Product carbon footprint tracking method, system and device assisted by qr code traceability

Abstract

The application discloses a product carbon footprint tracking method, system and equipment assisted by a two-dimensional code traceability, and relates to the technical field of carbon emission management. The method comprises the following steps: acquiring a target product full life cycle and creating a storage chain; generating a unique two-dimensional code and binding; generating a write instruction when the two-dimensional code is triggered; calling pre-carbon emission data, reading environmental parameters of a scanning node; combining the environmental parameters and the emission data to perform evolution analysis and generate updated data; writing the updated data into a storage space after associating the pre-data; and performing carbon footprint tracking management through the two-dimensional code and the updated chain. The technical problems of inaccurate carbon emission accounting and low tracking efficiency caused by data fragmentation, dynamic update lag and fuzzy boundary definition in the traditional carbon footprint calculation method are solved. Through the combination of the carbon footprint dynamic evolution mechanism of the two-dimensional code identification binding period storage chain and the real-time collection of environmental context parameters, the technical effects of improving the real-time performance, accuracy and traceability of carbon footprint accounting data are achieved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission management technology, specifically to a method, system, and equipment for product carbon footprint tracking assisted by QR code traceability. Background Technology

[0002] The accurate quantification and reliable management of product carbon footprints have become the core cornerstone for promoting green and low-carbon transformation. Currently, although the internationally accepted Life Cycle Assessment (LCA) method provides a theoretical framework, it faces significant challenges in practical applications: its calculations heavily rely on static average emission factors and lagging databases, making it difficult to capture the dynamic differences of individual products in the actual manufacturing, distribution, and use processes; the data mostly comes from manual entry and macro-level estimation, resulting in coarse granularity and low transparency; it focuses mainly on the production stage, lacking real-time collection and dynamic correlation of data throughout the entire life cycle, including raw material procurement, transportation, use, and disposal, leading to fragmented carbon emission data and low traceability efficiency, becoming a bottleneck for enterprises to cope with green trade barriers. Summary of the Invention

[0003] This application provides a product carbon footprint tracking method, system, and equipment assisted by QR code traceability, which solves the technical problems of inaccurate carbon emission accounting and low tracking efficiency caused by data fragmentation, lag in dynamic updates, and ambiguous boundary definition in traditional carbon footprint calculation methods.

[0004] In view of the above problems, this application provides a product carbon footprint tracking method, system and equipment assisted by QR code traceability.

[0005] The first aspect of this application provides a product carbon footprint tracking method assisted by QR code traceability, the method comprising:

[0006] The entire lifecycle of a target product is obtained, and a cycle storage chain is created based on this lifecycle. The cycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and these storage spaces are one-time write spaces. During the manufacturing process of the target product, a unique QR code identifier is generated and bound to the cycle storage chain. At any point in the lifecycle, if a first authorized entity triggers the QR code identifier, a write command is generated. Based on the write command, carbon emission data from preceding chain nodes in the cycle storage chain is retrieved, and environmental context parameters of the scanned nodes are read. These environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factors. Carbon footprint evolution analysis is performed based on the environmental context parameters and the carbon emission data to establish node update data. The node update data is associated with the data of preceding chain nodes and written to the mapped storage space. Carbon footprint tracking and management are then performed based on the QR code identifier and the updated cycle storage chain.

[0007] A second aspect of this application provides a product carbon footprint tracking system assisted by QR code traceability, the system comprising:

[0008] Chain Creation Module: Acquires the entire lifecycle of the target product and creates a cycle storage chain based on the entire lifecycle. The cycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and these storage spaces are one-time write spaces. Identifier Binding Module: During the manufacturing process of the target product, a unique QR code identifier is generated for the target product, and the QR code identifier is bound to the cycle storage chain. Identifier Trigger Module: In any lifecycle, if a first authorized entity triggers the QR code identifier, a write command is generated. Data Reading Module: After calling the carbon emission data of the preceding chain nodes in the cycle storage chain according to the write command, the environmental context parameters of the scanned nodes are read. The environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factor. Evolution Analysis Module: Performs carbon footprint evolution analysis based on the environmental context parameters and the carbon emission data, and establishes node update data. Tracking Management Module: After associating the node update data with the data of the preceding chain nodes, it is written to the mapped storage space, and carbon footprint tracking management is performed based on the QR code identifier and the updated cycle storage chain.

