfaces. It is understood that cargo description information can also be directly obtained by scanning the product barcode and bound to the tracking number during packaging; this is not limited here.

[0048] In some embodiments, the geographical location information for receipts may be inaccurate or inconsistently formatted, such as the address containing aliases or abbreviations. To address this, the data processing system integrates an address standardization and geocoding service. Upon obtaining the original address string, the system invokes this service to clean and format it into a standard administrative division structure (e.g., country-province-city-district), and obtains its corresponding geographic coordinates. This processing ensures accurate matching to the corresponding entries in the regional waste disposal strategy database, improving matching accuracy.

[0049] It should be noted that this address standardization and geocoding service is a multi-stage natural language processing and spatial data matching process. First, upon receiving the raw, unstructured address string, the system will activate the word segmentation and entity recognition module. Using a pre-built, vast dictionary containing the names of provinces, cities, districts, streets, and towns across the country, the system will segment the address string and identify possible administrative division entities. Secondly, the system enters the alias and fuzzy matching module, which maintains a thesaurus (e.g., "city of xx", "location x" corresponds to city A) and a common misspelling database. For words that cannot be directly matched, the system uses string similarity algorithms (such as edit distance or Jaro-Winkler distance) to perform fuzzy matching with the standard address database to find the most likely candidate address. Next, in the hierarchical verification and disambiguation module, the system uses the tree-like hierarchical relationship of administrative divisions for verification. For example, if "district cd" is identified, the system will check if other parts of the address contain information such as "city C" or "city D" to eliminate ambiguity; if there is no clear superior information, it may combine historical data or other order information (such as the place of shipment) for inference. Finally, once a unique, standardized address accurate to the district / county level is determined, the system calls the geocoding module to query a built-in geographic information database (which stores the polygonal boundaries of administrative divisions and their center point coordinates) and returns the center latitude and longitude coordinates of the area. This series of rigorous processes ensures that, regardless of the variations in the original address format, the system can output an accurate and consistent geographic location identifier to the greatest extent possible, laying the foundation for precise matching of waste disposal strategies in the future.

[0050] S104. Query the preset regional waste disposal strategy database to determine the waste disposal combination weights corresponding to the signed geographical location and cargo description information.

[0051] The regional waste disposal strategy database is a pre-built database that stores official statistical proportions of solid waste (especially plastics) disposal methods in different administrative regions. The waste disposal combination weight refers to a probability distribution calculated based on specific conditions, used to describe the disposal of a unit of waste stretch film through landfill, incineration, or recycling; the sum of the weights of the three methods is 1.

[0052] Specifically, after determining the receipt location (e.g., District A1, City A) and cargo description information (e.g., industrial lubricating oil) in step S103, the data processing system first uses City A as a keyword to query the latest waste disposal statistics for that city in the regional waste disposal strategy database, obtaining an initial proportion distribution, such as: initial landfill proportion 30%, initial incineration proportion 50%, and initial recycling proportion 20%. Next, the system analyzes the cargo description information, industrial lubricating oil, and determines it to be a high-pollution-risk cargo based on the built-in risk assessment rule base. Since the contaminated stretch film cannot enter the recycling channel, the system sets the initial recycling proportion of 20% to 0. Then, this 20% proportion is allocated according to the relative weights (3:5) of initial landfill (30%) and initial incineration (50%). The final revised landfill ratio is 30% + 20% * (30 / (30 + 50)) = 37.5%, and the revised incineration ratio is 50% + 20% * (50 / (30 + 50)) = 62.5%. Therefore, the waste disposal combination weights for this packaged target are: landfill 0.375, incineration 0.625, and recycling 0.

[0053] In some embodiments, the weight determination for this step can be achieved in several ways: Optionally, the data source for the strategy library can be the annual environmental status bulletin issued by national or local environmental protection departments, which the system automatically captures and updates periodically. Optionally, the pollution risk level of the cargo description information can be determined by a machine learning model, which is trained by learning from a large amount of cargo name-pollution level labeling data and can automatically classify new cargo descriptions. Understandably, the logic of weight allocation can also be more complex, for example, considering the differences between different incineration technologies (such as whether or not energy recovery is included), which is not limited here.

[0054] In some embodiments, there may be situations where the delivery location has no exact match in the policy library, such as a newly developed area. In such cases, the data processing system employs an upward-tracing matching strategy. If there is no data for District A1 in City A, the system will attempt to match data from its superior administrative unit, City A. If City A also has no data, it will continue tracing upwards to the average data for the larger region, or use the national average data as the default value. This strategy ensures that even with incomplete data, the system can still provide the most reasonable estimated weight, guaranteeing the integrity of the calculation process.

[0055] S105. Obtain the carbon emission factor of the material production of the stretch film. Calculate the carbon emission data results of the packaging target based on the absolute mass of material consumption, the carbon emission factor of the material production, and the combined weight of waste disposal.

[0056] The carbon emission factor in material production refers to the greenhouse gas emissions generated throughout the entire process of producing one unit mass (e.g., 1 kg) of stretch wrap film (typically LLDPE) from raw material mining to finished product delivery. This value is usually derived from an authoritative Life Cycle Assessment (LCA) database. The carbon emission data result refers to the calculated total greenhouse gas emissions generated throughout the entire life cycle of the stretch wrap film consumed to package the desired packaging target.

[0057] Specifically, the data processing system first retrieves the carbon emission factor of the material production corresponding to the currently used stretch film (e.g., 2.5 kg CO2e / kg, where CO2e is carbon dioxide equivalent, a unified unit used to measure the impact of different greenhouse gases on global warming) and the carbon emission factors of different waste disposal methods from its configuration library (e.g., landfill 0.05 kg CO2e / kg, incineration 0.8 kg CO2e / kg, recycling -1.5 kg CO2e / kg, negative values ​​indicate emission reduction). Assuming the absolute mass of material consumption obtained in step S102 is 0.5 kg, and the waste disposal combination weights obtained in step S104 are {landfill: 0.375, incineration: 0.625, recycling: 0}, the carbon emission data result for this packaging target is calculated as follows: Production stage carbon emission = 0.5 kg * 2.5 kg CO2e / kg = 1.25 kg CO2e.

