A Supply Chain Carbon Footprint and Energy Efficiency Collaborative Assessment System and Method Based on Carbon Quotas

Abstract

This application provides a supply chain carbon footprint energy efficiency collaborative assessment system and method based on carbon quotas, belonging to the field of energy efficiency assessment technology. The system includes: a first energy efficiency assessment module acquiring first supply characteristic parameters and a first carbon footprint of a first link in the supply chain, calculating a first carbon profit / loss and a first energy efficiency score; a second link prediction module acquiring a second carbon quota of a second link, obtaining a second predicted supply characteristic parameter; a second energy efficiency assessment module processing the supply of the second link according to a second adjusted carbon quota, calculating a second carbon profit / loss and a second energy efficiency score based on the second adjusted carbon quota; and a compensation scoring module compensating for the second energy efficiency score to obtain a second compensated energy efficiency score, and performing a collaborative assessment calculation of carbon footprint energy efficiency for all links to obtain a collaborative energy efficiency score. This solves the technical problems of insufficient synergy and poor reliability in the prior art for collaborative management assessment results of carbon footprints across multiple links in the supply chain.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency assessment, and more particularly to a system and method for collaborative assessment of supply chain carbon footprint and energy efficiency based on carbon quotas. Background Technology

[0002] Driven by the global goal of carbon neutrality, the supply chain, as the core carrier of carbon emissions in socio-economic activities, has made the refined management of its carbon footprint a key indicator for enterprises to achieve low-carbon transformation. However, traditional supply chain carbon footprint assessment methods typically focus only on the carbon emissions and quota matching degree of a single link, neglecting the interconnections between various links in the supply chain. This neglect of the overall linkage effect of the supply chain leads to problems such as insufficient synergy and poor reliability in traditional assessment results. Summary of the Invention

[0003] This invention addresses the technical problems of insufficient coordination and poor reliability in the assessment results of carbon footprint collaborative management of multiple links in the supply chain in the existing technology, and provides a supply chain carbon footprint energy efficiency collaborative assessment system and method based on carbon quotas.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] In a first aspect, the present invention provides a supply chain carbon footprint and energy efficiency synergistic assessment system based on carbon quotas, comprising:

[0006] The first energy efficiency assessment module is used to obtain the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and to calculate the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link.

[0007] The second-stage prediction module is used to obtain the second carbon quota for the second stage, combine it with the first carbon profit and loss calculation to obtain the second adjusted carbon quota, and perform supply processing prediction for the second stage based on the first supply characteristic parameters and the second carbon quota to obtain the second predicted supply characteristic parameters.

[0008] The second energy efficiency assessment module is used to process the second stage of supply according to the second adjusted carbon quota, obtain the second actual supply characteristic parameters and the second carbon footprint, and calculate the second carbon profit and loss and the second energy efficiency score in combination with the second adjusted carbon quota.

[0009] The compensation scoring module is used to calculate a second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and to calculate a quota change coefficient based on the second adjusted carbon quota, to compensate the second energy efficiency score to obtain a second compensated energy efficiency score, and to continue to perform a carbon footprint energy efficiency collaborative assessment calculation for all links based on the second carbon profit and loss to obtain a collaborative energy efficiency score.

[0010] Secondly, this invention provides a method for synergistic assessment of supply chain carbon footprint and energy efficiency based on carbon quotas, including:

[0011] Obtain the first supply characteristic parameters and first carbon footprint of the first link in the supply chain, and calculate the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link.

[0012] Obtain the second carbon quota for the second stage, calculate the second adjusted carbon quota in conjunction with the first carbon profit and loss, and predict the supply processing of the second stage based on the first supply characteristic parameter and the second carbon quota to obtain the second predicted supply characteristic parameter.

[0013] The second stage of supply processing is carried out in accordance with the second adjusted carbon quota to obtain the second actual supply characteristic parameters and the second carbon footprint. The second carbon profit and loss and the second energy efficiency score are calculated in combination with the second adjusted carbon quota.

[0014] The second supply change coefficient is calculated based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and the quota change coefficient is calculated based on the second adjusted carbon quota. The second energy efficiency score is compensated to obtain the second compensated energy efficiency score. The carbon footprint energy efficiency synergy assessment calculation of all links is carried out based on the second carbon profit and loss to obtain the synergistic energy efficiency score.

[0015] The beneficial effects of this invention are:

[0016] Compared to existing technologies, this application first obtains the first supply characteristic parameters and first carbon footprint of the first link in the supply chain through a first energy efficiency assessment module. Combined with the first carbon quota of the first link, it calculates the first carbon profit / loss and first energy efficiency score, providing a reliable data foundation for subsequent dynamic adjustments and full-chain collaborative assessments. Second, it obtains the second carbon quota of the second link through a second-link prediction module. Combined with the first carbon profit / loss, it calculates the second adjusted carbon quota. Based on the first supply characteristic parameters and the second carbon quota, it predicts the supply processing of the second link, obtaining the second predicted supply characteristic parameters. The carbon profit / loss of the first link is transmitted to the second link through the second adjusted carbon quota, achieving dynamic optimization of quotas. Furthermore, it uses a machine learning model to predict the supply processing of the second link, providing a benchmark for subsequent actual difference compensation. Third, it processes the supply of the second link according to the second adjusted carbon quota through a second energy efficiency assessment module, obtaining the second actual supply characteristic parameters and second carbon footprint. Combined with the second adjusted carbon quota, it calculates the second carbon profit / loss and second energy efficiency score, completing the actual assessment of the second link. This provides key data for subsequent quota adjustments, enabling a complete collaborative closed loop for supply chain carbon management. Finally, the second supply change coefficient is calculated by the compensation scoring module based on the second predicted supply characteristic parameters and the second actual supply characteristic parameters, and the quota change coefficient is calculated based on the second adjusted carbon quota. The second energy efficiency score is then compensated to obtain the second compensated energy efficiency score. Based on the second carbon profit and loss, the carbon footprint energy efficiency collaborative assessment calculation of all links is carried out to obtain the collaborative energy efficiency score. This score can accurately reflect the true carbon efficiency of each link and measure the overall collaborative carbon management level of the supply chain.