[0009] A third aspect of this application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0010] The entire lifecycle of a target product is obtained, and a cycle storage chain is created based on this lifecycle. The cycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and these storage spaces are one-time write spaces. During the manufacturing process of the target product, a unique QR code identifier is generated and bound to the cycle storage chain. At any point in the lifecycle, if a first authorized entity triggers the QR code identifier, a write command is generated. Based on the write command, carbon emission data from preceding chain nodes in the cycle storage chain is retrieved, and environmental context parameters of the scanned nodes are read. These environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factors. Carbon footprint evolution analysis is performed based on the environmental context parameters and the carbon emission data to establish node update data. The node update data is associated with the data of preceding chain nodes and written to the mapped storage space. Carbon footprint tracking and management are then performed based on the QR code identifier and the updated cycle storage chain.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] First, the entire lifecycle of the target product is obtained, and a cycle storage chain is created based on this lifecycle. The cycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and these storage spaces are one-time write spaces. Next, during the manufacturing process of the target product, a unique QR code identifier is generated for the target product, and this QR code identifier is bound to the cycle storage chain. Further, in any lifecycle stage, if a first authorized entity triggers the QR code identifier, a write command is generated. Then, based on the write command, the carbon emission data of the preceding chain nodes in the cycle storage chain is retrieved, and the environmental context parameters of the scanned nodes are read. These environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factors. Afterward, carbon footprint evolution analysis is performed based on the environmental context parameters and the carbon emission data to establish node update data. Finally, the node update data is associated with the data of the preceding chain nodes and written into the mapped storage space. Carbon footprint tracking and management are then performed based on the QR code identifier and the updated cycle storage chain. This invention addresses the technical problems of inaccurate carbon emission accounting and low tracking efficiency caused by data fragmentation, delayed dynamic updates, and ambiguous boundary definitions in traditional carbon footprint calculation methods. By combining a dynamic evolution mechanism of carbon footprint identification with periodic storage chains and real-time collection of environmental context parameters, it achieves the technical effect of improving the real-time performance, accuracy, and traceability of carbon footprint accounting data. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A schematic diagram of the product carbon footprint tracking method with QR code traceability assistance provided in the embodiments of this application.

[0015] Figure 2 A schematic diagram of the structure of a product carbon footprint tracking system with QR code traceability assistance provided in this application embodiment.

[0016] Explanation of reference numerals in the attached diagram: Chain creation module 11, Identifier binding module 12, Identifier triggering module 13, Data reading module 14, Evolution analysis module 15, Tracking management module 16. Detailed Implementation

[0017] This application addresses the technical problems of inaccurate carbon emission accounting and low tracking efficiency caused by traditional carbon footprint calculation methods due to data fragmentation, delayed dynamic updates, and ambiguous boundary definitions by providing a product carbon footprint tracking method, system, and equipment assisted by QR code traceability.

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0020] Example 1, as Figure 1 As shown, this application provides a product carbon footprint tracking method assisted by QR code traceability, wherein the method includes:

[0021] Obtain the entire lifecycle of the target product, and create a lifecycle storage chain based on the entire lifecycle. The lifecycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and the storage spaces are one-time write spaces.

[0022] In one embodiment, the entire lifecycle information of the target product is first acquired, covering all stages from raw material acquisition, production and processing, transportation and distribution, use and maintenance to end-of-life recycling. Then, based on the acquired lifecycle information, a corresponding cycle storage chain is established. This cycle storage chain is logically divided into multiple contiguous storage spaces, each corresponding one-to-one with a stage in the entire lifecycle, thus achieving hierarchical data management for different lifecycle stages. To ensure data authenticity and immutability, each storage space is designed as a one-time write space; that is, after data is written at the corresponding lifecycle node, the space is locked and can no longer be overwritten or modified, only appended reading is allowed. This design ensures that carbon emission activity data at each stage becomes a fixed record after being written, preventing human or systemic tampering and guaranteeing the integrity and reliability of the tracking data. Through this cycle storage chain, carbon emission data at each stage of the product lifecycle can be clearly linked together, forming a dynamically updated and traceable carbon footprint record chain, providing a solid foundation for subsequent QR code-based triggering, data retrieval, and tracking management.

[0023] During the manufacturing process of the target product, a unique QR code identifier is generated for the target product, and the QR code identifier is bound to the periodic storage chain.

[0024] In one embodiment, when a target product enters the manufacturing process, a unique digital identity is first generated for it in the form of a QR code. This QR code can be attached to or embedded in the target product through printing, laser etching, or electronic encoding, ensuring its continued presence throughout the product's lifecycle. Furthermore, the generated QR code is bound to a lifecycle storage chain, establishing a mapping relationship between the QR code and the lifecycle storage chain. This allows the system to automatically locate the corresponding lifecycle storage chain whenever the QR code is scanned or triggered, and perform data write or read operations within the storage space corresponding to that lifecycle. This binding mechanism ensures that the carbon emission data of each product corresponds one-to-one with its QR code, preventing data confusion between different products.

[0025] If a first authorized entity triggers the QR code identifier during any lifecycle, a write instruction is generated.