[0058] Carbon emissions during the disposal phase = 0.5 kg * (0.375 * 0.05 kg CO2e / kg + 0.625 * 0.8 kg CO2e / kg)

[0059] +0*-1.5kgCO2e / kg)=0.259kgCO2e.

[0060] Total carbon emissions = 1.25 + 0.259 = 1.509 kg CO2e. This result will ultimately be stored along with the tracking number.

[0061] In some embodiments, inconsistencies may arise in carbon emission factor values ​​from different sources. To address this, the data processing system establishes a multi-source factor management module and sets priority rules. For example, it prioritizes product-specific factors calculated by a third-party LCA (Limited Cost Analysis) agency commissioned by the enterprise, followed by factors from official databases published by the state or industry, and finally factors from internationally recognized databases. During each calculation, the system records the source of the factors used, ensuring the transparency and auditability of the calculation process.

[0062] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the carbon footprint tracking and calculation method for stretch wrap film in this application embodiment.

[0063] S201. Receive the tracking number of the packaging target and send the operation start command bound to the tracking number to the wrapping machine.

[0064] Refer to step S101, which will not be repeated here.

[0065] S202. Real-time monitoring of the quality change data of the film roll at the feeding end of the wrapping machine. After the packaging target completes the packaging operation, the absolute mass of material consumption of the packaging target is generated based on the quality difference of the film roll before and after packaging.

[0066] Refer to step S102, which will not be repeated here.

[0067] In some embodiments, the data processing system can achieve a more accurate calculation of the absolute mass of material consumption. Specifically, when the data processing system receives a work start command, it reads the first value of the weighing sensor installed on the film frame of the winding machine as the initial film roll mass; when it detects that the winding machine has stopped operating and the film cutting signal is triggered, it reads the second value of the weighing sensor as the final film roll mass; it calculates the mass difference between the initial film roll mass and the final film roll mass, and corrects the mass difference by combining it with a preset film frame vibration compensation coefficient to obtain the absolute mass of material consumption.

[0068] The load cell is a measuring element that converts mass signals into electrical signals, typically mounted below the film carrier or on the core support structure. The film cutting signal is a digital signal issued by the wrapping machine's PLC after packaging is completed and the film is cut, marking the end of material consumption. The film carrier vibration compensation coefficient is a preset or adaptively learned value used to correct for weighing reading fluctuations caused by the rapid rotation and lifting of the wrapping machine.

[0069] Specifically, after the data processing system sends a start command to the wrapping machine, the system waits for a brief stabilization period (e.g., 1 second) to ensure the film carriage is relatively stationary. Then, it sends a read command to the weighing sensor to acquire and store the first value as the initial film roll mass. Throughout the packaging process, the system can continuously collect mass data, but only for monitoring. When the wrapping machine controller issues a film cutting signal and the film carriage has stopped lifting, the system waits for another stabilization period before reading the second value as the final film roll mass. At this point, the system extracts the vibration characteristics (such as amplitude and frequency) from the sensor data and calculates a dynamic compensation value based on a preset vibration-mass error model. Alternatively, it directly uses a fixed film carriage vibration compensation coefficient, calibrated experimentally, to correct the original difference between the initial and final mass, thus obtaining the final absolute mass of material consumption.

[0070] In some embodiments, the correction process can be implemented in several ways: Optionally, by installing an accelerometer on the winding machine to measure the vibration acceleration of the film frame in real time, the data processing system dynamically calculates the impact of the inertial force generated by the vibration on the weighing reading according to Newton's second law (F=ma) and performs real-time compensation. This method offers higher accuracy. Optionally, during the equipment installation and commissioning phase, a series of no-load and load operation tests are conducted, and the weighing error under different operating parameters is recorded. A compensation function or lookup table is fitted, and the data processing system calls the corresponding compensation value according to the operating parameters in subsequent calculations. It is understood that digital filtering techniques (such as Kalman filtering) can also be used to process the original weighing signal to smooth out high-frequency vibration noise; this is not limited here.

[0071] It should be noted that the determination of the film carriage vibration compensation coefficient is not a fixed, single value, but a dynamic compensation model closely related to the operating status of the wrapping machine. The data processing system enters a compensation model learning mode during the initial equipment debugging or periodic calibration phase. In this mode, the system instructs the wrapping machine to execute various typical packaging procedures without consuming film (e.g., using a fixed counterweight instead of the film roll), covering different combinations of turntable speeds, film carriage lifting speeds, and accelerations. Simultaneously, the system frequently collects weighing sensor readings and accelerometer data mounted on the film carriage. By analyzing this data, the system can establish a mapping model. This model takes real-time film carriage vibration acceleration, operating speed, and other state parameters as input and outputs a corresponding mass reading error value. For example, the model can detect that when the film carriage is lifted upwards with a specific acceleration, a downward inertial force is generated, causing the weighing reading to be instantaneously and quantifiably larger. In actual operation, the data processing system monitors these operating parameters in real time and calls the compensation model to calculate the dynamic compensation value at each moment, correcting the original weighing data in real time. Thus, when calculating the mass difference before and after packaging, it can effectively filter out physical interferences such as inertial force and centrifugal force introduced by the mechanical movement of the equipment itself, ensuring that the final absolute mass of material consumption has higher accuracy and reliability.

[0072] In some embodiments, the load cell itself may experience zero-point drift or sensitivity changes, leading to long-term inaccurate measurements. To address this, the data processing system incorporates an automatic calibration and diagnostic procedure. The system utilizes the opportunity of each membrane roll replacement, knowing the standard mass of the new roll. Once the operator confirms the replacement is complete, the system reads the load cell reading and compares it to the standard mass. If the error exceeds a preset range, the system prompts for sensor calibration. Simultaneously, the system records the readings for each empty membrane frame (without a roll), monitoring its zero-point stability. If persistent drift is detected, an alert is generated for sensor maintenance or replacement.

[0073] S203. Read the set pre-stretch ratio, number of wrapping turns, and film carriage lifting stroke data of the wrapping machine during the packaging process. Combine the perimeter data of the packaging target, the standard density and standard thickness of the wrapping film, and calculate the theoretical material consumption mass of the packaging target.