[0017] Through the aforementioned technical solution, this application achieves optimized allocation of carbon resources along the supply chain by dynamically transmitting carbon quotas across different stages, allowing the carbon surplus or deficit of upstream stages to influence quota allocation in downstream stages. This breaks down the fragmentation of static quotas. Furthermore, it utilizes machine learning models to predict supply processing and combines supply-demand differences with quota adjustments for dual-dimensional correction, effectively mitigating fluctuation interference and ensuring the objectivity of single-stage energy efficiency scores. Ultimately, it transforms dispersed stage efficiency into quantifiable, chain-wide collaborative indicators through full-stage energy efficiency compensation scoring. This enhances the synergy and reliability of multi-stage carbon footprint assessment and provides enterprises with quantitative decision-making support for transitioning from single-stage optimization to a full-chain low-carbon transformation. Attached Figure Description

[0018] Figure 1 A schematic diagram of the structure of the supply chain carbon footprint and energy efficiency collaborative assessment system based on carbon quotas provided by the present invention;

[0019] Figure 2 This is a flowchart illustrating the supply chain carbon footprint and energy efficiency synergistic assessment method based on carbon quotas provided by the present invention.

[0020] In the attached diagram, the components represented by each number are as follows:

[0021] The first energy efficiency assessment module 11, the second stage prediction module 12, the second energy efficiency assessment module 13, and the compensation scoring module 14. Detailed Implementation

[0022] 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.

[0023] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0024] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0025] Example 1, as Figure 1 As shown, this embodiment of the invention provides a supply chain carbon footprint energy efficiency collaborative assessment system based on carbon quotas. The system includes: a first energy efficiency assessment module 11, a second link prediction module 12, a second energy efficiency assessment module 13, and a compensation scoring module 14.

[0026] The first energy efficiency assessment module 11 is used to obtain the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and to calculate the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link.

[0027] The supply chain is a multi-stage system in which carbon emissions and quota utilization affect each other. For example, the quotas saved in the first stage can be used to supplement the second stage. Therefore, it is necessary to collect and calculate carbon profit and loss and energy efficiency scores after the first stage to provide an initial benchmark for subsequent stages and ensure the consistency and accuracy of the whole chain assessment.

[0028] To address the aforementioned issues, this application obtains the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and calculates the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link.

[0029] Specifically, the first energy efficiency assessment module 11 in the system includes:

[0030] After the first stage of supply processing within the supply chain, test to obtain the first supply characteristic parameters and the first carbon footprint;

[0031] Obtain the first carbon allowance in the first stage;

[0032] Calculate the ratio of the first carbon allowance to the first carbon footprint as the first energy efficiency score;

[0033] Calculate the difference between the first carbon quota and the first carbon footprint as the first carbon profit or loss.

[0034] In this embodiment of the application, after the supply processing is carried out in the first link of the supply chain, the first supply characteristic parameters and the first carbon footprint are obtained by testing. The first link of the supply chain refers to the starting link of the supply chain, such as the planting link of the agricultural product supply chain, the raw material smelting link of the electronic supply chain, etc. For example, after production and processing are carried out in the first stage of the supply chain, the first supply characteristic parameters and the first carbon footprint are obtained through testing. The first supply characteristic parameters are key characteristic indicators describing the supply process in the first stage, such as output, processing time, and resource input, reflecting the supply capacity and process characteristics of the first stage. The first carbon footprint describes the total amount of carbon emissions actually generated in the first stage during the supply process, usually in tons of CO2 equivalent, including direct emissions (such as fuel combustion) and indirect emissions (such as upstream carbon emissions corresponding to electricity consumption). The first supply characteristic parameters can be monitored in real time by IoT sensors to collect process data such as output and energy consumption, combined with structured information integrated by management systems such as ERP, and supplemented by standardized manual records to complete information in non-automated scenarios. The first carbon footprint can be obtained by collecting direct emission data such as fuel combustion through flow meters and gas analyzers according to standards such as GHG Protocol, and calculating indirect emissions by correlating electricity consumption with regional carbon emission factors. After verification and integration, a complete carbon footprint is obtained.

[0035] Secondly, the first carbon allowance for the first stage is obtained. This first carbon allowance is a pre-allocated carbon emission limit for the first stage, which can be based on limits stipulated by industry policies or target values ​​set based on historical emission data. For example, 800 tons of CO2 equivalent are allocated to the first stage according to industry policies as the first carbon allowance. This first carbon allowance serves as a baseline for measuring whether the carbon emissions of the first stage are compliant or efficient, and is a reference standard for subsequent energy efficiency calculations and coordinated adjustments.

