A method and system for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical companies

Abstract

This invention belongs to the field of intelligent carbon footprint monitoring technology, and provides a method and system for monitoring the carbon footprint of traditional Chinese medicine (TCM) pharmaceutical companies. The invention uses a data acquisition robot module to collect historical and monthly data on TCM preparations. A data governance robot module preprocesses the collected data, performs standard carbon footprint calculations for TCM preparations, and calculates monthly plant-wide carbon emissions. A data analysis robot module identifies key carbon emission links, predicts monthly carbon emission sub-items, monitors abnormal carbon emission sub-items, and diagnoses carbon footprint anomalies. A human-computer interaction robot module provides a visual interface for the collected data, key carbon emission links, predicted monthly carbon emission sub-items, abnormal carbon emission sub-items, and carbon footprint anomaly diagnosis results. This invention enables TCM pharmaceutical companies to promptly monitor abnormal carbon footprints and abnormal carbon emission events, facilitating low-carbon operations and helping companies manage their carbon emissions.

Description

Technical Field

[0001] This invention belongs to the field of intelligent carbon footprint monitoring technology, specifically relating to a method and system for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical companies. Background Technology

[0002] In recent years, the traditional Chinese medicine (TCM) pharmaceutical industry, as an important branch of manufacturing, has occupied a pivotal position in my country's national economy and social development. However, this traditional and distinctive industry also faces challenges such as high energy consumption and large emissions. Its production process encompasses multiple stages, including the cultivation of medicinal herbs, processing of medicinal slices, preparation production, and product distribution and sales. Especially in the industrial production of TCM, the consumption of electricity, heat, and organic solvents is significant, while also generating large amounts of wastewater, waste residue, and high carbon emissions. Traditional carbon footprint accounting methods mainly rely on manual data collection and analysis, which is not only time-consuming and labor-intensive but also prone to data omissions and errors, making it difficult to meet the needs of modern enterprises for efficient and accurate carbon emission management.

[0003] Currently, some research and applications have attempted to improve carbon emission monitoring through information technology. For example, patent document CN114358668A proposes a carbon footprint big data analysis method and system based on industrial internet identifiers. This system can acquire the industrial internet identifier of a product, parse the target identifier to obtain the target storage address, then obtain product information, calculate the product's carbon footprint, and generate a carbon footprint report. Patent document CN117151960A proposes a big data-driven carbon footprint assessment system. This system includes a product lifecycle management module, a data acquisition module, an analysis and assessment module, and a result display module. It can record the carbon emission stages of a product throughout its entire lifecycle, collect carbon emission data, and conduct assessments.

[0004] Although these patents propose different technical solutions for monitoring and calculating carbon footprints, there are still some shortcomings: (1) lack of comprehensiveness and accuracy in data collection, unable to fully cover all relevant carbon emission sources, or the accuracy of data collection needs to be improved; (2) insufficient real-time performance, making it difficult to meet the demand for real-time data in the production process; (3) insufficient system integration and compatibility capabilities. Summary of the Invention

[0005] This invention aims to address the problems existing in the prior art by providing a carbon footprint monitoring method and system for Chinese medicine pharmaceutical companies. This system can comprehensively reflect the carbon emission situation of enterprises, enabling Chinese medicine pharmaceutical companies to promptly monitor abnormal carbon footprints and abnormal carbon emission events, thus helping them maintain low-carbon operations in the long term, reduce carbon emissions, and better manage their carbon emissions.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0007] A method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical companies includes the following steps:

[0008] S1. Historical data and monthly data collection of traditional Chinese medicine preparations;

[0009] S2. Preprocess the collected historical and monthly data of traditional Chinese medicine preparations;

[0010] S3. Obtain pre-processed historical data of traditional Chinese medicine preparations, perform standard carbon footprint accounting for traditional Chinese medicine preparations, and perform monthly carbon emission accounting for the entire plant.

[0011] S4. Obtain pre-processed monthly data of traditional Chinese medicine preparations, calculate the impact of improvements in different stages of traditional Chinese medicine preparations on overall carbon emissions based on the standard carbon footprint of traditional Chinese medicine preparations, rank different stages according to the degree of impact, and identify stages with a degree of impact greater than the preset standard as key carbon emission stages.