[0074] The pre-stretch ratio is a key parameter of the wrapping machine, representing the ratio by which the film is stretched before leaving the film carrier. The number of wraps and the film carrier lifting stroke are parameters that define the packaging formulation, controlling the lateral and longitudinal coverage of the film, respectively. The theoretical material consumption mass is the mass of film that should be consumed under ideal conditions, calculated based on these settings and the cargo dimensions.

[0075] Specifically, during or after the packaging operation, the data processing system reads the actual process parameters executed in that operation from the controller of the wrapping machine. For example, the pre-stretch ratio is 250%, the number of top wraps is 3, the number of bottom wraps is 3, and the film carriage lifting stroke is 1.5 meters. Simultaneously, the system obtains the perimeter data of the packaging target (e.g., 4.8 meters) through a dimension detection device associated with the wrapping machine (such as a 3D camera or laser scanner); or through relevant dimension parameters input by the operator after measurement. Combined with the pre-set basic information of the wrapping film in the system (standard density 0.918 g / cm³, standard thickness 23 μm), the system begins calculations. For example, the film length covered by the lifting stroke = (1.5m / (film width * (1 - overlap ratio))) * 4.8m, and the film length of the top and bottom reinforcing rings = (3 + 3) * 4.8m. These lengths are summed to obtain the total stretched length, then divided by (1 + pre-stretch ratio) to obtain the film length before stretching. Finally, multiplying by the film's cross-sectional area and density yields the theoretical material consumption mass.

[0076] It should be noted that, to ensure rigor when calculating the theoretical material consumption mass, in some embodiments, the data processing system can first determine the effective surface area covered by the stretch film. For an approximately cubic pallet, its perimeter (P) is known. The stretch film does not cover vertically during the lifting process, but rises or falls in a spiral shape. The spiral spacing is determined by the lifting speed of the film carriage and the rotation speed of the turntable, which directly affects the overlap ratio (R_o) between the films. This overlap ratio is usually a preset process parameter. Therefore, the number of turntable rotations (N_rev) required for a complete lifting stroke (H) can be calculated as N_rev=H / (W_film*(1-R_o)), where W_film is the standard width of the stretch film. At this time, the film length (L_spiral) consumed by the spiral winding part after stretching is L_spiral=N_rev*P. Adding the top reinforcing rotations (N_top) and bottom reinforcing rotations (N_bottom), the length consumed after stretching is L_reinforce=(N_top+N_bottom)*P. Therefore, the total length of the stretched film is:

[0077] L_stretched = L_spiral + L_reinforce. Based on the prestretch ratio (Ratio_prestretch), the original (unstretched) length of the membrane consumed can be calculated.

[0078] L_original = L_stretched / (1 + Ratio_prestretch). Finally, combining the standard thickness (T_film) and standard density (D_film) of the stretch film, the theoretical material consumption mass is:

[0079] M_theoretical = L_original * W_film * T_film * D_film. This calculation process ensures that the theoretical value accurately reflects the set process parameters, providing a reliable benchmark for subsequent performance deviation rate calculations.

[0080] S204. Calculate the tensile performance deviation rate between the absolute mass of material consumption and the theoretical mass of material consumption.

[0081] The stretching performance deviation rate is an indicator used to quantify the difference between the actual amount of film used and the theoretical amount of film used. Its calculation formula is: (absolute mass of material consumed - theoretical mass of material consumed) / theoretical mass of material consumed * 100%. This deviation rate directly reflects whether the pre-stretching system of the winding machine is working as expected.

[0082] Specifically, after obtaining the absolute mass of material consumption (e.g., 0.5 kg) generated in step S202 and the theoretical mass of material consumption (e.g., 0.45 kg) calculated in step S203, the data processing system directly substitutes them into the formula for calculation. Tensile performance deviation rate = (0.5 - 0.45) / 0.45 * 100% ≈ 11.1%. This positive value indicates that the actual amount of film used exceeds the theoretical value. Conversely, if the absolute mass is less than the theoretical mass, a negative deviation will occur, which may indicate abnormal stretching or data acquisition errors. This deviation rate result will be recorded along with data such as the logistics tracking number, absolute consumption, and theoretical consumption.

[0083] In some embodiments, the theoretical calculation model itself may contain systematic errors, causing the deviation rate to consistently deviate from zero over a long period. To address this, the data processing system introduces a self-calibration mechanism. The system continuously monitors the tensile performance deviation rate of a specific packaging formulation and calculates the average deviation rate over a period of time (e.g., one week) during which the equipment is confirmed to be operating normally. If this average deviates significantly from zero (e.g., consistently within 5%), the system uses this average as a model correction coefficient to compensate for it in subsequent theoretical quality calculations, thereby bringing the baseline of the deviation rate back to near zero and improving the sensitivity of anomaly detection.

[0084] On the one hand, in some embodiments, after diagnosing that the equipment is operating normally, the data processing system will assess the rationality of the packaging process and make adaptive optimizations. That is, when the stretching performance deviation rate does not exceed the preset mechanical performance threshold, the data processing system will acquire reverse logistics feedback data associated with the logistics tracking number. The reverse logistics feedback data includes records of goods being dumped, scattered, or damaged during transportation. When the carbon emission data result is in a preset high range and the reverse logistics feedback data has zero records in a preset historical period, the current packaging process is determined to be a redundant packaging state. A micro-decreasing test instruction is generated to reduce the number of wrapping layers or increase the pre-stretch ratio by a preset step size in subsequent packaging operations of similar goods until the preset reverse logistics feedback critical threshold is detected.

[0085] Reverse logistics feedback data refers to records of returns and claims arising from quality issues (including damage caused by damaged packaging) after goods are delivered. This data is typically recorded in the after-sales service system or quality management system. Redundant packaging refers to packaging strength far exceeding the protection level required for actual transportation, resulting in unnecessary material waste and carbon emissions. A micro-reduction test instruction is a guide for A / B testing, aiming to find the optimal balance between protective effect and material consumption by gradually reducing packaging strength.