[0036] Next, the ratio of the first carbon allowance to the first carbon footprint is calculated as the first energy efficiency score, where the first energy efficiency score = first carbon allowance / first carbon footprint. For example, if the first carbon allowance is 800 tons of CO2 equivalent and the first carbon footprint is 600 tons of CO2 equivalent, then the first energy efficiency score = 800 / 600 = 1.33. If the first carbon footprint is less than the first carbon allowance, the first energy efficiency score is greater than 1, indicating that the actual carbon emissions are lower than the allowance and the efficiency meets the standard. Furthermore, the higher the first energy efficiency score, the better the efficiency. Conversely, if the first carbon footprint is greater than the first carbon allowance, the higher the first energy efficiency score, indicating that the actual carbon emissions exceed the allowance and the efficiency does not meet the standard.

[0037] Finally, the difference between the first carbon allowance and the first carbon footprint is calculated as the first carbon profit / loss, where first carbon profit / loss = first carbon allowance - first carbon footprint. For example, if the first carbon allowance is 800 tons of CO2 equivalent and the first carbon footprint is 600 tons of CO2 equivalent, then the first carbon profit / loss = 800 - 600 = +200 tons of CO2 equivalent. A positive first carbon profit / loss indicates a surplus in the first stage's carbon allowance, which can be transferred to subsequent stages. Conversely, if the first carbon footprint is 900 tons of CO2 equivalent, then the first carbon profit / loss = 800 - 900 = -100 tons of CO2 equivalent. A negative first carbon profit / loss indicates an overspending in the first stage's carbon allowance, meaning excessive emissions in the first stage, requiring subsequent stages to reduce allowances to compensate for the overspending.

[0038] In summary, compared to existing technologies, this application obtains the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and calculates the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link. This provides a reliable data foundation for subsequent dynamic adjustments and full-chain collaborative assessments.

[0039] The second-stage prediction module 12 is used to obtain the second carbon quota for the second stage, calculate the second adjusted carbon quota in combination with the first carbon profit and loss, and perform supply processing prediction for the second stage based on the first supply characteristic parameters and the second carbon quota to obtain the second predicted supply characteristic parameters.

[0040] There is a strong input-output coupling relationship between each link in the supply chain: the supply characteristics of the first link (such as the output and quality of raw materials) directly determine the processing limit of the second link. For example, if the first link produces 100 tons of raw materials, the second link can process a maximum of 100 tons. The carbon quota of the second link determines its processing constraints. In traditional management, the processing plan of the second link often relies on experience and judgment, which often leads to the problem of mismatch between the output of the preceding link and the processing of the following link.

[0041] To address the aforementioned issues, this application obtains a second carbon allowance for the second stage, calculates a second adjusted carbon allowance based on the first carbon profit and loss, and forecasts the supply processing for the second stage based on the first supply characteristic parameter and the second carbon allowance to obtain a second predicted supply characteristic parameter.

[0042] Specifically, the second-stage prediction module 12 in the system includes:

[0043] Obtain the second carbon allowance in the second stage;

[0044] Calculate the sum of the second carbon allowance and the first carbon surplus / deficit to obtain the second adjusted carbon allowance;

[0045] Based on the first supply characteristic parameter and the second carbon quota, the supply processing prediction for the second stage is performed to obtain the second predicted supply characteristic parameter.

[0046] In this embodiment, the second carbon allowance for the second stage is first obtained. The second carbon allowance is an initial carbon emission limit pre-allocated to the second stage of the supply chain (such as processing, assembly, transportation, etc., which are stages dependent on the output of the first stage). For example, the second carbon allowance for the second stage is obtained using the same method as the first carbon allowance. For instance, 100 tons of CO2 equivalent is used as the benchmark value for adjusting the allowance for the second stage, reflecting the upper limit of carbon emissions for the second stage under the independent assessment model.

[0047] Next, the sum of the second carbon allowance and the first carbon surplus / deficit is calculated to obtain the second adjusted carbon allowance, where the second adjusted carbon allowance = the second carbon allowance + the first carbon surplus / deficit. For example, if the first carbon surplus / deficit is +200 tons of CO2 equivalent and the second carbon allowance is 100 tons of CO2 equivalent, then the second adjusted carbon allowance = 200 + 100 = 300 tons of CO2 equivalent. In this way, the carbon emission results (surplus / overrun) of the first stage are transmitted to the second stage, realizing the dynamic redistribution of allowances and reflecting the synergy of overall carbon management of the supply chain rather than isolated management of each stage. For example, if the first carbon surplus / deficit is positive (surplus), the unused allowances of the first stage can be supplemented to the second stage, improving the overall allowance utilization efficiency. Conversely, if the first carbon surplus / deficit is negative (overrun), the excess emissions of the first stage need to be deducted from the allowance of the second stage. For example, if the first stage overruns by 10 tons of CO2 equivalent and the second carbon allowance is 100 tons of CO2 equivalent, the adjusted second carbon allowance is 90 tons of CO2 equivalent, forcing the second stage to reduce emissions more efficiently and avoiding exceeding the total emission standard of the entire chain.