[0012] S5. Construct a decision tree model. Use the data obtained in steps S2 and S4 to train the decision tree model and use the decision tree model to obtain the monthly carbon emission sub-item prediction values.

[0013] S6. Based on the monthly carbon emission sub-item forecast values, perform carbon footprint anomaly diagnosis using the deviation model principle.

[0014] Preferably, in step S1, the collected historical data of traditional Chinese medicine preparations includes energy consumption data for the procurement, sales and transportation of each traditional Chinese medicine preparation, energy consumption data for storage, energy consumption data for processing, and carbon emission factors of different types of energy; the collected monthly data of traditional Chinese medicine preparations includes carbon emission-related items and monthly production data of each traditional Chinese medicine preparation product; the historical data and monthly data of traditional Chinese medicine preparations are collected from the information system of traditional Chinese medicine pharmaceutical companies.

[0015] Preferably, in step S2, the preprocessing of the collected historical data and monthly data on traditional Chinese medicine preparations includes:

[0016] S201. Clean the collected historical and monthly data of traditional Chinese medicine preparations. The cleaning method is to desensitize, remove duplicates, and fill in missing values ​​in the collected historical and monthly data of traditional Chinese medicine preparations.

[0017] S202. Standardize and organize the cleaned historical and monthly data of traditional Chinese medicine preparations. The standardization and organization method is to associate, match and aggregate historical and monthly data of traditional Chinese medicine preparations from different sources.

[0018] S203. Conduct quality assessment on the standardized and organized historical and monthly data of traditional Chinese medicine preparations. The quality assessment method is to use a quality assessment mechanism to check and manage the quality of the historical and monthly data of traditional Chinese medicine preparations.

[0019] Preferably, in step S3, energy consumption data for procurement and sales transportation, energy consumption data for storage, energy consumption data for processing, and carbon emission factors for different types of energy are obtained, and the carbon emission factor method is used to calculate the standard carbon footprint of traditional Chinese medicine.

[0020] Preferably, in step S3, the carbon footprint calculation of traditional Chinese medicine standards specifically includes:

[0021] S301. Calculate the carbon emissions of each type of traditional Chinese medicine in the procurement, sales and transportation process;

[0022] S302. Calculate the carbon emissions during the storage of each type of traditional Chinese medicine.

[0023] S303. Calculate the carbon emissions of each type of traditional Chinese medicine processing step;

[0024] S304. Calculate the standard carbon footprint of each traditional Chinese medicine preparation based on the carbon emissions from the procurement, sales, and transportation stages, the storage stage, and the processing stage.

[0025] Preferably, in step S3, the monthly plant-wide carbon emission accounting specifically involves calculating the monthly plant-wide carbon emission amount based on carbon emission-related items, whereby the carbon emission-related items include the actual carbon emission amount from product transportation, the actual carbon emission amount from product processing, and the actual carbon emission amount from product storage.

[0026] Preferably, in step S4, the monthly data for traditional Chinese medicine products includes the number of product categories of traditional Chinese medicine products in a month, the product batch size corresponding to each product category, each batch of each product category, as well as the number of product categories, product batch size, actual carbon emissions from product transportation, actual carbon emissions from product processing, actual carbon emissions from product storage, and standard carbon emissions.

[0027] Preferably, in step S5, the preprocessed historical data and monthly data of traditional Chinese medicine prepared in step S2, the standard carbon footprint of traditional Chinese medicine prepared in step S3 and the monthly carbon emissions of the whole plant, and the key carbon emission links obtained in step S4 are input into the decision tree model to train the decision tree model and use the decision tree model to obtain the monthly carbon emission sub-item prediction values.

[0028] Preferably, in step S6, the decision tree model trained in step S5 is used to obtain the monthly carbon emission prediction value based on the number of product categories of traditional Chinese medicine products in the current month, the product batches corresponding to each product category, and each batch of each product category. The prediction value is compared with the standard carbon emission amount to calculate the total monitoring value. When the total monitoring value exceeds the set threshold, the monthly carbon emission sub-item prediction value is compared with the sub-item standard carbon emission amount to calculate the sub-item monitoring value. When the sub-item monitoring value exceeds the set sub-item threshold, it is recorded as an anomaly in the carbon emission sub-item.