[0086] Specifically, the data processing system confirms that the stretching efficiency deviation rate of a certain operation is within the normal range (e.g., 5%), indicating that the equipment is working properly. The system calculates its carbon emission result as 2.0 kg CO2e, which falls into the high range defined by the system based on historical data (e.g., higher than 20% of the average carbon emission of similar goods). At the same time, the system queries the customer associated with this logistics tracking number and finds that there are no records of cargo damage due to packaging issues for similar goods shipped to this customer in the past six months. Based on the two conditions of high carbon emissions and zero cargo damage, the system determines that the current packaging process for this type of cargo (e.g., 5 wraps at the top and 5 wraps at the bottom) may be redundant. Therefore, the system generates a small reduction test instruction, suggesting that in the next packaging of similar goods, the number of wraps be reduced to 4 wraps at the top and 4 wraps at the bottom, and continue this test until a very low probability of cargo damage begins to appear (e.g., the cargo damage rate reaches the preset critical threshold of 0.1%). The parameters at this point are considered the optimized best process.

[0087] In some embodiments, redundancy judgment and testing in this step can be implemented in several ways: Optionally, the judgment criteria for redundant packaging can be combined with the bumpiness data of the transportation route. If a route is very smooth, there may be room for optimization even if carbon emissions are low. Optionally, the micro-reduction test can be executed automatically. After generating the instruction, the system directly modifies the packaging formula sent to the wrapping machine and automatically tracks the damage feedback of subsequent batches, forming a fully automated PDCA (Plan-Do-Check-Act) optimization cycle. It is understood that, in addition to reducing the number of wrapping layers, the test instruction can also be to increase the pre-stretch ratio or replace it with a thinner but equally performing film; this is not limited here.

[0088] In some embodiments, there may be a delay in the feedback of cargo damage records, which could lead to a situation where a large number of goods have already been shipped using risky packaging parameters during the testing process. To address this, the data processing system employs a more cautious small-batch group testing strategy. The system does not immediately apply the new parameters to all similar goods. Instead, it selects a small portion (e.g., 5%) of the goods as a test group, packaging them using the new parameters, while the remaining 95% of the goods continue to use the old parameters. The system closely monitors the cargo damage feedback for the test group. Only after the test group has completed a full logistics cycle and is confirmed to be safe will the system gradually expand the application of the new parameters, thereby minimizing potential risks.

[0089] On the other hand, when the data processing system diagnoses an abnormality in equipment operation, that is:

[0090] S205. When the tensile performance deviation rate exceeds the preset mechanical performance threshold, it is determined that the pre-stretching roller of the winding machine has a slippage abnormality, and an equipment maintenance instruction is generated.

[0091] The mechanical performance threshold is an acceptable fluctuation range, such as ±10%, set for the stretch performance deviation rate. Exceeding this range is considered abnormal. Pre-stretch roller slippage is a common fault in winding machines, indicating that two rollers traveling at different speeds fail to effectively stretch the film, resulting in an actual stretch ratio lower than the set value. Equipment maintenance instructions are automatically generated notifications from the system, requiring maintenance personnel to inspect and repair designated equipment.

[0092] Specifically, the data processing system compares the stretching performance deviation rate (e.g., 11.1%) calculated in step S204 with a preset mechanical performance threshold (e.g., +10%). Since 11.1% > 10%, the system determines an anomaly has occurred. Because an excessively large positive deviation means that the actual film usage is significantly higher than the theoretical value, which is consistent with insufficient pre-stretching (i.e., the actual stretching ratio is less than the set value), the system infers that the most likely cause is slippage of the pre-stretching roller. The system automatically creates an equipment maintenance work order, which includes the equipment number, the time of the anomaly, the deviation rate value, and the inferred cause of the fault (pre-stretching roller slippage), and sends this instruction to the equipment maintenance team via the company's internal work order system, email, or SMS.

[0093] In some embodiments, multiple faults may lead to similar abnormal deviation rates. To address this, the data processing system establishes a multi-dimensional fault diagnosis model. In addition to the stretching performance deviation rate, the system simultaneously analyzes other sensor data from the winding machine, such as motor current and roller speed encoder readings. For example, if an abnormal increase in the pre-stretching motor current is detected when the deviation rate exceeds the limit, it can be further confirmed that the slippage is due to insufficient friction caused by roller surface wear or contamination. This multi-information fusion diagnostic method improves the accuracy of fault diagnosis.

[0094] It should be noted that this multi-dimensional fault diagnosis model is a supervised learning-based classification model, such as Gradient Boosting Decision Tree (GBDT) or Random Forest. Training this model requires a large amount of historical data with accurate fault labels. The data processing system continuously records multi-dimensional time-series data related to equipment status for each packing operation, including but not limited to stretching efficiency deviation rate, real-time current and speed of the pre-stretching motor and turntable motor, load of the membrane frame lifting motor, raw reading sequences of weighing sensors, vibration spectrum characteristics measured by accelerometers, and equipment temperature read from the PLC. These data constitute the model's input features (X). Corresponding to these data are maintenance work order records in the enterprise's equipment maintenance management system (such as CMMS), which provide accurate fault labels (Y), such as pre-stretching roller wear, insufficient cleaning of rubber rollers leading to slippage, poor lubrication of lifting chains, and encoder signal loss. Each sample in the training dataset is a feature data vector of a packing operation and its corresponding true fault label (or no fault label). The goal of training is to minimize the model's classification error rate, which is achieved by adjusting the model's internal parameters (such as the structure and weights of the decision tree) to make its predicted fault types as consistent as possible with actual maintenance records. The evaluation criterion for training is usually the macro-average F1 score under cross-validation to ensure that the model has robust recognition capabilities across various fault types, including rare faults.

[0095] The model can be understood as a complex expert decision-making system composed of hundreds or thousands of decision trees. Each tree makes a series of yes / no judgments based on different combinations of input features, ultimately providing a preliminary fault diagnosis. For example, a tree might learn that if the stretching performance deviation rate is greater than 10% and the pre-stretching motor current is below 80% of the normal threshold at the set stretching ratio, there is a high probability of roller slippage. By integrating the judgments of all trees (e.g., through weighted voting), the model can synthesize multiple information sources and output a list of probability distributions for all known fault types, such as: {pre-stretching roller slippage: 0.92, normal motor load: 0.05, other faults: 0.03}.