[0026] In one embodiment, the target product may trigger carbon emission data updates at any stage of its lifecycle. When an entity with write permissions, i.e., the first-level authorized entity, scans or activates the product's unique QR code, the QR code is automatically identified, and the validity of its authorization is verified. If the verification passes, a write instruction is generated. This instruction includes the trigger time, the triggering entity's identity information, and the current lifecycle stage identifier, indicating subsequent data collection and storage operations. After the write instruction is generated, it automatically associates with the target product's lifecycle storage chain, locates the one-time storage space corresponding to the current lifecycle stage, and reserves data interfaces for the environmental context parameters and carbon emission activity data to be written, ensuring the integrity and continuity of the carbon footprint information throughout the entire lifecycle and avoiding data loss or delayed updates.

[0027] Furthermore, the provision that if a first authorized entity triggers the QR code identifier during any lifecycle, a write instruction will be generated, also includes:

[0028] Determine whether the lifecycle is a product quality inspection cycle; if the lifecycle is a product quality inspection cycle, generate a cycle verification instruction; perform target product quality verification at the current node according to the cycle verification instruction; if the quality verification result is a pass result, retain the cycle storage chain and continue to update carbon emission data according to the write instruction.

[0029] Preferably, throughout the target product's lifecycle, the status of each stage is tracked and determined in real time based on the lifecycle stage markers. When the target product is in the quality inspection stage, this cycle is automatically identified as the product quality inspection cycle. Once the current lifecycle stage is determined to be the product quality inspection cycle, a cycle verification instruction is automatically generated. This cycle verification instruction aims to initiate the quality inspection process and confirm whether the product meets the quality standards at this stage. When the automated inspection equipment receives the cycle verification instruction, it performs quality verification on the current batch of target products from aspects such as product appearance inspection and performance testing, and feeds back the quality verification results to the system. Subsequently, the feedback quality verification results are analyzed. If the quality verification result shows that the verification is passed, it is determined that the batch of target products meets the quality requirements, the verification process is passed, the data in the cycle storage chain will remain unchanged, and carbon emission data will continue to be updated according to the previous write instructions to ensure the accuracy of product quality and carbon emission data.

[0030] Furthermore, the step of performing target product quality verification at the current node according to the periodic verification instruction includes:

[0031] If the quality verification result is unsuccessful, a chain reset instruction for the periodic storage chain is generated; the periodic storage chain is reset to a scrapped storage chain according to the chain reset instruction; and carbon emission data is updated according to the scrapped storage chain.

[0032] Optionally, if the quality verification result shows that the verification failed, a chain reset instruction will be automatically generated. This chain reset instruction carries the unique QR code identifier of the target product, the test result status, and the reset type field, which is used to trigger the system to reset the cycle storage chain. When executing the chain reset instruction, the system marks the cycle storage chain originally bound to the product as invalid and resets it to a scrapped storage chain. The scrapped storage chain is structurally consistent with the normal cycle storage chain, but the lifecycle node attributes are uniformly defined as scrapped status, which is used to record the carbon emission data generated by the product during the scrapping process. At the same time, all subsequent write instructions can only be executed on the scrapped storage chain to prevent invalid products from continuing to accumulate data in the normal chain. Once the target product enters the end-of-life stage, the system updates the carbon emission data according to the end-of-life storage chain. The updated data includes parameters such as dismantling energy consumption, transportation methods, waste disposal methods, and recycling rates generated during the end-of-life process. By writing this data into the one-time storage space of the end-of-life storage chain, a complete carbon footprint record of the product at the end of its life cycle can be formed. These records will also be synchronized to the cloud platform to improve the full life cycle carbon footprint report of the target product.

[0033] After calling the carbon emission data of the preceding chain node in the cycle storage chain according to the write instruction, the environmental context parameters of the scanning node are read. The environmental context parameters include location data, transportation mode, energy composition and real-time carbon emission factor.

[0034] In one embodiment, after generating a write instruction, the system updates the data of the current lifecycle node according to the write instruction. At this time, it first calls the carbon emission data of the preceding chain nodes already written in the periodic storage chain to ensure the continuity and integrity of the data. Subsequently, the scanning node is activated, and the IoT device bound to the node is retrieved synchronously according to the write instruction to capture the contextual information of the target product's environment, including location data, transportation mode, energy composition, and real-time carbon emission factor. Among them, the location data records the geographical location of the current stage; the transportation mode records the transportation mode of the target product at this node, such as road transportation, rail transportation, air transportation, or sea transportation; the energy composition describes the types and proportions of energy used in this stage, usually including electricity, fuel, natural gas, etc.; the real-time carbon emission factor reflects the carbon emission factor at the current moment, which measures the amount of carbon emissions generated per unit of energy or material consumption. Subsequently, the collected environmental context parameters are combined with the carbon emission data of the preceding chain nodes as input data for subsequent carbon footprint evolution analysis. This ensures that the carbon emission data of each life cycle node not only reflects the historical cumulative value, but also reflects the immediate status and actual impact of that node, thereby improving the accuracy and timeliness of carbon footprint tracking.