[0029] The present invention also provides a carbon footprint monitoring system for traditional Chinese medicine pharmaceutical enterprises. The system uses the carbon footprint monitoring method for traditional Chinese medicine pharmaceutical enterprises as described above, and includes: a data acquisition robot module, a data governance robot module, a data analysis robot module, and a human-computer interaction robot module.

[0030] The data acquisition robot module is used to collect historical and monthly data on traditional Chinese medicine preparations.

[0031] The data governance robot module is used to preprocess the collected historical and monthly data of traditional Chinese medicine preparations, and to perform standard carbon footprint accounting for traditional Chinese medicine preparations and monthly carbon emission accounting for the entire plant.

[0032] The data analysis robot module is used to acquire pre-processed monthly data of traditional Chinese medicine, identify key carbon emission links, predict monthly carbon emission sub-items, monitor abnormal carbon emission sub-items, and perform carbon footprint anomaly diagnosis.

[0033] The human-computer interaction robot module is used to visualize the collected historical data of traditional Chinese medicine, monthly data of traditional Chinese medicine, key carbon emission links, monthly carbon emission sub-item prediction values, abnormal carbon emission sub-items and carbon footprint anomaly diagnosis results, and generate a visual user interface.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] (1) This invention can collect basic data from each production link of Chinese medicine pharmaceutical enterprises and analyze and optimize it through decision tree model, which greatly reduces manual intervention and improves work efficiency;

[0036] (2) The present invention uses decision tree model and data analysis technology to obtain carbon emission prediction values ​​with high accuracy and operability, which can comprehensively reflect the carbon emission situation of enterprises;

[0037] (3) This invention uses a trained decision tree model to predict carbon emissions and monitors abnormal carbon emission items of traditional Chinese medicine pharmaceutical companies to diagnose abnormal carbon footprints, thereby achieving continuous improvement in carbon emission management. Traditional Chinese medicine pharmaceutical companies can pay attention to abnormal carbon footprints and abnormal carbon emission items in a timely manner, which helps them maintain low-carbon operation in the long term, reduce carbon emissions, and achieve a win-win situation for both economic and environmental benefits.

[0038] (4) This invention provides a visual user interface, which makes it convenient for users to view historical data of traditional Chinese medicine, monthly data of traditional Chinese medicine, key carbon emission links, monthly carbon emission sub-item prediction values, abnormal carbon emission sub-items and carbon footprint abnormal diagnosis results at any time, so as to help users better manage the carbon emissions of enterprises. Attached Figure Description

[0039] Figure 1 This is a flowchart of a method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical companies according to an embodiment of the present invention;

[0040] Figure 2 This is a technical roadmap for a carbon footprint monitoring system for traditional Chinese medicine pharmaceutical companies, as described in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1

[0043] Combination Figure 1 As shown, this embodiment of the invention provides a method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical companies, including the following steps:

[0044] S1. Historical data and monthly data collection of traditional Chinese medicine preparations;

[0045] S2. Preprocess the collected historical and monthly data of traditional Chinese medicine preparations;

[0046] S3. Obtain pre-processed historical data of traditional Chinese medicine preparations, perform standard carbon footprint accounting for traditional Chinese medicine preparations, and perform monthly carbon emission accounting for the entire plant.

[0047] S4. Obtain pre-processed monthly data of traditional Chinese medicine preparations, calculate the impact of improvements in different stages of traditional Chinese medicine preparations on overall carbon emissions based on the standard carbon footprint of traditional Chinese medicine preparations, rank different stages according to the degree of impact, and identify stages with a degree of impact greater than the preset standard as key carbon emission stages.

[0048] S5. Construct a decision tree model. Use the data obtained in steps S2 and S4 to train the decision tree model and use the decision tree model to obtain the monthly carbon emission sub-item prediction values.

[0049] S6. Based on the monthly carbon emission sub-item forecast values, perform carbon footprint anomaly diagnosis using the deviation model principle.

