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CN-121599681-B - Global sustainable supply chain authentication method and equipment

CN121599681BCN 121599681 BCN121599681 BCN 121599681BCN-121599681-B

Abstract

The application discloses a global sustainable supply chain authentication method and equipment, belonging to the technical field of data management, wherein the method comprises the steps of carrying out GIS positioning analysis on a production place and generating a geofence area; the method comprises the steps of calculating sustainable quantitative index data of a geofence area, obtaining activity data and emission factors with reliability grade marks from a plurality of data acquisition channels with reliability grade, calculating life cycle carbon emission of a product, determining probability distribution models and uncertainty parameters respectively followed by the activity data and the emission factors according to preset mapping relations between the reliability grade and the probability distribution models to generate a confidence interval of the life cycle carbon emission, and carrying out sustainable standard compliance verification on the sustainable quantitative index data and the life cycle carbon emission according to the confidence interval. The technical problems of insufficient geographic precision, inaccurate carbon emission accounting and unquantifiable result credibility in the traditional authentication are solved.

Inventors

  • TIAN YAJUN
  • WANG CHANGFANG
  • SUN WANYI
  • JI XING
  • SUN LIANG

Assignees

  • 中国科学院青岛生物能源与过程研究所

Dates

Publication Date
20260512
Application Date
20260127

Claims (10)