[0096] In actual production, the data processing system collects the aforementioned input feature data in real time for each packaging operation and feeds it into a pre-trained fault diagnosis model. The model outputs a fault probability distribution instantly. The system sets a confidence threshold (e.g., 0.85). If the probability value of the most probable fault type exceeds this threshold, the system determines that the fault has occurred and automatically generates equipment maintenance instructions containing specific fault diagnosis results. This data-driven diagnostic approach, compared to rule-based judgments relying solely on a single deviation rate threshold, can identify potential faults earlier and more accurately, providing more specific maintenance guidance and significantly improving the predictability and efficiency of equipment maintenance.

[0097] S206. After the tracking number shows that the goods have been signed for, confirm the geographical location of the delivery and the description of the goods.

[0098] Refer to step S103, which will not be repeated here.

[0099] S207. Query the preset regional waste disposal strategy database to determine the waste disposal combination weights corresponding to the receipt location and cargo description information.

[0100] Refer to step S104, which will not be repeated here.

[0101] In some embodiments, the data processing system optimizes the process of determining the weights of the waste disposal portfolio. Specifically, the data processing system acquires annual waste disposal statistics for the administrative region where the delivery location is located. These statistics include the initial landfill ratio, the initial incineration ratio, and the initial recycling ratio. The system determines the pollution risk level of the stretch film based on the cargo description information, and resets the initial recycling ratio to zero when the pollution risk level is higher than a preset risk threshold. The reset initial recycling ratio is then allocated to the initial landfill ratio and the initial incineration ratio according to their relative weights, resulting in corrected landfill and incineration ratios. Corresponding carbon emission factors are matched to the corrected landfill and incineration ratios to calculate the weights of the waste disposal portfolio.

[0102] The annual waste disposal statistics refer to official data reports released by authoritative agencies (such as local environmental protection bureaus) regarding the final disposal destination of domestic or industrial solid waste in the region. The pollution risk level is a graded assessment of the likelihood that the stretch film will be contaminated by pollutants (such as oil, chemicals, and organic matter) after use, thus losing its recycling value. The relative weight refers to the relative probability of landfill and incineration as the two original disposal methods when allocating recycling proportions after the recycling pathway is blocked; it is assumed here to be proportional to the initial proportion.

[0103] Specifically, suppose a packaged item is delivered to Tianjin, and the goods are described as auto parts. The data processing system first queries the strategy library to obtain the initial disposal ratios for Tianjin: {Landfill: 20%, Incineration: 40%, Recycling: 40%}. Next, the system queries the built-in goods-risk association table. Auto parts are defined as low-pollution risk, and their risk level does not exceed the preset risk threshold. Therefore, the recycling ratio remains unchanged, and the disposal combination weight for this packaged item is the initial ratio. Now suppose another packaged item is also delivered to Tianjin, but the goods are described as pesticide formulations. The system determines this to be high-pollution risk and sets the initial recycling ratio of 40% to 0. This 40% ratio needs to be reallocated. In this case, the initial landfill and incineration ratios are 20% and 40%, respectively, with a relative weight of 20:40, or 1:2. Therefore, the revised landfill ratio = 20% + 40% * (20 / (20 + 40)) ≈ 33.3%, and the revised incineration ratio = 40% + 40% * (40 / (20 + 40)) ≈ 66.7%. Ultimately, the disposal portfolio weights for this high-pollution-risk cargo are {landfill: 0.333, incineration: 0.667, recycling: 0}.

[0104] In some embodiments, risk assessment and weight allocation for this step can be implemented in several ways: Optionally, multiple pollution risk levels can be set, such as high, medium, and low, with different levels corresponding to different degrees of reduction in the recycling ratio, rather than simply zeroing it out. For example, a medium risk may lead to a halving of the recycling ratio. Optionally, the redistribution logic of the recycling ratio may not be linked to the initial ratio, but rather follow an independent rule. For example, in some regions, all non-recyclable plastic waste is preferentially sent to incineration plants for energy utilization, then the zeroed recycling ratio will be entirely added to the incineration ratio. Understandably, the parsing of goods description information can utilize natural language processing technology to extract key features from unstructured product names to determine their pollution attributes; this is not limited here.

[0105] In some embodiments, the responsible parties and behavioral patterns for disposing of waste stretch film may differ significantly between B2B (business-to-business) and B2C (business-to-consumer) transactions. In response, the data processing system selects different disposal weight calculation models based on order type or customer information. For B2C transactions, waste film typically enters the municipal solid waste system, making the aforementioned location-based statistical data model suitable. For B2B transactions, the receiving company may have its own waste management system or even a recycling agreement with the packaging supplier. In this case, the system prioritizes the agreed-upon disposal method (e.g., 100% recycling) to determine the disposal weight, ensuring the calculation results better reflect commercial realities.

[0106] S208. Obtain the carbon emission factor of the material production of the stretch film. Calculate the carbon emission data results of the packaging target based on the absolute mass of material consumption, the carbon emission factor of the material production, and the combined weight of waste disposal.

[0107] Refer to step S105, which will not be repeated here.

[0108] S209. Group the carbon emission data results according to geographical region and time period to generate regional time series data.

[0109] In this context, a geographic region is a statistical unit defined based on the geographical location of the packaged goods; it can be a city, province, or a custom sales area. The time period is the granularity of the data, which can be daily, weekly, monthly, or quarterly. Regional time-series data refers to a dataset formed by aggregating the carbon emission data of each packaged item according to its geographic region and time period.

[0110] Specifically, the data processing system runs a batch processing task (e.g., executed every morning at midnight). This task iterates through all carbon emission data calculated the previous day. For each record, the system extracts its delivery location (e.g., City A) and completion time (e.g., January 5, 2026). Then, the system adds the record's carbon emission value (e.g., 1.509 kg CO2e) and the absolute mass of material consumed (e.g., 0.5 kg), among other key indicators, to the statistical unit corresponding to City A on January 5, 2026. After the task is completed, time-series carbon emission data aggregated by region and day is generated.