[0050] Example 2

[0051] Based on Example 1, in this example, the historical data of traditional Chinese medicine (TCM) collected in step S1 includes energy consumption data for the procurement, sales, and transportation of each TCM product, energy consumption data for storage, energy consumption data for processing, and carbon emission factors for different types of energy. The monthly data of TCM collected includes carbon emission-related items and monthly production data for each TCM product. The historical and monthly data of TCM are collected from the information systems of TCM pharmaceutical companies. Specifically, the information systems mainly include EMS systems, EF databases, eAM systems, TMS systems, CRM systems, ERP systems, MES systems, WMS systems, SRM systems, SCADA systems, etc.

[0052] For the common information systems of traditional Chinese medicine pharmaceutical companies, the collected data mainly comes from various data sources closely related to carbon footprint accounting. For example, Table 1 shows some commonly used tables and their data fields, as well as possible data management source systems:

[0053] Table 1

[0054]

[0055] Furthermore, in step S2, the preprocessing of the collected historical data and monthly data on traditional Chinese medicine preparations includes:

[0056] S201. Clean the collected historical and monthly data of traditional Chinese medicine preparations. The cleaning method is to desensitize, remove duplicates, and fill in missing values ​​in the collected historical and monthly data of traditional Chinese medicine preparations.

[0057] S202. Standardize and organize the cleaned historical and monthly data of traditional Chinese medicine preparations. The standardization and organization method is to associate, match and aggregate historical and monthly data of traditional Chinese medicine preparations from different sources.

[0058] S203. Conduct quality assessment on the standardized and organized historical and monthly data of traditional Chinese medicine preparations. The quality assessment method is to use a quality assessment mechanism to check and manage the quality of the historical and monthly data of traditional Chinese medicine preparations.

[0059] Example 3

[0060] Based on Example 2, in this example, in step S3, data on energy consumption for procurement, sales transportation, storage, and processing of Chinese patent medicines, as well as carbon emission factors for different types of energy, are obtained. Specifically, the energy consumption data for procurement and sales transportation include the diesel energy consumption and gasoline energy consumption generated in the procurement and sales transportation links of each Chinese patent medicine. The energy consumption data for storage include the electricity energy consumption generated in the storage link of each Chinese patent medicine. The energy consumption data for processing include the electricity energy consumption, water energy consumption, and raw material consumption generated in the processing link of each Chinese patent medicine. The carbon emission factors for different types of energy include the carbon emission factor of diesel, the carbon emission factor of gasoline, the carbon emission factor of electricity, the carbon emission factor of water energy, and the carbon emission factor of raw materials;

[0061] The carbon emission factor method is used to calculate the standard carbon footprint of Chinese patent medicines. Specifically, the formula for the carbon emission factor method is:

[0062] ,

[0063] where, represents the carbon emissions, represents the energy consumption data, represents the carbon emission factor;

[0064] Since it is difficult to obtain carbon footprint information for upstream raw material production and downstream drug use, and it is also difficult to apply optimization control, the scope of carbon footprint accounting and optimization is generally limited to the range from material procurement transportation, in-plant storage, in-plant processing to product sales transportation. The entire production process of Chinese patent medicines from raw material acquisition to product delivery is mapped in detail. Each step in the process is identified, including raw material procurement, processing, packaging, transportation, distribution, etc. Energy consumption data for each process step are collected, including electricity, water, steam, fuel, etc. Transportation data for raw materials and products are collected, including transportation mode, distance, load, etc. The carbon emission factors for each type of energy and raw material are determined, and these factors can be based on IPCC guidelines or industry standards. For the transportation link, the carbon emission factors for different transportation modes are determined. Using the collected data and carbon emission factors, the carbon emissions for each process step are calculated. Further, the carbon emissions of the entire value stream are aggregated to obtain the total carbon footprint of the product. The established standard carbon footprint of Chinese patent medicines can be used as a basis for carbon emission optimization and can also serve as a benchmark for future comparison and improvement;

[0065] Furthermore, for the calculation of the standard carbon footprint of Chinese patent medicines, it specifically includes:

[0066] S301. Calculate the carbon emissions in the procurement and sales transportation links of each Chinese patent medicine. The specific calculation formula is as follows:

[0067] ,

[0068] in, This indicates the carbon emissions during the procurement, sales, and transportation processes. This indicates the carbon emissions generated when diesel fuel is used in the transportation process. This indicates the carbon emissions generated when gasoline is used in the transportation process.