[0048] Finally, based on the first supply characteristic parameter and the second carbon quota, the supply processing of the second stage is predicted to obtain the second predicted supply characteristic parameter. The first supply characteristic parameter is the actual supply result of the first stage. For example, if the first stage produces 500 tons of raw materials, it directly determines the upper limit of the input of the second stage. The second carbon quota reflects the carbon emission constraints of the second stage when it is not affected by the first stage, and indirectly affects its processing capacity. Through the pre-trained second stage predictor, the second predicted supply characteristic parameter is predicted and output, providing a benchmark for the actual supply characteristic parameter of the subsequent second stage.

[0049] Furthermore, the step of "predicting the supply process in the second stage based on the first supply characteristic parameter and the second carbon quota to obtain the second predicted supply characteristic parameter" includes:

[0050] The second-stage predictor is invoked, wherein the second-stage predictor is constructed using machine learning and is trained using the first set of supply feature parameters of the samples, the second set of carbon quotas of the samples, and the second set of predicted supply feature parameters of the samples.

[0051] The first supply characteristic parameter and the second carbon quota are input into the second stage predictor, and the prediction output is used to obtain the second predicted supply characteristic parameter.

[0052] In this embodiment of the application, the second-stage predictor is first invoked. The second-stage predictor is constructed using machine learning and is trained using the first set of supply feature parameters of the samples, the second set of carbon quotas of the samples, and the second set of predicted supply feature parameters of the samples.

[0053] For example, the second-stage predictor can be implemented through the following technical path: 1. Data preparation: Collect multi-dimensional sample data from the historical database, including the first supply characteristic parameters of the first stage in history, such as the raw material output, energy consumption, and processing time of the past 100 batches, to form a sample first supply characteristic parameter set; the second carbon quota of the second stage in the corresponding batch, to form a sample second carbon quota set; and the actual supply characteristic parameters achieved in the second stage in the corresponding batch, such as the actual processing volume and output qualification rate, to form a sample second predicted supply characteristic parameter set. The three types of data are preprocessed, for example, extreme values ​​exceeding the 3σ range are removed, and the data is scaled to the [0, 1] interval through normalization to eliminate the difference in dimensions. Then, the data is divided into a training set (for model learning), a validation set (for hyperparameter tuning), and a test set (for final performance evaluation) in a ratio of 7:1.5:1.5. 2. Model Construction: A neural network architecture is adopted, mainly composed of an input layer, hidden layers, and an output layer. The number of neurons in the input layer is consistent with the dimension of the input features. For example, if the first supply feature parameter contains 3 indicators and the second carbon quota contains 1 indicator, then the input layer has 4 neurons, responsible for receiving the normalized first supply feature parameter and the second carbon quota of the sample. The hidden layer has 2-3 layers (adjusted according to data complexity). The first layer has 64 neurons and the second layer has 32 neurons, both using the ReLU activation function to solve the nonlinearity problem and alleviate gradient vanishing. The number of neurons in the output layer is consistent with the dimension of the prediction target. For example, if predicting a single processing quantity, then 1 neuron is set, using a linear activation function. 3. Model Training: Using the first supply feature parameters and the second carbon quota of the samples in the training set as inputs, and the corresponding second predicted supply feature parameters of the samples as supervision labels, the mean squared error (MSE) is used as the loss function. Iterative training is performed using the Adam optimizer (with an initial learning rate of 0.001, decaying as needed). After each training round, the loss value is evaluated using a validation set. The model is optimized by adjusting hyperparameters such as the number of hidden layers, the number of neurons, and the learning rate until the prediction accuracy on the validation set is achieved. If the R² coefficient of determination is ≥0.95, or the mean relative error is ≤5%, the model is considered converged, and the second-stage predictor is obtained. 4. Model Validation: The generalization ability of the converged model is evaluated using an independent test set. If the performance on the test set is close to that on the validation set (e.g., error difference ≤2%), the model is determined to be a usable second-stage predictor; otherwise, it needs to be returned to the data preparation or model building stage for optimization.

[0054] Secondly, the first supply characteristic parameter and the second carbon quota are input into the second-stage predictor, and the prediction output yields the second predicted supply characteristic parameter. For example, the first supply characteristic parameter (e.g., the output of the first stage in this case is 480 tons) and the second carbon quota (e.g., 300 tons of CO2 equivalent) are input into the second-stage predictor, and the prediction output is the second predicted supply characteristic parameter, such as a predicted processing volume of 470 tons. In this way, the coupling effect of the first supply characteristic parameter and the second carbon quota is automatically captured by the machine learning model, and it can be continuously optimized with data accumulation to improve prediction accuracy.

[0055] In summary, compared to existing technologies, this application obtains a second carbon allowance for the second stage, calculates a second adjusted carbon allowance based on the first carbon surplus / deficit, and predicts the supply processing for the second stage based on the first supply characteristic parameters and the second carbon allowance, thereby obtaining a second predicted supply characteristic parameter. In this way, the carbon surplus / deficit from the first stage is transferred to the second stage through the second adjusted carbon allowance, achieving dynamic optimization of the allowance. Furthermore, the supply processing prediction for the second stage is performed using a machine learning model, providing a benchmark for subsequent actual difference compensation.