[0035] Furthermore, the reading of the environment context parameters of the scanning node includes:

[0036] The IoT device is activated according to the write instruction, and environmental context parameters are collected using the IoT device to establish the collection results; the edge computing node is activated, and the data preprocessing of the collection results is performed according to the edge computing node; the preprocessed collection results are uploaded as environmental context parameters to the storage space under the current scanning node.

[0037] Preferably, the process begins by sending an activation signal to the IoT devices configured on the current node according to the write command. These IoT devices include, but are not limited to, smart meters, temperature and humidity sensors, energy consumption monitoring terminals, GPS positioning devices, or transportation trackers. Subsequently, the activated IoT devices collect environmental context data related to the product lifecycle in real time, such as geographical location, transportation mode, energy consumption type and quantity, and carbon emission factors provided by external databases or real-time interfaces. The collected data is then aggregated and stored to form a collection result. This result is then transmitted to edge computing nodes, typically deployed in production workshop gateways, logistics relay stations, or local servers. These nodes preprocess the raw data, performing tasks such as removing outliers and duplicates, imputing missing values, and data standardization. Outlier removal can be performed using statistical methods (e.g., Z-score, box plot); duplicate removal can be performed using comparisons based on primary keys or timestamps; missing value imputation can be performed using mean / median interpolation; and data standardization can be achieved by converting data from different sources into a unified unit. This edge computing node processing significantly improves data transmission efficiency and reduces latency. Then, the edge computing nodes send back the preprocessed collection results. The system will use the received data as environmental context parameters and upload it to the one-time storage space corresponding to the current scanning node in the cycle storage chain. The writing process is controlled by the write command to ensure that the data corresponds one-to-one with the life cycle node and cannot be modified. In this way, each node retains verified context information, which can be directly called and combined with the carbon emission data of the preceding nodes to perform carbon footprint evolution analysis, ensuring the integrity and reliability of the carbon footprint data chain.

[0038] Carbon footprint evolution analysis is performed based on the environmental context parameters and carbon emission data to establish node update data.

[0039] In one embodiment, after obtaining the environmental context parameters of the scanning node and the carbon emission data of the preceding chain nodes, the environmental context parameters and the accumulated carbon emission data of the preceding nodes are synchronously sent to the carbon footprint dynamic evolution network for fusion processing. The quantitative indicators in the environmental context are transformed into computable carbon footprint evolution operators. These calculated carbon footprint evolution operators are then combined with the carbon emission data for dynamic transformation processing. That is, the carbon emission data is corrected based on the real-time carbon footprint evolution operators, thereby reflecting the impact of changes in energy structure on cumulative carbon emissions. Finally, updated data for the current node is established based on the calculation results and correlated with the data of the preceding chain nodes to form a complete and continuous carbon footprint evolution chain, providing a solid data foundation for subsequent tracking and authentication.

[0040] Furthermore, the step of performing carbon footprint evolution analysis based on the environmental context parameters and the carbon emission data to establish node update data includes:

[0041] The environmental context parameters and carbon emission data are synchronously sent to the carbon footprint dynamic evolution network. The preprocessing layer of the carbon footprint dynamic evolution network performs data transformation of the environmental context parameters to establish a carbon footprint evolution operator, which includes a spatial migration operator, a circulation operator, an energy mixing operator, and a temporal perturbation operator. The carbon footprint evolution operator and the carbon emission data are sent to the computing layer to perform dynamic transformation processing of the carbon emission data. Node update data is established based on the dynamic transformation processing results.