[0069] ,

[0070] in, This indicates the amount of diesel energy consumed in the procurement, sales, and transportation processes. Indicates the carbon emission factor of diesel fuel.

[0071]

[0072] in, This indicates the amount of gasoline energy consumed in the procurement, sales, and transportation processes. Indicates the carbon emission factor of gasoline;

[0073] S302. Calculate the carbon emissions during the storage of each type of traditional Chinese medicine. The specific calculation formula is as follows:

[0074] ,

[0075] in, This indicates the carbon emissions during the storage process. This indicates the carbon emissions generated by the electricity consumed during the storage process. This indicates the amount of electricity consumed during the storage process due to the need for refrigerated storage of raw materials, intermediate products, and finished products. The carbon emission factor representing electricity;

[0076] S303. Calculate the carbon emissions of each type of traditional Chinese medicine processing step. The specific calculation formula is as follows:

[0077] ,

[0078] in, This indicates the carbon emissions of each stage of the processing of traditional Chinese medicine. This indicates the carbon emissions generated by the water energy consumption in each stage of traditional Chinese medicine processing. This indicates the carbon emissions generated by the electricity consumption during the processing of each type of traditional Chinese medicine. This indicates the carbon emissions generated by the consumption of all raw materials during the processing of each type of traditional Chinese medicine.

[0079] ,

[0080] in, This indicates the amount of water energy consumed during the processing of each type of traditional Chinese medicine. The carbon emission factor representing hydropower;

[0081] ,

[0082] in, This indicates the electrical energy consumption generated during the processing of each type of traditional Chinese medicine. The carbon emission factor representing electricity;

[0083] ,

[0084] in, Indicates the first The consumption of each raw material Indicates the first The carbon emission factor of each raw material;

[0085] S304. Based on the carbon emissions from the procurement, sales, and transportation stages, the storage stage, and the processing stage of each traditional Chinese medicine preparation, the standard carbon footprint of each preparation is calculated. The specific calculation formula is as follows:

[0086] ;

[0087] Furthermore, the monthly plant-wide carbon emission accounting specifically involves calculating the monthly plant-wide carbon emissions based on carbon emission-related items. These carbon emission-related items include actual product transportation carbon emissions, actual product processing carbon emissions, and actual product storage carbon emissions. Specifically, this can be calculated by tracking the real-time water energy consumption, real-time electricity energy consumption, gasoline consumption, diesel consumption, and raw material consumption at each stage of actual product transportation, processing, and storage, as shown in Table 2.

[0088] Table 2

[0089]

[0090] The formula for calculating the monthly carbon emissions of the entire plant is as follows:

[0091] ,

[0092] in, This represents the total actual carbon emissions of the entire plant for the month. Indicates actual monthly transportation Carbon emissions generated by each product Indicates actual monthly processing Carbon emissions generated by each product Indicates actual monthly storage Carbon emissions generated by each product Indicates product Carbon emissions generated during actual transportation Indicates product Carbon emissions generated during actual processing Indicates product Carbon emissions generated during actual storage Indicates product Carbon emission factors in actual transportation. Indicates product Carbon emission factors in actual processing Indicates product The carbon emission factor of carbon emissions in actual storage.

[0093] Example 4

[0094] Based on Example 2, in this example, in step S4, the monthly data of traditional Chinese medicine (TCM) products includes the number of product categories of TCM products in a month, the product batch size corresponding to each product category, the product category in each batch, as well as the number of product categories, product batch size, actual carbon emissions from product transportation, actual carbon emissions from product processing, actual carbon emissions from product storage, and standard carbon emissions. The pre-processed monthly data of TCM products is obtained, and the impact of improvements in different stages of TCM production on overall carbon emissions is calculated based on the standard carbon footprint of TCM products (e.g., Table 3). Different stages are ranked according to their impact, and stages with an impact greater than the preset standard are identified as key carbon emission stages. Priority is given to implementing carbon emission improvements for these key carbon emission stages.

[0095] Table 3

[0096]

[0097] Furthermore, in step S5, the preprocessed historical data and monthly data of traditional Chinese medicine prepared in step S2, the standard carbon footprint of traditional Chinese medicine prepared in step S3 and the monthly carbon emissions of the whole plant, and the key carbon emission links obtained in step S4 are input into the decision tree model to train the decision tree model and use the decision tree model to obtain the monthly carbon emission sub-item prediction values.