[0111] In some embodiments, data grouping in this step can be implemented in several ways: Optionally, the grouping operation can be completed in a data warehouse through an ETL (Extract, Transform, Load) process, leveraging the powerful aggregation and computing capabilities of the data warehouse to process massive amounts of data. Optionally, the data processing system can provide an interactive data analysis interface, allowing users to dynamically select different combinations of geographical regions and time periods to generate the required grouped data in real time to support ad-hoc queries. It is understood that the grouping dimensions can also be expanded, for example, by adding business dimensions such as product type and customer type; this is not limited here.

[0112] In some embodiments, changes in administrative divisions may lead to changes in the name of a geographic region. To address this, the data processing system maintains a historical table of geographic information changes. When performing grouping and aggregation, the system first standardizes the geographic location information in the records by comparing it with this historical table, uniformly mapping the old names (e.g., District A2 of City A) to the new names (e.g., District A1 of City A). This mechanism ensures that even after changes in administrative divisions, historical and new data can be correctly categorized into the same statistical region, ensuring the continuity and accuracy of time series analysis.

[0113] S210. Based on regional time-series data, calculate the amount of stretch film used per unit volume of goods and the carbon emission level in each region, and calculate the standard deviation of carbon emissions.

[0114] The amount of stretch film used per unit volume of goods is an indicator of packaging efficiency, calculated by dividing the total amount of stretch film used in the region by the total volume of goods. Carbon emission levels can be either total regional carbon emissions or carbon emissions per unit volume. The carbon emission standard deviation is a statistic that measures the dispersion of carbon emissions per unit volume among different packaging objectives within a region; a larger standard deviation indicates poorer consistency in packaging operations within the region.

[0115] Specifically, based on the regional time-series data generated in step S209 (e.g., data for January 2026 in City A), the data processing system first calculates the sum of various items: total carbon emissions, total stretch film usage, and total cargo volume (cargo volume data needs to be collected during packing). Then, it calculates core indicators, such as the stretch film usage per unit volume in Region A in January = total usage / total volume. Next, for each independent packing operation in the region, the system calculates its carbon emissions per unit volume. Finally, based on these individual sample values, it calculates their standard deviation. For example, if there are N packing operations in Region A in January, then the carbon emission standard deviation = sqrt[Σ(single unit volume carbon emissions - regional average unit volume carbon emissions)² / (N-1)].

[0116] In some embodiments, the calculation of indicators in this step can be implemented in several ways: Optionally, the calculation can be efficiently completed at the database level using SQL queries, particularly by utilizing window functions to calculate the standard deviation. Optionally, the system can visualize the calculation results, for example, by displaying a map of unit volume usage for each region on a dashboard, or by displaying a trend chart of the standard deviation of carbon emissions for a certain region over time. It is understood that the calculated indicators can also include usage per unit weight, usage per unit value, etc., to adapt to different business analysis needs, and are not limited here.

[0117] In some embodiments, there may be regions with excessively small sample sizes, resulting in statistically insignificant standard deviations. To address this, the data processing system sets a minimum sample size threshold (e.g., 30 times). Before calculating the standard deviation, the system checks the number of operations performed in that region within the given time period. If the number of operations is below the threshold, the system will either not calculate the standard deviation or explicitly indicate an insufficient sample size when displaying the results, to prevent managers from making incorrect judgments based on unreliable data.

[0118] S211. Based on the carbon emission standard deviation, generate adjustment suggestions for winding process parameters in multiple regions.

[0119] The winding process parameters mainly refer to the adjustable settings of the winding machine, such as the pre-stretch ratio, number of winding turns, and film carriage lifting speed. Adjustment suggestions are specific operational guidelines provided by the system based on data analysis, aimed at reducing carbon emissions or improving operational consistency.

[0120] Specifically, after executing step S210, the data processing system sorts the carbon emission standard deviations of each region. For the region with the highest standard deviation (e.g., region B), the system marks it as a key optimization region because of its large internal operational differences and significant optimization potential. Then, within region B, the system identifies the batch of packaging operations with the lowest carbon emissions per unit volume (e.g., the lowest 5%) and extracts the process parameters used in these operations (e.g., pre-stretch ratio 280%, 2 top loops, 2 bottom loops) as the optimal practice parameters for that region. Finally, the system generates an adjustment recommendation: It suggests that region B standardize its existing packaging formulation according to the optimal practice parameters (pre-stretch ratio 280%, 2 / 2 loops) to reduce internal operational differences and overall carbon emission levels.

[0121] In some embodiments, the generation of recommendations for this step can be achieved in several ways: Optionally, the system can employ more complex algorithms, such as decision trees or association rule mining, to discover the deep relationship between low carbon emissions and combinations of process parameters, rather than simply extracting optimal values. Optionally, the adjustment recommendations can be personalized, for example, providing different optimal parameter recommendations for different types of goods within region B. It is understood that the recommendations can be presented as text reports or by directly pushing the optimized parameters to the wrapping machine's formula management system; no limitation is made here.

[0122] In some embodiments, directly replicating the parameters of the optimal region to other regions may lead to an increase in cargo damage rates due to differences in cargo characteristics or transportation conditions. To address this, the data processing system employs a more cautious strategy when generating adjustment recommendations. The system considers not only carbon emission levels but also analyzes reverse logistics feedback data. It seeks the optimal parameters while maintaining an acceptable low cargo damage rate. The generated recommendation might be: In the optimal region, parameter X achieves the lowest carbon emissions and a cargo damage rate of 0. The recommendation suggests that the target region gradually adjust from the current parameters towards parameter X, closely monitoring changes in the cargo damage rate to find a new equilibrium point.