[0042] Optionally, the collected environmental context parameters and carbon emission data from the preceding chain nodes are synchronously sent to a pre-constructed carbon footprint dynamic evolution network. This network, as an integrated data analysis platform, can centrally process and analyze carbon emission data from different nodes in real time, ensuring the real-time nature and accuracy of the data. In the carbon footprint dynamic evolution network, the preprocessing layer receives the environmental context parameters. For the received location data, it uses Euclidean distance to calculate the geographical distance between the geographic coordinates of the preceding chain node and the geographic coordinates of the currently scanned node, and uses this distance as a spatial migration operator. For the received transportation mode, it compares the mode with the transportation emission table to obtain the unit transportation emission coefficient, and uses this coefficient as a flow operator. For the received energy composition, it organizes the energy composition of the currently scanned node into a percentage set, such as: E={(e1,p1),(e2,p2),...,(e... n ,p n )}, where e n p represents the nth energy type. n Let p1, p2, ..., pn represent the proportion of the nth energy type in the total energy consumption, and p1, p2, ..., pn represent the proportion of the nth energy type in the total energy consumption. nThe sum of the values ​​is 1. Then, the carbon emission factors of various energy sources are labeled to form an energy mixing operator. For the received real-time carbon emission factors, the real-time carbon emission factors are divided by the historical average carbon emission factors to obtain a time-series perturbation operator. By summarizing and storing the calculated spatial migration operator, circulation operator, energy mixing operator, and time-series perturbation operator, a carbon footprint evolution operator is formed, which will serve as a key tool for analyzing carbon footprint changes. Next, the preprocessing layer sends the carbon footprint evolution operator and carbon emission data to the computation layer. The computation layer calculates and corrects the carbon emission data based on the carbon footprint evolution operator, generating the final dynamic transformation result. This dynamic transformation result reflects the real-time changes in carbon emissions, comprehensively considering multiple dimensions such as space, transportation, energy, and time. Finally, through this dynamic transformation result, a node update data is established. This node update data includes the carbon emission values ​​adjusted according to the evolution operator and possible new carbon emission sources. It will be used to supplement and correct the carbon footprint data chain throughout the product lifecycle, ensuring that carbon emission calculations are more accurate and reasonable.

[0043] Furthermore, the step of sending the carbon footprint evolution operator and the carbon emission data to the computing layer to perform dynamic transformation processing of the carbon emission data includes:

[0044] The spatial processing sublayer in the computation layer iteratively calculates the carbon emission data and the spatial migration operator corresponding to the location parameters to establish a first dynamic correction value. The transportation processing sublayer in the computation layer processes the first dynamic correction value and the circulation operator corresponding to the transportation mode to establish a second dynamic correction value. The energy mixing sublayer in the computation layer processes the second dynamic correction value and the energy mixing operator corresponding to the energy composition to establish a third dynamic correction value. The confidence computation layer in the computation layer calculates the confidence of the data source based on the time-series perturbation operator, and uses the confidence to weight the third dynamic correction value to establish a dynamic transformation result.

[0045] Optionally, the computational layers in the carbon footprint dynamic evolution network include a spatial processing sublayer, a transportation processing sublayer, an energy mixing sublayer, and a confidence calculation layer. In the spatial processing sublayer, the spatial migration operator corresponding to the location parameter is multiplied by the carbon intensity of the region at the current moment, and then added to the current carbon emission data. This process is iterative; each time the location changes, the spatial migration operator is recalculated, and the carbon emission data is corrected based on the new spatial migration operator to obtain the first dynamic correction value. In the transportation processing sublayer, the circulation operator corresponding to the transportation mode is multiplied by the transportation mass and transportation distance, and the calculated product is superimposed on the first dynamic correction value to obtain the second dynamic correction value. In the energy mixing sublayer, the proportion of each energy type in the energy mixing operator corresponding to the energy composition is multiplied by the carbon emission factor of that energy type, and the products are accumulated to obtain the energy mixing correction value. By adding the energy mixing correction value to 1, and then multiplying the sum by the second dynamic correction value, the third dynamic correction value is obtained. In the confidence calculation layer, the confidence quantification of the time-series perturbation operator is performed based on the Sigmoid function or a linear function to determine the confidence weight of the data source. By weighting this confidence weight with the third dynamic correction value, the dynamic transformation result is obtained. This dynamic transformation result includes the carbon emission value after comprehensive correction in four dimensions: space, transportation, energy, and confidence, thereby providing high-precision data support for carbon footprint tracking throughout the product's entire life cycle.

[0046] After associating the node update data with the data of the preceding chain node, the data is written into the mapped storage space, and carbon footprint tracking management is performed based on the QR code identifier and the updated periodic storage chain.

[0047] In one embodiment, upon receiving node update data, this data is associated with the data of the preceding chain nodes and written to the corresponding mapped storage space in the periodic storage chain. Once written, the data is automatically locked to prevent subsequent modifications, thus ensuring the immutability and authenticity of the data. After writing, the system uses a QR code as an access point to bind the updated periodic storage chain to the target product. At this point, any authorized entity can retrieve the product's latest carbon footprint data in real time by scanning the QR code, ensuring the integrity and timeliness of carbon footprint tracking management. Through the above-described carbon footprint tracking management, not only can precise traceability of products at each stage of their lifecycle be achieved, but also a carbon footprint profile conforming to international standards can be established at the entire chain level for subsequent carbon certification, green supply chain management, and other purposes.

[0048] Furthermore, the carbon footprint tracking management based on QR code identification and the updated periodic storage chain includes:

[0049] If a second authorized entity triggers the QR code identifier, a read-only instruction is generated; after encapsulating the periodic storage chain according to the read-only instruction, the carbon footprint visualization is performed.