[0056] The second energy efficiency assessment module 13 is used to process the second stage of supply according to the second adjusted carbon quota, obtain the second actual supply characteristic parameters and the second carbon footprint, and calculate the second carbon profit and loss and the second energy efficiency score in combination with the second adjusted carbon quota.

[0057] The carbon profit and loss in the first link of the supply chain affects the quota adjustment in the second link, and the actual implementation results of the second link will affect the carbon quota of the next link. Therefore, it is necessary to conduct actual assessments of the second link to ensure that quota adjustments can be continuously optimized based on actual implementation results.

[0058] To address the aforementioned issues, this application employs a second-stage supply process based on the second adjusted carbon quota to obtain a second actual supply characteristic parameter and a second carbon footprint. Combined with the second adjusted carbon quota, a second carbon profit / loss and a second energy efficiency score are calculated.

[0059] Specifically, the second energy efficiency assessment module 13 in the system includes:

[0060] The second stage of supply processing is carried out in accordance with the second adjustment of carbon quotas, and the second actual supply characteristic parameters and the second carbon footprint are collected after the processing is completed.

[0061] Calculate the ratio of the second adjusted carbon allowance to the second carbon footprint to obtain the second energy efficiency score;

[0062] The difference between the second adjusted carbon allowance and the second carbon footprint is calculated to obtain the second carbon profit and loss.

[0063] In this embodiment, the second stage of supply processing is first carried out according to the second adjusted carbon quota. After processing, the second actual supply characteristic parameter and the second carbon footprint are collected. The second actual supply characteristic parameter reflects the key indicators of the actual processing result of the second stage, and the second carbon footprint is the total carbon emissions generated in the actual processing of the second stage. For example, the second adjusted carbon quota is 300 tons of CO2 equivalent. Based on this, the second stage of supply processing is carried out in the production / processing / transportation process, and the second actual supply characteristic parameter and the second carbon footprint are collected after processing using the same collection method as the first actual supply characteristic parameter and the first carbon footprint.

[0064] Secondly, the ratio of the second adjusted carbon allowance to the second carbon footprint is calculated to obtain the second energy efficiency score, where the second energy efficiency score = second adjusted carbon allowance / second carbon footprint. The second energy efficiency score can reflect the carbon emission efficiency of the second stage under the constraint of the dynamically adjusted second adjusted carbon allowance. If the second carbon footprint is less than the second adjusted carbon allowance, the calculated second energy efficiency score is greater than 1, indicating that the second stage has achieved efficient carbon control. Conversely, if the second carbon footprint is greater than the second adjusted carbon allowance, the calculated second energy efficiency score is less than 1, indicating that the actual emissions of the second stage exceed the adjusted second adjusted carbon allowance.

[0065] Finally, the difference between the second adjusted carbon allowance and the second carbon footprint is calculated to obtain the second carbon profit and loss, where the second carbon profit and loss = second adjusted carbon allowance - second carbon footprint. The second carbon profit and loss reflects the actual utilization result of the second stage of the second adjusted carbon allowance. If the second carbon profit and loss is positive, it means there is a surplus, and the allowance saved in the second stage can be transferred to the third stage to improve the overall allowance utilization rate. Conversely, if the second carbon profit and loss is negative, it means there is an overspending, that is, the second stage exceeds the emission limit, which needs to be deducted from the allowance of the third stage to avoid the total emissions of the entire chain exceeding the limit.

[0066] In summary, compared to existing technologies, this application processes the second stage of supply according to the second adjusted carbon quota, obtaining the second actual supply characteristic parameters and the second carbon footprint. Combined with the second adjusted carbon quota, a second carbon profit / loss and a second energy efficiency score are calculated. This completes the actual assessment of the second stage, providing crucial data for subsequent quota adjustments and enabling a complete collaborative closed loop in supply chain carbon management.

[0067] The compensation scoring module 14 is used to calculate a second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and to calculate a quota change coefficient based on the second adjusted carbon quota, to compensate the second energy efficiency score to obtain a second compensated energy efficiency score, and to continue to perform a carbon footprint energy efficiency collaborative assessment calculation for all links based on the second carbon profit and loss to obtain a collaborative energy efficiency score.

[0068] The aforementioned steps form an iterative full-chain assessment logic through single-stage carbon efficiency assessment, cross-stage quota dynamic adjustment, and compensation for differences between prediction and reality. Therefore, the carbon footprint and energy efficiency collaborative assessment calculation of all links in the supply chain can be completed in sequence, and finally the corrected energy efficiency scores of each link can be integrated to obtain a collaborative energy efficiency score that reflects the overall carbon management efficiency of the supply chain.

[0069] To address the aforementioned issues, this application calculates a second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and calculates a quota change coefficient based on the second adjusted carbon quota. It then compensates the second energy efficiency score to obtain a second compensated energy efficiency score, and continues to perform a synergistic assessment of carbon footprint energy efficiency across all stages based on the second carbon profit and loss to obtain a synergistic energy efficiency score.

[0070] Specifically, the compensation scoring module 14 in the system includes:

[0071] Calculate the difference between the second predicted supply characteristic parameter and the second actual supply characteristic parameter to obtain the second supply change coefficient;

[0072] Calculate the difference between the second adjusted carbon quota and the second carbon quota to obtain the second quota change coefficient;

[0073] Calculate the similarity between the second supply change coefficient and the second quota change coefficient to obtain the second energy efficiency correction coefficient;

[0074] The second energy efficiency correction coefficient is used to compensate the second energy efficiency score to obtain the second compensated energy efficiency score.