[0123] In some embodiments, during parameter optimization, the data processing system acquires the batch number and cargo type characteristics of the stretch film used in different regions, groups the regions based on the batch number and cargo type characteristics to obtain multiple region comparison groups; selects a certain region comparison group as the target comparison group, and obtains multiple regions within the target comparison group whose absolute value of carbon emission standard deviation is higher than a preset standard threshold as the target optimization group; determines the region with the lowest carbon emission level within the target optimization group as the optimal region, and determines the optimal packaging process parameters corresponding to the packaging operation data in the optimal region; based on the optimal packaging process parameters, generates adjustment suggestions for the packaging process parameters of multiple regions within the target comparison group other than the optimal region.

[0124] The batch number is a unique identifier assigned by the stretch film manufacturer to each batch of products. Products within the same batch have highly consistent physical properties (such as thickness uniformity and tensile strength). Goods type characteristics can include dimensions, weight, and shape stability. Regional comparison groups refer to collections of different geographical areas that used the same batch of stretch film to package the same type of goods, ensuring fairness in the comparison.

[0125] Specifically, the data processing system first filters out all operational records that used batch M stretch film to package goods of type Q (e.g., standard-sized bagged powder). These records may be distributed across three regions: A, B, and C. These three regions form a regional comparison group. The system calculates the standard deviation of carbon emissions per unit volume for each of the three regions and finds that the standard deviation of B is much higher than that of A and C, exceeding the preset threshold. Therefore, B and C, which also has a slightly higher standard deviation, are selected as the target optimization group. Among B and C, the system finds that C has a lower average carbon emission level per unit volume, thus determining C as the optimal region within this optimization group. The system retrieves the most commonly used process parameters (i.e., optimal packaging process parameters) for packaging goods of type Q in region C, such as {pre-stretch ratio: 250%, number of wraps: 3 / 3}. Finally, the system generates an adjustment suggestion for region B: for goods of type Q (using batch M film), it is recommended to adjust the process parameters from the current {200%, 5 / 5} to {250%, 3 / 3} used in the optimal region (C) to improve packaging efficiency and consistency.

[0126] In some embodiments, the grouping and optimization recommendations for this step can be implemented in several ways: Optionally, the granularity of grouping can be finer, for example, by adding the palletizing method dimension to the cargo type characteristics, since different palletizing methods affect the stability of the cargo, thereby affecting the required packaging strength. Optionally, when determining the optimal packaging process parameters, the system can not only extract the parameters for the optimal region, but also use these parameters as the center, combined with a machine learning model, to generate more personalized, fine-tuned parameter recommendations for other regions to adapt to the slight environmental differences that may exist in each region. It is understood that the adjustment recommendations may include an estimated expected carbon emission reduction to incentivize regional managers to adopt the recommendations, which is not limited here.

[0127] In some embodiments, the optimal performance of an optimal region may be due to unobserved factors, such as generally higher operator skills in that region. Directly replicating its parameters may not achieve the same effect in other regions. To address this, the data processing system initiates a factor analysis procedure while generating recommendations. The system attempts to correlate more potential influencing factors, such as operator ID, equipment model, and shift time, with carbon emission data. If it finds that a particular operator's carbon emission performance is consistently significantly better than others, the system adds a note to the adjustment recommendations: the superior performance of the optimal region may be related to the skilled operation of a specific operator; it is recommended to organize relevant operational training or experience sharing while adjusting parameters. This expands the optimization recommendations from simple parameter adjustments to comprehensive improvement schemes that include personnel skill enhancement.

[0128] It should be noted that this factor analysis program is essentially a multivariate statistical analysis module. Its core function is to construct one or more regression models to quantify the impact of different factors on carbon emission performance. When the system needs to perform in-depth causal analysis, it extracts a specific dataset from the database, based on a single packaging job. The dependent variable is a standardized carbon emission indicator (such as carbon emissions per unit volume), while the independent variables include all possible influencing factors. These independent variables are divided into several categories:

[0129] 1. Process parameters (such as pre-stretch ratio, number of winding turns);

[0130] 2. Equipment and environment (e.g., equipment model, equipment service life, ambient temperature and humidity);

[0131] 3. Personnel and management information (e.g., operator ID, shift, region);

[0132] 4. Materials and goods categories (such as stretch film batches, goods types, and palletizing stability levels).

[0133] The system employs statistical methods such as multiple linear regression, mixed-effects models, or analysis of variance (ANOVA) to fit the relationships between the independent and dependent variables. For example, a mixed-effects model can treat region and operator ID as random effects to separate systematic differences caused by individuals or teams. The model outputs the coefficients and their statistical significance (p-value) for each independent variable. A coefficient with a large absolute value and a small p-value (e.g., <0.05) indicates that the factor has a significant and quantifiable impact on carbon emissions. For example, if the coefficient for the dummy variable operator A is -0.15, it means that, controlling for all other factors, operations performed by operator A will have an average carbon emission reduction of 0.15 units per unit volume. Based on these statistically significant analysis results, the system generates improvement recommendations that go beyond simply replicating parameters; they provide more fundamental, data-driven management insights, such as highlighting the need to focus on the equipment maintenance status in region B or promoting operator A's operating methods.

[0134] In this embodiment, by deeply binding logistics tracking numbers to individual packaging operations and combining them with a series of technical means such as real-time material consumption measurement, dynamic waste disposal scenario analysis, equipment performance diagnosis, and closed-loop process optimization, this application constructs a complete and accurate carbon footprint tracking and management system for stretch wrap film. This solution effectively solves the fundamental problems of traditional carbon accounting methods, which rely on static averages, fail to reflect individual differences, and lack diagnostic and optimization capabilities. It achieves a shift from passive and coarse carbon emission statistics to a proactive, refined, traceable, and continuously optimized carbon management model, providing strong technical support and data-driven decision-making basis for the green and low-carbon transformation of the packaging industry and even the entire supply chain.

[0135] The data processing system in the embodiments of this invention is described below from a hardware processing perspective. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a data processing system in an embodiment of this application.

[0136] It should be noted that, Figure 3 The structure of the data processing system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0137] like Figure 3 As shown, the data processing system includes a CPU 301, which can perform various appropriate actions and processes according to a program stored in ROM 302 or a program loaded from storage section 308 into RAM 303, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0138] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including hard disks, etc.; and communication section 309 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0139] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0141] Specifically, the data processing system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the carbon footprint tracking and calculation method for the stretch wrap film provided in the above embodiment.