[0050] Preferably, when a second authorized entity (such as a regulator, third-party certification body, or user) scans the QR code of the target product, the identity of the authorized entity is verified to ensure that it has read-only access. Upon successful verification, a read-only instruction is generated, containing the QR code identifier, product information, read timestamp, and user identity information. Subsequently, the system encapsulates the lifecycle storage chain corresponding to the target product according to the read-only instruction. This involves extracting all relevant carbon footprint data from the lifecycle storage chain and storing it separately, ensuring that all carbon emission data related to the product's lifecycle can be extracted at once via the read instruction. During the encapsulation process, the data in the chain is encrypted to ensure data transmission security and prevent unauthorized access or tampering. After encapsulation, the extracted carbon footprint data is visualized, typically using charts, dashboards, or other visual methods to display the product's full lifecycle carbon emission information, including carbon emissions at each stage (such as production, transportation, use, and disposal), energy structure, and the carbon footprint contribution of transportation methods. This enhances users' understanding of product carbon emissions and provides transparent and verifiable evidence for third-party certification and international green trade.

[0051] Furthermore, after generating the write instruction, the process includes:

[0052] Generate a cloud platform synchronization instruction based on the write instruction; upload the stored data in the periodic storage chain to the cloud platform based on the cloud platform synchronization instruction, and build a carbon footprint report on the cloud platform; perform carbon footprint tracking and management of the target product based on the carbon footprint report of the cloud platform.

[0053] Preferably, the cloud platform synchronization instruction is first generated based on the content of the write instruction. This cloud platform synchronization instruction contains all carbon emission data in the lifecycle storage chain, the data upload timestamp, the data source identifier, and the unique QR code identifier of the target product, ensuring that the data in the lifecycle storage chain can be accurately and in real time uploaded to the cloud platform. Subsequently, according to this cloud platform synchronization instruction, all carbon emission data in the lifecycle storage chain is uploaded to the cloud platform via a secure data transmission protocol. During the upload process, the data is packaged and encrypted according to a predetermined format to ensure security during transmission. Once the data is successfully uploaded, the cloud platform automatically generates a carbon footprint report based on the uploaded data. This carbon footprint report calculates the overall carbon footprint based on the carbon emission data throughout the product's entire lifecycle and provides detailed information such as carbon emission distribution and carbon emission amounts at each stage of the lifecycle. The carbon footprint report generated by the cloud platform will then be used for carbon footprint tracking and management, including continuous monitoring and analysis of carbon emissions throughout the product's lifecycle, to track the dynamic carbon emissions of the product at different stages of its lifecycle in real time, helping enterprises, regulatory agencies, and consumers understand the green performance of products and achieve sustainable development goals.

[0054] In summary, the embodiments of this application have at least the following technical effects:

[0055] First, the entire lifecycle of the target product is obtained, and a cycle storage chain is created based on this lifecycle. The cycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and these storage spaces are one-time write spaces. Next, during the manufacturing process of the target product, a unique QR code identifier is generated for the target product, and this QR code identifier is bound to the cycle storage chain. Further, in any lifecycle stage, if a first authorized entity triggers the QR code identifier, a write command is generated. Then, based on the write command, the carbon emission data of the preceding chain nodes in the cycle storage chain is retrieved, and the environmental context parameters of the scanned nodes are read. These environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factors. Afterward, carbon footprint evolution analysis is performed based on the environmental context parameters and the carbon emission data to establish node update data. Finally, the node update data is associated with the data of the preceding chain nodes and written into the mapped storage space. Carbon footprint tracking and management are then performed based on the QR code identifier and the updated cycle storage chain. This invention addresses the technical problems of inaccurate carbon emission accounting and low tracking efficiency caused by data fragmentation, delayed dynamic updates, and ambiguous boundary definitions in traditional carbon footprint calculation methods. By combining a dynamic evolution mechanism of carbon footprint identification with periodic storage chains and real-time collection of environmental context parameters, it achieves the technical effect of improving the real-time performance, accuracy, and traceability of carbon footprint accounting data.

[0056] Example 2, based on the same inventive concept as the QR code-assisted product carbon footprint tracking method in the aforementioned examples, such as... Figure 2 As shown, this application provides a product carbon footprint tracking system assisted by QR code traceability, wherein the system includes:

[0057] Chain Creation Module 11: Obtains the entire lifecycle of the target product and creates a cycle storage chain based on the entire lifecycle. The cycle storage chain is configured with storage spaces that are mapped one-to-one with the entire lifecycle, and these storage spaces are one-time write spaces. Identifier Binding Module 12: During the manufacturing process of the target product, a unique QR code identifier is generated for the target product, and the QR code identifier is bound to the cycle storage chain. Identifier Trigger Module 13: In any lifecycle, if a first authorized entity triggers the QR code identifier, a write command is generated. Data Reading Module 14: After calling the carbon emission data of the preceding chain nodes in the cycle storage chain according to the write command, the environmental context parameters of the scanned nodes are read. The environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factor. Evolution Analysis Module 15: Performs carbon footprint evolution analysis based on the environmental context parameters and the carbon emission data, and establishes node update data. Tracking Management Module 16: After associating the node update data with the data of the preceding chain nodes, it is written to the mapped storage space, and carbon footprint tracking management is performed based on the QR code identifier and the updated cycle storage chain.