[0075] In this embodiment, the difference between the second predicted supply characteristic parameter and the second actual supply characteristic parameter is first calculated to obtain the second supply variation coefficient. The second supply variation coefficient = |second predicted supply characteristic parameter - second actual supply characteristic parameter| / second actual supply characteristic parameter. The second supply variation coefficient reflects the degree of deviation between the predicted and actual supply characteristic parameters in the second stage and can measure the accuracy of the prediction. For example, if the second predicted supply characteristic parameter is a predicted processing volume of 470 tons and the second actual supply characteristic parameter is an actual processing volume of 450 tons, then the second supply variation coefficient = |470 - 450| / 450 = 0.04. A larger second supply variation coefficient indicates a lower degree of matching between the second predicted and actual supply characteristic parameters, and the impact of this deviation needs to be reflected in the energy efficiency score.

[0076] Secondly, the difference between the second adjusted carbon quota and the second carbon quota is calculated to obtain the second quota change coefficient, where the second quota change coefficient = |second adjusted carbon quota - second carbon quota| / second carbon quota. The second quota change coefficient reflects the degree of deviation between the adjusted carbon quota after the second stage and the initial second carbon quota, and can measure the impact of quota dynamic adjustment. For example, if the second carbon quota is 100 tons of CO2 equivalent and the second adjusted carbon quota is 120 tons of CO2 equivalent, then the second quota change coefficient = |120 ​​- 100| / 100 = 0.2. The larger the second quota change coefficient, the larger the quota adjustment range, and the more significant the impact on carbon constraints in the second stage. Such external adjustment interference needs to be removed from the energy efficiency score.

[0077] Next, the similarity between the second supply change coefficient and the second quota change coefficient is calculated to obtain the second energy efficiency correction coefficient. This second energy efficiency correction coefficient measures the correlation between the magnitude of supply changes and the magnitude of quota changes, determining the strength of the correction for the second energy efficiency score. A higher similarity indicates that the supply deviation may be caused by quota adjustments; for example, if an increase in quotas improves actual supply capacity, the correction strength can be weakened, and vice versa. For instance, 1 minus the absolute value of the difference between the second supply change coefficient and the second quota change coefficient can be used as the second supply change coefficient and the second quota change coefficient. For example, if the second supply change coefficient is 0.04 and the second quota change coefficient is 0.2, the absolute value of their difference is 0.16, then the second energy efficiency correction coefficient = 1 - 0.16 = 0.84.

[0078] Finally, a second energy efficiency correction coefficient is used to compensate for the second energy efficiency score, resulting in a second compensated energy efficiency score. The second compensated energy efficiency score equals the second energy efficiency correction coefficient multiplied by the second energy efficiency score. This eliminates scoring biases caused by factors other than the efficiency of the process itself, such as inaccurate predictions or passive quota adjustments, retaining only the true efficiency resulting from the process's own carbon management capabilities. For example, if the second energy efficiency score is 1.2 and the second energy efficiency rating is 0.9, then the second compensated energy efficiency score = 1.2 * 0.9 = 1.08. The compensated second energy efficiency score reflects the performance of the second process under dynamic quotas while eliminating external interference, providing reliable single-process data for the whole-chain collaborative assessment.

[0079] Specifically, the phrase "continuing to perform a synergistic assessment of carbon footprint and energy efficiency across all stages based on the second carbon profit and loss, and obtaining a synergistic energy efficiency score" includes:

[0080] Based on the second carbon profit and loss, the third compensation energy efficiency score of the third stage is calculated, and the total energy efficiency score of all stages is obtained.

[0081] Based on the total energy efficiency score, the synergistic energy efficiency score is calculated.

[0082] In this embodiment, the third compensation energy efficiency score for the third stage is first calculated based on the second carbon profit and loss, and then the total energy efficiency scores for all stages are obtained. For example, the second carbon profit and loss is used as input for quota adjustments in the third stage. The aforementioned steps are repeated to calculate the third compensation energy efficiency score for the third stage, and so on, until all stages of the supply chain are evaluated and the total energy efficiency scores for all stages are obtained. In this way, the quota for each stage is affected by the carbon profit and loss of the previous stage, ensuring the consistency and objectivity of the data across the entire chain.

[0083] Secondly, a collaborative energy efficiency score is calculated based on the overall energy efficiency score. For example, the collaborative energy efficiency score can be calculated based on the importance of each link in the supply chain. For instance, if the supply chain contains three links with energy efficiency scores of 1.2 (weight 0.4), 1.1 (weight 0.3), and 0.9 (weight 0.3), then the collaborative energy efficiency score = 1.2 × 0.4 + 1.1 × 0.3 + 0.9 × 0.3 = 1.08. This collaborative energy efficiency score directly reflects the overall carbon efficiency level of the supply chain, demonstrating both the individual performance of each link and the collaborative effects between them, such as whether dynamic quota adjustments improve overall supply chain efficiency, thus providing a global decision-making basis for supply chain carbon optimization.