[0142] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the data processing system described in the above embodiments; or it may exist independently and not assembled into the data processing system. The storage medium carries one or more computer programs that, when executed by a processor of the data processing system, cause the data processing system to implement the carbon footprint tracking and calculation method for the stretch-wound film provided in the above embodiments.

[0143] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0144] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if..." or "after..." or "or in response to determining..." or "or in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "or if (the stated condition or event) is detected" can be interpreted as meaning "if determining..." or "or in response to determining..." or "or in response to detecting (the stated condition or event)" or "or in response to detecting (the stated condition or event)".

Claims

1. A method for tracking and calculating the carbon footprint of stretch wrap film, characterized in that, Applied to a data processing system, the method includes: Receive the tracking number of the packaging target and send a start command bound to the tracking number to the wrapping machine; Real-time monitoring of film roll quality change data at the feeding end of the wrapping machine; after the packaging target completes the packaging operation, the absolute mass of material consumption of the packaging target is generated based on the difference in film roll quality before and after packaging. After the tracking number shows that the goods have been signed for, the geographical location of the delivery and the description of the goods are confirmed. Query the preset regional waste disposal strategy library to determine the waste disposal combination weights corresponding to the signed geographical location and the cargo description information; Obtain the carbon emission factor of the material production of the stretch film, and calculate the carbon emission data result of the packaging target based on the absolute mass of the material consumption, the carbon emission factor of the material production, and the combined weight of the waste disposal.

2. The method according to claim 1, characterized in that, The step of real-time monitoring of film roll quality change data at the feeding end of the wrapping machine, and generating the absolute mass of material consumption for the packaging target based on the difference in film roll quality before and after packaging, specifically includes: Upon receiving the operation start command, the first value of the weighing sensor installed on the film frame of the wrapping machine is read as the initial film roll mass. When the winding machine stops operating and the film cutting signal is triggered, the second value of the weighing sensor is read as the final film roll mass. The mass difference between the initial membrane roll mass and the final membrane roll mass is calculated, and the mass difference is corrected by combining a preset membrane frame vibration compensation coefficient to obtain the absolute mass of material consumption.

3. The method according to claim 1, characterized in that, The step of querying a preset regional waste disposal strategy database to determine the waste disposal combination weights corresponding to the delivery location and the cargo description information specifically includes: Obtain annual waste disposal statistics for the administrative region to which the signed geographical location belongs; the statistics include the initial landfill ratio, the initial incineration ratio, and the initial recycling ratio. The pollution risk level of the stretch film is determined based on the cargo description information, and the initial recycling ratio is reset to zero when the pollution risk level is higher than a preset risk threshold. The initial recycling ratio, which is reduced to zero, is allocated to the initial landfill ratio and the initial incineration ratio according to their relative weights, to obtain the corrected landfill ratio and incineration ratio. The corresponding carbon emission factors are matched to the modified landfill ratio and incineration ratio respectively, and the waste disposal combination weights are calculated.

4. The method according to claim 1, characterized in that, After the steps of monitoring the quality change data of the film roll at the feeding end of the wrapping machine in real time, and generating the absolute mass of material consumption of the packaging target based on the quality difference of the film roll before and after packaging, the method further includes: The pre-stretch ratio, number of wrapping turns, and film carriage lifting stroke data of the wrapping machine during the packaging process are read, and combined with the perimeter data of the packaging target, the standard density and standard thickness of the wrapping film, the theoretical material consumption mass of the packaging target is calculated. Calculate the tensile performance deviation rate between the absolute mass of material consumed and the theoretical mass of material consumed; When the tensile performance deviation rate exceeds the preset mechanical performance threshold, it is determined that the pre-stretching roller of the winding machine has a slippage abnormality, and an equipment maintenance instruction is generated.

5. The method according to claim 4, characterized in that, After the step of calculating the tensile performance deviation rate between the absolute mass of material consumed and the theoretical mass of material consumed, the method further includes: When the tensile performance deviation rate does not exceed the preset mechanical performance threshold, reverse logistics feedback data associated with the logistics tracking number is acquired; the reverse logistics feedback data includes records of goods being dumped, scattered, or damaged during transportation. When the carbon emission data is in a preset high range and the reverse logistics feedback data has zero records within a preset historical period, the current packaging process is determined to be in a redundant packaging state. Generate micro-decreasing test instructions to reduce the number of wrapping layers or increase the pre-stretch ratio in subsequent packaging operations of similar goods by a preset step size until a preset reverse logistics feedback critical threshold is detected.

6. The method according to claim 1, characterized in that, After the step of obtaining the carbon emission factor of the material production of the stretch film, and calculating the carbon emission data result of the packaging target based on the absolute mass of material consumption, the carbon emission factor of the material production, and the combined weight of the waste disposal, the method further includes: The carbon emission data results are grouped according to geographical region and time period to generate regional time series data; Based on the time-series data of the region, the amount of stretch film used per unit volume of goods and the carbon emission level of each region are calculated, and the standard deviation of carbon emissions is calculated. Based on the carbon emission standard deviation, adjustment suggestions for winding process parameters in multiple regions are generated.

7. The method according to claim 6, characterized in that, The step of generating adjustment suggestions for winding process parameters for multiple regions based on the carbon emission standard deviation specifically includes: Obtain the batch number and cargo type characteristics of the stretch film used in different regions, and group each region based on the batch number and cargo type characteristics to obtain multiple region comparison groups; Select a certain region as the target comparison group, and obtain multiple regions within the target comparison group whose absolute value of carbon emission standard deviation is higher than a preset standard threshold as the target optimization group; The region with the lowest carbon emission level within the target optimization group is identified as the optimal region, and the optimal packaging process parameters corresponding to the packaging operation data in the optimal region are determined. Based on the optimal packaging process parameters, adjustment suggestions for the packaging process parameters of multiple regions other than the optimal region within the target comparison group are generated.

8. A data processing system, characterized in that, The data processing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the data processing system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the data processing system, the data processing system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on a data processing system, the data processing system performs the method as described in any one of claims 1-7.

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