[0058] In some embodiments, the identifier triggering module 13 includes:

[0059] Determine whether the lifecycle is a product quality inspection cycle; if the lifecycle is a product quality inspection cycle, generate a cycle verification instruction; perform target product quality verification at the current node according to the cycle verification instruction; if the quality verification result is a pass result, retain the cycle storage chain and continue to update carbon emission data according to the write instruction.

[0060] In some embodiments, the identifier triggering module 13 includes:

[0061] If the quality verification result is unsuccessful, a chain reset instruction for the periodic storage chain is generated; the periodic storage chain is reset to a scrapped storage chain according to the chain reset instruction; and carbon emission data is updated according to the scrapped storage chain.

[0062] In some embodiments, the data reading module 14 includes:

[0063] The IoT device is activated according to the write instruction, and environmental context parameters are collected using the IoT device to establish the collection results; the edge computing node is activated, and the data preprocessing of the collection results is performed according to the edge computing node; the preprocessed collection results are uploaded as environmental context parameters to the storage space under the current scanning node.

[0064] In some embodiments, the evolution analysis module 15 includes:

[0065] The environmental context parameters and carbon emission data are synchronously sent to the carbon footprint dynamic evolution network. The preprocessing layer of the carbon footprint dynamic evolution network performs data transformation of the environmental context parameters to establish a carbon footprint evolution operator, which includes a spatial migration operator, a circulation operator, an energy mixing operator, and a temporal perturbation operator. The carbon footprint evolution operator and the carbon emission data are sent to the computing layer to perform dynamic transformation processing of the carbon emission data. Node update data is established based on the dynamic transformation processing results.

[0066] In some embodiments, the evolution analysis module 15 includes:

[0067] The spatial processing sublayer in the computation layer iteratively calculates the carbon emission data and the spatial migration operator corresponding to the location parameters to establish a first dynamic correction value. The transportation processing sublayer in the computation layer processes the first dynamic correction value and the circulation operator corresponding to the transportation mode to establish a second dynamic correction value. The energy mixing sublayer in the computation layer processes the second dynamic correction value and the energy mixing operator corresponding to the energy composition to establish a third dynamic correction value. The confidence computation layer in the computation layer calculates the confidence of the data source based on the time-series perturbation operator, and uses the confidence to weight the third dynamic correction value to establish a dynamic transformation result.

[0068] In some embodiments, the tracking management module 16 includes:

[0069] If a second authorized entity triggers the QR code identifier, a read-only instruction is generated; after encapsulating the periodic storage chain according to the read-only instruction, the carbon footprint visualization is performed.

[0070] In some embodiments, the tracking management module 16 includes:

[0071] Generate a cloud platform synchronization instruction based on the write instruction; upload the stored data in the periodic storage chain to the cloud platform based on the cloud platform synchronization instruction, and build a carbon footprint report on the cloud platform; perform carbon footprint tracking and management of the target product based on the carbon footprint report of the cloud platform.

[0072] Example 3: Based on the same inventive concept as the QR code traceability-assisted product carbon footprint tracking method in Example 1, this application provides an electronic device. This electronic device can be a server, including a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. The computer program is executed by the processor to implement the QR code traceability-assisted product carbon footprint tracking method.