[0084] In summary, compared to existing technologies, this application calculates a second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and calculates a quota change coefficient based on the second adjusted carbon quota. This coefficient is then used to compensate for the second energy efficiency score to obtain a second compensated energy efficiency score. Furthermore, based on the second carbon profit and loss, a collaborative assessment of carbon footprint energy efficiency across all stages is conducted to obtain a collaborative energy efficiency score. This approach accurately reflects the true carbon efficiency of each stage and measures the overall collaborative carbon management level of the supply chain.

[0085] In summary, the embodiments of this application have at least the following technical effects:

[0086] Compared to existing technologies, this application first obtains the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and calculates the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link. In this way, a reliable data foundation is provided for the dynamic adjustment of subsequent links and the collaborative evaluation of the entire chain.

[0087] Secondly, this application obtains a second carbon allowance for the second stage, calculates a second adjusted carbon allowance based on the first carbon surplus / deficit, and predicts the supply processing for the second stage based on the first supply characteristic parameters and the second carbon allowance to obtain a second predicted supply characteristic parameter. In this way, the carbon surplus / deficit from the first stage is transmitted to the second stage through the second adjusted carbon allowance, achieving dynamic optimization of the allowance. Furthermore, the supply processing prediction for the second stage is performed using a machine learning model, providing a benchmark for subsequent actual difference compensation.

[0088] Furthermore, this application processes the second stage of supply according to the second adjusted carbon allowance, obtaining the second actual supply characteristic parameters and the second carbon footprint. Combined with the second adjusted carbon allowance, a second carbon profit / loss and a second energy efficiency score are calculated. This completes the actual assessment of the second stage, providing crucial data for subsequent allowance adjustments and creating a complete collaborative closed loop for supply chain carbon management.

[0089] Finally, this application calculates a second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and calculates a quota change coefficient based on the second adjusted carbon quota. This coefficient is then used to compensate for the second energy efficiency score to obtain a second compensated energy efficiency score. Finally, based on the second carbon profit and loss, a collaborative assessment of carbon footprint energy efficiency across all stages is performed to obtain a collaborative energy efficiency score. This approach accurately reflects the true carbon efficiency of each stage and measures the overall collaborative carbon management level of the supply chain.

[0090] Through the aforementioned technical solution, this application achieves optimized allocation of carbon resources along the supply chain by dynamically transmitting carbon quotas across different stages, allowing the carbon surplus or deficit of upstream stages to influence quota allocation in downstream stages. This breaks down the fragmentation of static quotas. Furthermore, it utilizes machine learning models to predict supply processing and combines supply-demand differences with quota adjustments for dual-dimensional correction, effectively mitigating fluctuation interference and ensuring the objectivity of single-stage energy efficiency scores. Ultimately, it transforms dispersed stage efficiency into quantifiable, chain-wide collaborative indicators through full-stage energy efficiency compensation scoring. This enhances the synergy and reliability of multi-stage carbon footprint assessment and provides enterprises with quantitative decision-making support for transitioning from single-stage optimization to a full-chain low-carbon transformation.

[0091] Example 2, as Figure 2 As shown, embodiments of the present invention also provide a method for synergistic assessment of supply chain carbon footprint and energy efficiency based on carbon quotas, including:

[0092] Obtain the first supply characteristic parameters and first carbon footprint of the first link in the supply chain, and calculate the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link.

[0093] Obtain the second carbon quota for the second stage, calculate the second adjusted carbon quota in conjunction with the first carbon profit and loss, and predict the supply processing of the second stage based on the first supply characteristic parameter and the second carbon quota to obtain the second predicted supply characteristic parameter.

[0094] The second stage of supply processing is carried out in accordance with the second adjusted carbon quota to obtain the second actual supply characteristic parameters and the second carbon footprint. The second carbon profit and loss and the second energy efficiency score are calculated in combination with the second adjusted carbon quota.

[0095] The second supply change coefficient is calculated based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and the quota change coefficient is calculated based on the second adjusted carbon quota. The second energy efficiency score is compensated to obtain the second compensated energy efficiency score. The carbon footprint energy efficiency synergy assessment calculation of all links is carried out based on the second carbon profit and loss to obtain the synergistic energy efficiency score.

[0096] Specifically, the phrase "obtaining the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and calculating the first carbon profit and loss and the first energy efficiency score in combination with the first carbon quota of the first link" includes:

[0097] After the first stage of supply processing within the supply chain, test to obtain the first supply characteristic parameters and the first carbon footprint;

[0098] Obtain the first carbon allowance in the first stage;

[0099] Calculate the ratio of the first carbon allowance to the first carbon footprint as the first energy efficiency score;

[0100] Calculate the difference between the first carbon quota and the first carbon footprint as the first carbon profit or loss.

[0101] Specifically, the step of "obtaining the second carbon quota for the second stage, calculating the second adjusted carbon quota in conjunction with the first carbon profit and loss, and predicting the supply processing for the second stage based on the first supply characteristic parameter and the second carbon quota to obtain the second predicted supply characteristic parameter" includes:

[0102] Obtain the second carbon allowance in the second stage;

[0103] Calculate the sum of the second carbon allowance and the first carbon surplus / deficit to obtain the second adjusted carbon allowance;

[0104] Based on the first supply characteristic parameter and the second carbon quota, the supply processing prediction for the second stage is performed to obtain the second predicted supply characteristic parameter.