[0098] Specifically, the decision tree model is a classification and regression method based on a tree structure. Here, a regression variant of the decision tree is used to predict carbon emissions; the decision tree divides the data through a series of questions, with each internal node judging a feature. Based on the judgment result, the data is assigned to the next level node until the leaf node is reached, where the leaf node provides the predicted value.

[0099] The decision tree model inputs a data matrix X (e.g., Table 4) (monthly product production data).

[0100] Table 4

[0101]

[0102] The decision tree model prediction results data matrix Y (e.g., Table 5) (monthly carbon emission sub-item prediction values);

[0103] Table 5

[0104]

[0105] The random forest decision tree method is adopted, with the number of decision trees in the random forest set to 50. The Bootsrap sampling method is used to extract multiple subsamples from the original data, and a decision tree is trained for each subsample. The prediction results of all decision trees are combined to obtain the final prediction value.

[0106] During model training, different decision tree depths (such as 5, 10, 15, etc.) are set, and cross-validation is used to evaluate model performance. The depth with the best performance is selected as the final model depth. Different numbers of leaf nodes are set (such as 5, 10, 20, etc.), and cross-validation is used to evaluate model performance. The leaf nodes with the best performance are selected as the parameters of the final model. Different splitting criteria, such as Gini impurity and information gain, are used to evaluate model performance. The splitting criterion with the best performance is selected as the splitting criterion of the final model.

[0107] The training results of the decision tree model are as follows:

[0108] Training set shape: (46, 15)

[0109] Test set shape: (13, 15)

[0110] Optimize decision tree depth:

[0111] Depth 5: Average MSE = 2.3829

[0112] Depth 10: Average MSE = 2.3558

[0113] Depth 15: Average MSE = 2.3557

[0114] Depth 20: Average MSE = 2.3557

[0115] Depth 25: Average MSE = 2.3557

[0116] Depth None: Average MSE = 2.3557

[0117] Optimal depth: 15

[0118] Optimize the number of leaf nodes:

[0119] Minimum number of leaf nodes 1: Average MSE = 2.3557

[0120] Minimum number of leaf nodes 5: Average MSE = 4.8115

[0121] Minimum number of leaf nodes 10: Average MSE = 7.8475

[0122] Minimum number of leaf nodes 15: Average MSE = 13.2646

[0123] Minimum number of leaf nodes 20: Average MSE = 13.2646

[0124] Minimum number of leaf nodes 25: Average MSE = 13.2646

[0125] Optimal number of leaf nodes: 1

[0126] Optimize splitting criteria:

[0127] Split criterion squared error: Average MSE = 2.3557

[0128] Splitting criterion (friedman_mse): Average MSE = 2.4344

[0129] Split criterion absolute_error: Average MSE = 2.6747

[0130] Split criteria Poisson: Mean MSE = 2.7192

[0131] Optimal splitting criterion: squared_error

[0132] Training set performance evaluation:

[0133] Transportation carbon emissions summary - MSE: 0.1851, R2: 0.9668

[0134] Processing carbon emissions summary - MSE: 1.2104, R2: 0.9611

[0135] Storage carbon emissions summary - MSE: 0.0645, R2: 0.9691

[0136] Test set performance evaluation:

[0137] Summary of carbon emissions from transportation - MSE: 0.0563, R2: 0.9514

[0138] Processing carbon emissions summary - MSE: 0.6765, R2: 0.8686

[0139] Storage carbon emissions summary - MSE: 0.0348, R2: 0.9050

[0140] Further, in step S6, using the decision tree model trained in step S5, the monthly carbon emission prediction value is obtained based on the number of product categories of traditional Chinese medicine products in the current month, the product batch size corresponding to each product category, and each product category in each batch. The prediction value is compared with the standard carbon emission amount to calculate the total monitoring value. When the total monitoring value exceeds the set threshold, the monthly carbon emission sub-item prediction value is compared with the sub-item standard carbon emission amount to calculate the sub-item monitoring value. When the sub-item monitoring value exceeds the set sub-item threshold, it is recorded as an anomaly in that carbon emission sub-item. The specific calculation process is as follows:

[0141] ,

[0142] in, This represents the plant's predicted carbon emissions for the current month. It is a product Standard carbon emissions;

[0143] When the total monitored value exceeds the set threshold 'a', the individual monitored values ​​are calculated using the following formula:

[0144] ,

[0145] ,

[0146] ,

[0147] in, This represents the monthly forecast value for carbon emissions from transportation. This indicates the monthly standard carbon emissions for transportation. This represents the monthly carbon emission forecast for storage. This indicates the monthly standard carbon emissions from storage. This represents the monthly carbon emission forecast for processing. This represents the monthly standard carbon emissions during processing. The set threshold for the transportation monitoring value is b, the set threshold for the storage monitoring value is c, and the set threshold for the processing monitoring value is d.

[0148] The predicted values ​​for each carbon emission category are compared with their corresponding standard values ​​(e.g., Table 6). The difference between the predicted and standard values ​​for each category is calculated and recorded as the monitoring value for that category. When the monitoring value for a category exceeds the set threshold, it is considered an anomaly for that carbon emission category. The thresholds for categories b, c, and d are all set at 20%. When the difference for a category exceeds 20%, it is considered an anomaly for that category, and a detailed deviation investigation is conducted.

[0149] Table 6

[0150]

[0151] Example 5

[0152] Combination Figure 2 As shown, this embodiment of the invention also provides a carbon footprint monitoring system for traditional Chinese medicine pharmaceutical enterprises. The system uses the carbon footprint monitoring method for traditional Chinese medicine pharmaceutical enterprises as described above, including: a data acquisition robot module, a data governance robot module, a data analysis robot module, and a human-computer interaction robot module.

[0153] The data acquisition robot module is used to collect historical and monthly data on traditional Chinese medicine preparations.

[0154] The data governance robot module is used to preprocess the collected historical and monthly data of traditional Chinese medicine preparations, and to perform standard carbon footprint accounting for traditional Chinese medicine preparations and monthly carbon emission accounting for the entire plant.

[0155] The data analysis robot module is used to acquire pre-processed monthly data of traditional Chinese medicine, identify key carbon emission links, predict monthly carbon emission sub-items, monitor abnormal carbon emission sub-items, and perform carbon footprint anomaly diagnosis.

[0156] The human-computer interaction robot module is used to visualize the collected historical data of traditional Chinese medicine, monthly data of traditional Chinese medicine, key carbon emission links, monthly carbon emission sub-item prediction values, abnormal carbon emission sub-items and carbon footprint anomaly diagnosis results, and generate a visual user interface.