  1. 1. A global sustainable supply chain authentication method, the method comprising: Performing GIS positioning analysis on the production place of the product in the supply chain to generate a geofence area; calculating sustainable quantitative index data of the geofence area based on multi-source remote sensing data and a GIS-LCA space analysis method; Acquiring activity data and emission factors with credibility grade identifiers from a plurality of data acquisition channels with credibility grades; calculating life cycle carbon emissions of the product using an LCA carbon accounting model; Aiming at the activity data and the emission factors with the credibility level marks, determining a probability distribution model and corresponding uncertainty parameters respectively followed by the activity data and the emission factors according to a preset mapping relation between the credibility level and the probability distribution model so as to generate a confidence interval of life cycle carbon emission through Monte Carlo simulation; according to the confidence interval, carrying out sustainability standard compliance verification on the sustainability quantization index data and life cycle carbon emission; during a non-transportation link, acquiring activity data with a credibility grade identifier from a plurality of data acquisition channels with credibility grades, wherein the method specifically comprises the following steps: Acquiring direct monitoring data of a geographic position, and acquiring activity data with a first credibility level identifier when the direct monitoring data accords with a first preset effective condition; When the first preset effective condition is not met, acquiring operation business data of the geographic position; When the operation service data accords with a second preset effective condition, obtaining activity data with a second credibility grade mark; when the second preset effective condition is not met, geographic image data and production space related geographic feature data in the geographic fence area are obtained; evaluating production activity of a product in the geofence area according to the geoimage data; estimating according to the production activity and the geographic feature data to obtain activity data with a third credibility level mark; and according to the confidence interval, carrying out sustainability standard compliance verification on the sustainability quantization index data and the life cycle carbon emission, wherein the method specifically comprises the following steps of: When the upper limit of the confidence interval of the life cycle carbon emission is smaller than a preset threshold value, extracting weather prediction data of the geofence area in a future time length from a global weather change prediction model; Predicting a climate risk level of the geofence area over a future time period based on the climate prediction data; compensating a preset emission threshold according to the climate risk level, wherein the climate risk level and the compensation coefficient form a negative correlation; And when the forest cutting history is not available, the carbon emission is smaller than the preset compensation emission threshold value and the social risk level is lower than the preset level, determining that the product sustainability standard compliance verification is passed.
  2. 2. The method of claim 1, wherein the GIS location analysis is performed on the production site of the product in the supply chain to generate the geofence area, specifically comprising: Acquiring geometric center point coordinates or accurate geographic coordinates of the production place, and determining the coordinates as seed points of a Voronoi algorithm; Dividing the production place into Voronoi polygons to obtain an initial geofence area; superposing hexagonal grids on the initial geofence area, and calculating the intersecting area ratio of each hexagonal grid and the polygon; A hexagonal grid having a significant spatial overlap with the polygon is selected to determine a geofence area, the significant spatial overlap being determined by comparing the ratio of intersecting areas to a preset ratio threshold.
  3. 3. The method according to claim 1, wherein calculating the sustainability quantization index data for the geofence area when the sustainability quantization index is a forest cut history, specifically comprises: respectively calculating normalized vegetation indexes of each pixel in satellite images and current time images of the geofence area in a preset historical time to obtain an NDVI distribution diagram corresponding to each image; extracting an NDVI value on each historical image aiming at each appointed pixel in the NDVI distribution diagram of the current time image to obtain an NDVI time sequence of each appointed pixel in the NDVI distribution diagram of the current time image; analyzing the NDVI time sequence of each specified pixel according to a preset forest cutting detection network model to obtain a forest cutting marking result of each specified pixel; if the proportion of the area marked as the felling pixel to the total area of the geofence area is larger than a preset area proportion threshold value, determining that the forest felling index data is a damaged forest; and if the ratio of the area marked as the felling pixel to the total area of the geofence area is smaller than or equal to a preset area ratio threshold value, determining that the forest felling index data is the non-destroyed forest.
  4. 4. The method according to claim 1, wherein when the sustainability quantization index is a social risk, calculating sustainability quantization index data for the geofence area, specifically comprises: smoothly analyzing social risk data of a minimum administrative unit where a geofence area is located by using a nuclear density estimation method, and generating a social risk density surface distribution map; Performing spatial correlation analysis on the social risk density surface distribution map to identify statistically significant hot spot areas and generate a social risk thermodynamic diagram; And analyzing the geofence area according to the social risk thermodynamic diagram to obtain the social risk grade of the production place.
  5. 5. The method of claim 4, wherein the step of smoothly analyzing the social risk data of the smallest administrative unit of the geofence area by using a nuclear density estimation method to generate a social risk density surface distribution map specifically comprises: The population and labor index data are weighted and summed to obtain a comprehensive index, the comprehensive index is used as the weight of the center point of the minimum administrative unit where the geofence area is located, and the center point of the administrative unit is used as the center of the kernel function for weighted kernel density estimation to obtain a population and labor risk density surface map; taking the occurrence place of each negative news event in the news public opinion data as the center of a kernel function to carry out weighted kernel density estimation, and obtaining a public opinion risk density surface map; taking the occurrence place of each historical risk event as the center of a kernel function to carry out weighted kernel density estimation to obtain a historical risk density surface map; And generating a social risk density surface distribution map according to at least one of the population and labor risk density surface map, the public opinion risk density surface map and the historical risk density surface map.
  6. 6. The method according to claim 4, wherein the spatial correlation analysis of the social risk density surface profile specifically comprises: Converting the social risk density surface distribution map into grid data; determining a space weight matrix corresponding to the grid data based on a preset grid distance or an adjacency relation; According to the space weight matrix, getis-Ord Gi statistics of each grid are calculated to obtain Z scores and P values of each grid; And classifying the grids according to the Z scores and the P values, and identifying significant hot spot areas and cold spot areas.
  7. 7. The method according to claim 1, wherein the acquiring of the emission factor with the confidence level identification from the plurality of data acquisition channels of the confidence level during the non-transportation link specifically comprises: Matching the emission factor type of the geofence area in a localized emission factor database of an administrative area or localized geographic unit to which the production area belongs; If the matching is successful, acquiring an emission factor with a first credibility grade identifier; If the matching fails, acquiring characteristic parameters of an administrative region to which the production place belongs, and correcting standard emission factors of an upper-level administrative region to which the production place belongs according to the characteristic parameters, wherein the characteristic parameters comprise at least one of an energy structure, an industrial composition and a climate zone type; if the correction is successful, obtaining an emission factor with a second credibility level mark; and if the correction fails, adopting an average factor in a general standard database to obtain an emission factor with a third credibility grade mark.
  8. 8. The method according to claim 1, wherein the method further comprises: Creating and registering a digital identity for each participating node of the supply chain; receiving material data digitally signed by each participating node and recording the verified data as a transaction record to a distributed ledger to generate a material balance data chain; determining at least one of sustainable quantization index data, life cycle carbon emission data and geographic information of a geofence area as a sustainable file, calling a first intelligent contract deployed on a blockchain, wherein the first intelligent contract is configured to automatically check digital signature validity and data format compliance of the sustainable file, and after verification is passed, recording and linking a hash value and metadata of the file; after the sustainability standard compliance verification of the product passes, invoking a second intelligent contract deployed on the blockchain, wherein the second intelligent contract is configured to collect on-chain certification records related to product certification, the records comprise material balance chain records, sustainability file hash and compliance verification conclusion, and generate a unique certification evidence chain identifier and a certification digital certificate comprising the certification evidence chain identifier, the product information and the certification conclusion; and submitting the authentication evidence chain identifier and the hash value of the authentication digital certificate to an external trusted certification system for certification.
  9. 9. The method of any of claims 1-8, further comprising generating a traceback digital certificate, the traceback digital certificate comprising: Product identification information for uniquely identifying a particular product lot or data set of products; a center estimate of the life cycle carbon emissions, the center estimate being a mean or median of a carbon emission probability distribution generated by monte carlo simulation; a confidence interval characterizing the uncertainty of the center estimate, the confidence interval generated by: assigning credibility levels to activity data and emission factors from different data acquisition channels; Determining uncertainty parameters based on probability distribution models followed by the confidence levels; uncertainty propagation analysis is performed through Monte Carlo simulation, and a confidence interval is generated.
  10. 10. A global sustainable supply chain authentication device, comprising: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores instructions executable by the at least one processor to enable the at least one processor to perform a global sustainable supply chain authentication method according to any one of claims 1 to 9.