[0073] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0074] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0075] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for product carbon footprint tracking with the aid of QR code traceability, characterized in that, The method comprises: acquiring a full life cycle of a target product, and creating a cycle storage chain according to the full life cycle, wherein a storage space that is one-to-one mapped with the full life cycle is configured in the cycle storage chain, and the storage space is a one-time write space; generating a unique two-dimensional code identifier of the target product when the target product is in a manufacturing process, and binding the two-dimensional code identifier with the cycle storage chain; if a first authority triggers the two-dimensional code identifier in any life cycle, generating a write instruction; after invoking carbon emission data of a previous chain node in the cycle storage chain according to the write instruction, reading environmental context parameters of a scanning node, the environmental context parameters comprising location data, transportation mode, energy composition and real-time carbon emission factor; based on the environmental context parameters and the carbon emission data, performing carbon footprint evolution analysis to establish node update data; after associating the node update data with data of the previous chain node, writing the node update data into the mapped storage space, and performing carbon footprint tracking management according to the two-dimensional code identifier and the updated cycle storage chain; based on the environmental context parameters and the carbon emission data, performing carbon footprint evolution analysis to establish node update data, comprising: synchronously sending the environmental context parameters and the carbon emission data to a carbon footprint dynamic evolution network; performing data conversion of the environmental context parameters through a preprocessing layer of the carbon footprint dynamic evolution network, establishing a carbon footprint evolution operator, the carbon footprint evolution operator comprising a space migration operator, a flow operator, an energy mixing operator and a time series disturbance operator; sending the carbon footprint evolution operator and the carbon emission data to a calculation layer to perform dynamic transformation processing of the carbon emission data, and establishing node update data according to a dynamic transformation processing result; using a space processing sub-layer in the calculation layer to perform iterative operation on the carbon emission data and a space migration operator corresponding to the location parameter to establish a first dynamic correction value; using a transportation processing sub-layer in the calculation layer to process the first dynamic correction value and a flow operator corresponding to the transportation mode to establish a second dynamic correction value; using an energy mixing sub-layer in the calculation layer to process the second dynamic correction value and an energy mixing operator corresponding to the energy composition to establish a third dynamic correction value; using a confidence calculation layer in the calculation layer to calculate a confidence degree of a data source according to the time series disturbance operator, and weighting the third dynamic correction value according to the confidence degree to establish a dynamic transformation result.

2. The product carbon footprint tracking method with the aid of the two-dimensional code traceability as claimed in claim 1, wherein, The reading of the environmental context parameters of the scanning node comprises: activating an Internet of Things device according to the write instruction, collecting the environmental context parameters by using the Internet of Things device to establish a collection result; activating an edge computing node, and performing data preprocessing of the collection result according to the edge computing node; uploading the collection result after the data preprocessing to a storage space under a current scanning node as the environmental context parameters.

3. The product carbon footprint tracking method with the aid of the two-dimensional code traceability as claimed in claim 1, wherein, The carbon footprint tracking management according to the two-dimensional code identifier and the updated cycle storage chain comprises: if a second authority triggers the two-dimensional code identifier, generating a read-only instruction; performing cycle storage chain packaging according to the read-only instruction, and performing carbon footprint visual expression.

4. The product carbon footprint tracking method with the aid of the two-dimensional code traceability as claimed in claim 1, wherein, The write instruction is generated if the first authority triggers the two-dimensional code mark in any life cycle. It is judged whether the life cycle is a product quality detection cycle. If the life cycle is a product quality detection cycle, a cycle verification instruction is generated. The target product quality of the current node is verified according to the cycle verification instruction. If the quality verification result is a pass result, the cycle storage chain is retained, and the carbon emission data is updated according to the write instruction.

5. The product carbon footprint tracking method with the aid of a two-dimensional code traceability as claimed in claim 4, wherein, The target product quality of the current node is verified according to the cycle verification instruction. If the quality verification result is a fail result, a chain reset instruction of the cycle storage chain is generated. The cycle storage chain is reset to a scrap storage chain according to the chain reset instruction. The carbon emission data is updated according to the scrap storage chain.

6. The product carbon footprint tracking method with the aid of the two-dimensional code traceability as claimed in claim 1, wherein, After the write instruction is generated, the following steps are included: A cloud platform synchronization instruction is generated according to the write instruction. The storage data in the cycle storage chain is uploaded to the cloud platform based on the cloud platform synchronization instruction, and a carbon footprint report is constructed in the cloud platform. The target product carbon footprint tracking management is performed according to the carbon footprint report of the cloud platform.

7. A product carbon footprint tracking system assisted by QR traceability, characterized by, The system for realizing the product carbon footprint tracking method assisted by the two-dimensional code traceability according to any one of claims 1-6 includes: A chain creation module: obtaining the full life cycle of the target product, and creating a cycle storage chain according to the full life cycle, wherein the cycle storage chain is configured with a storage space one-to-one mapped with the full life cycle, and the storage space is a one-time write space; An identification binding module: generating a unique two-dimensional code mark of the target product when the target product is in the manufacturing process, and binding the two-dimensional code mark with the cycle storage chain; An identification triggering module: generating a write instruction if a first authority triggers the two-dimensional code mark in any life cycle; A data reading module: reading the environmental context parameters of the scanning node after calling the carbon emission data of the previous chain node in the cycle storage chain according to the write instruction, wherein the environmental context parameters include location data, transportation mode, energy composition, and real-time carbon emission factor; An evolution analysis module: performing carbon footprint evolution analysis based on the environmental context parameters and the carbon emission data, and establishing node update data; A tracking management module: writing the node update data into the mapped storage space after associating the node update data with the data of the previous chain node, and performing carbon footprint tracking management according to the two-dimensional code mark and the updated cycle storage chain. 8.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to realize the steps of the product carbon footprint tracking method assisted by the two-dimensional code traceability according to any one of claims 1-6.

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