[0157] The above description is merely an embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical companies, characterized in that, Includes the following steps: S1. Historical and monthly data collection of traditional Chinese medicine (TCM) preparations; among which, historical data of TCM preparations includes energy consumption data for procurement, sales, transportation, storage, and processing of each TCM preparation, as well as carbon emission factors for different types of energy; monthly data of TCM preparations includes carbon emission-related items and monthly production data for each TCM preparation product; the source of historical and monthly data of TCM preparations is the information system of TCM pharmaceutical companies. S2. Preprocess the collected historical and monthly data of traditional Chinese medicine preparations, including: S201. Clean the collected historical and monthly data of traditional Chinese medicine preparations. The cleaning method is to desensitize, remove duplicates, and fill in missing values ​​in the collected historical and monthly data of traditional Chinese medicine preparations. S202. Standardize and organize the cleaned historical and monthly data of traditional Chinese medicine preparations. The standardization and organization method is to associate, match and aggregate historical and monthly data of traditional Chinese medicine preparations from different sources. S203. Conduct quality assessment on the standardized and organized historical and monthly data of traditional Chinese medicine preparations. The quality assessment method is to use a quality assessment mechanism to check and manage the quality of the historical and monthly data of traditional Chinese medicine preparations. S3. Obtain pre-processed historical data of traditional Chinese medicine preparations, perform standard carbon footprint accounting for traditional Chinese medicine preparations, and perform monthly carbon emission accounting for the entire plant. In step S3, energy consumption data for procurement and sales transportation, energy consumption data for storage, energy consumption data for processing, and carbon emission factors for different types of energy are obtained, and the carbon emission factor method is used to calculate the standard carbon footprint of traditional Chinese medicine. S4. Obtain pre-processed monthly data of traditional Chinese medicine preparations, calculate the impact of improvements in different stages of traditional Chinese medicine preparations on overall carbon emissions based on the standard carbon footprint of traditional Chinese medicine preparations, rank different stages according to the degree of impact, and identify stages with a degree of impact greater than the preset standard as key carbon emission stages. The monthly data for traditional Chinese medicine products includes the number of product categories, the batch size of each product category, the number of each product category in each batch, the number of product categories, the batch size, the actual carbon emissions from product transportation, the actual carbon emissions from product processing, the actual carbon emissions from product storage, and the standard carbon emissions. S5. Construct a decision tree model. Use the data obtained in steps S2 and S4 to train the decision tree model and use the decision tree model to obtain the monthly carbon emission sub-item prediction values. S6. Based on the monthly carbon emission sub-item forecast values ​​and the principle of the deviation model, perform carbon footprint anomaly diagnosis, including: Using the decision tree model trained in step S5, the monthly carbon emission prediction value is obtained based on the number of product categories of traditional Chinese medicine products in the current month, the product batches corresponding to each product category, and each batch of each product category. The prediction value is compared with the standard carbon emission amount to calculate the total monitoring value. When the total monitoring value exceeds the set threshold, the monthly carbon emission sub-item prediction value is compared with the sub-item standard carbon emission amount to calculate the sub-item monitoring value. When the sub-item monitoring value exceeds the set sub-item threshold, it is recorded as an anomaly in that carbon emission sub-item.

2. The method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical enterprises according to claim 1, characterized in that, In step S3, the carbon footprint accounting of traditional Chinese medicine standards specifically includes: S301. Calculate the carbon emissions of each type of traditional Chinese medicine in the procurement, sales and transportation process; S302. Calculate the carbon emissions during the storage of each type of traditional Chinese medicine. S303. Calculate the carbon emissions of each type of traditional Chinese medicine processing step; S304. Calculate the standard carbon footprint of each traditional Chinese medicine preparation based on the carbon emissions from the procurement, sales, and transportation stages, the storage stage, and the processing stage.

3. The method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical enterprises according to claim 2, characterized in that, In step S3, the monthly plant-wide carbon emission accounting specifically involves calculating the monthly plant-wide carbon emission amount based on carbon emission-related items, which include actual product transportation carbon emissions, actual product processing carbon emissions, and actual product storage carbon emissions.

4. The method for monitoring the carbon footprint of traditional Chinese medicine pharmaceutical enterprises according to claim 1, characterized in that, In step S5, the preprocessed historical data and monthly data of traditional Chinese medicine prepared in step S2, the standard carbon footprint of traditional Chinese medicine prepared in step S3 and the monthly carbon emissions of the whole plant, and the key carbon emission links obtained in step S4 are input into the decision tree model to train the decision tree model and use the decision tree model to obtain the monthly carbon emission sub-item prediction values.

5. A carbon footprint monitoring system for traditional Chinese medicine pharmaceutical companies, characterized in that, The carbon footprint monitoring method for traditional Chinese medicine pharmaceutical enterprises as described in any one of claims 1-4 includes: a data acquisition robot module, a data governance robot module, a data analysis robot module, and a human-computer interaction robot module; The data acquisition robot module is used to collect historical and monthly data on traditional Chinese medicine preparations. The data governance robot module is used to preprocess the collected historical and monthly data of traditional Chinese medicine preparations, and to perform standard carbon footprint accounting for traditional Chinese medicine preparations and monthly carbon emission accounting for the entire plant. The data analysis robot module is used to acquire pre-processed monthly data of traditional Chinese medicine, identify key carbon emission links, predict monthly carbon emission sub-items, monitor abnormal carbon emission sub-items, and perform carbon footprint anomaly diagnosis. The human-computer interaction robot module is used to visualize the collected historical data of traditional Chinese medicine, monthly data of traditional Chinese medicine, key carbon emission links, monthly carbon emission sub-item prediction values, abnormal carbon emission sub-items and carbon footprint anomaly diagnosis results, and generate a visual user interface.

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