Description

Global sustainable supply chain authentication method and equipment Technical Field The application relates to the technical fields of carbon emission accounting, data management and data management, in particular to a global sustainable supply chain authentication method and equipment. Background With the global increasing concern over climate change and sustainable development. Various types of sustainability authentication standards (e.g., ISCC, RSPO, etc.) play an important role in supply chain management. International sustainability and carbon certification systems have become one of the widely accepted standards in the biomass and bioenergy fields. However, the conventional authentication method has the technical limitations that a geographic information system is used for supply chain traceability, but is limited to simple path visualization, the geographic space analysis precision is insufficient, the accurate positioning of a production place is difficult to realize, the average value of an area or a country is often used for life cycle carbon accounting, and the real environment influence of a specific production place cannot be accurately reflected. In addition, in the current accounting practice, the data sources, boundary assumptions and processing methods are opaque, and the accounting results of different institutions on the same product are greatly different, so that space is provided for green washing behaviors of selectively using beneficial data. Disclosure of Invention In order to solve the problems, the application provides a global sustainable supply chain authentication method, which comprises the steps of carrying out GIS positioning analysis on production places of products in a supply chain to generate a geofence area, calculating sustainable quantitative index data of the geofence area based on multi-source remote sensing data and a GIS-LCA space analysis method, acquiring activity data and emission factors with credibility grade identifiers from a plurality of data acquisition channels with credibility grading, calculating life cycle carbon emission of the products by utilizing an LCA carbon accounting model, determining a probability distribution model and uncertainty parameters, which are respectively followed by the activity data and the emission factors, according to the credibility grade corresponding to the activity data and the emission factors respectively, and determining a confidence interval of the life cycle carbon emission by Monte Carlo simulation according to a preset mapping relation between the credibility grade and the probability distribution model, and carrying out sustainability standard compliance verification on the sustainable quantitative index data and the life cycle carbon emission according to the confidence interval. In another aspect, an embodiment of the present application provides a global sustainable supply chain authentication device comprising at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a global sustainable supply chain authentication method as described in any one of the preceding claims. The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects: By performing GIS location analysis on the production site and generating a geofence area, the analysis unit is accurate from a macroscopic area to a microscopic geographic grid. The method ensures that the activity data and the emission factors which are continuously and quantitatively analyzed and extracted in the follow-up process can be accurately and spatially bound with specific production activities, and the error of generalization of the geographic position range is eliminated from the source. And establishing a credibility grading data acquisition channel. The system can intelligently select the optimal available data source and assign an explicit credibility level identifier to the data. The mechanism is used for orthoscopic and systematic management of data quality differences existing in the observers in the global supply chain, and under the reality that the data are incomplete, a reliable carbon footprint accounting result is produced as much as possible through a layered, degradable and transparent quantized data management and calculation framework, and the transparency of the accounting process and the traceability of the data are greatly improved. Drawings In order to more clearly illustrate the technical solution of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which: Fig. 1 is a flowchart of a global sustainable supply chain authentication method according to an embodiment of the present application. Detailed Description In order to ma