[0105] Furthermore, the step of "predicting the supply process in the second stage based on the first supply characteristic parameter and the second carbon quota to obtain the second predicted supply characteristic parameter" includes:

[0106] The second-stage predictor is invoked, wherein the second-stage predictor is constructed using machine learning and is trained using the first set of supply feature parameters of the samples, the second set of carbon quotas of the samples, and the second set of predicted supply feature parameters of the samples.

[0107] The first supply characteristic parameter and the second carbon quota are input into the second stage predictor, and the prediction output is used to obtain the second predicted supply characteristic parameter.

[0108] Specifically, the phrase "performing second-stage supply processing according to the second adjusted carbon quota, obtaining second actual supply characteristic parameters and a second carbon footprint, and calculating a second carbon profit / loss and a second energy efficiency score in conjunction with the second adjusted carbon quota" includes:

[0109] The second stage of supply processing is carried out in accordance with the second adjustment of carbon quotas, and the second actual supply characteristic parameters and the second carbon footprint are collected after the processing is completed.

[0110] Calculate the ratio of the second adjusted carbon allowance to the second carbon footprint to obtain the second energy efficiency score;

[0111] The difference between the second adjusted carbon allowance and the second carbon footprint is calculated to obtain the second carbon profit and loss.

[0112] Specifically, the step of "calculating a second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and calculating a quota change coefficient based on the second adjusted carbon quota, and compensating the second energy efficiency score to obtain a second compensated energy efficiency score" includes:

[0113] Calculate the difference between the second predicted supply characteristic parameter and the second actual supply characteristic parameter to obtain the second supply change coefficient;

[0114] Calculate the difference between the second adjusted carbon quota and the second carbon quota to obtain the second quota change coefficient;

[0115] Calculate the similarity between the second supply change coefficient and the second quota change coefficient to obtain the second energy efficiency correction coefficient;

[0116] The second energy efficiency correction coefficient is used to compensate the second energy efficiency score to obtain the second compensated energy efficiency score.

[0117] Furthermore, the phrase "continuing to perform a synergistic assessment of carbon footprint and energy efficiency across all stages based on the second carbon profit and loss, and obtaining a synergistic energy efficiency score" includes:

[0118] Based on the second carbon profit and loss, the third compensation energy efficiency score of the third stage is calculated, and the total energy efficiency score of all stages is obtained.

[0119] Based on the total energy efficiency score, the synergistic energy efficiency score is calculated.

[0120] In summary, the embodiments of this application have at least the following technical effects:

[0121] Compared to existing technologies, this application first obtains the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain. Combined with the first carbon quota of the first link, it calculates the first carbon profit / loss and the first energy efficiency score, providing a reliable data foundation for subsequent dynamic adjustments and full-chain collaborative assessments. Secondly, it obtains the second carbon quota of the second link and calculates the second adjusted carbon quota based on the first carbon profit / loss. Based on the first supply characteristic parameters and the second carbon quota, it predicts the supply processing of the second link, obtaining the second predicted supply characteristic parameters. The carbon profit / loss of the first link is transmitted to the second link through the second adjusted carbon quota, achieving dynamic optimization of quotas. Furthermore, it uses a machine learning model to predict the supply processing of the second link, providing a benchmark for subsequent actual difference compensation. Thirdly, it processes the supply of the second link according to the second adjusted carbon quota, obtaining the second actual supply characteristic parameters and the second carbon footprint. Combined with the second adjusted carbon quota, it calculates the second carbon profit / loss and the second energy efficiency score, completing the actual assessment of the second link. This provides key data for subsequent quota adjustments, enabling a complete collaborative closed loop for supply chain carbon management. Finally, a second supply change coefficient is calculated based on the second predicted supply characteristic parameters and the second actual supply characteristic parameters, and a quota change coefficient is calculated based on the second adjusted carbon quota. This coefficient is then used to compensate for the second energy efficiency score, resulting in a second compensated energy efficiency score. Furthermore, based on the second carbon profit and loss, a collaborative assessment of carbon footprint energy efficiency across all stages is conducted to obtain a collaborative energy efficiency score. This score accurately reflects the true carbon efficiency of each stage and measures the overall collaborative carbon management level of the supply chain. This enhances the synergy and reliability of multi-stage carbon footprint assessment and provides enterprises with a quantitative decision-making basis for moving from single-stage optimization to a full-chain low-carbon transformation.

[0122] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0128] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A supply chain carbon footprint and energy efficiency collaborative assessment system based on carbon quotas, characterized in that, The system includes: The first energy efficiency assessment module is used to obtain the first supply characteristic parameters and the first carbon footprint of the first link in the supply chain, and calculate the first carbon profit and loss and the first energy efficiency score by combining the first carbon quota of the first link. The supply characteristic parameters refer to the output, processing time and resource input of the supply process, and the first energy efficiency score is the ratio of the first carbon quota to the first carbon footprint. The second-stage prediction module is used to obtain the second carbon quota for the second stage, calculate the second adjusted carbon quota by combining the first carbon profit and loss, and predict the supply processing of the second stage based on the first supply characteristic parameters and the second carbon quota to obtain the second predicted supply characteristic parameters. The second adjusted carbon quota is the sum of the second carbon quota and the first carbon profit and loss. The second energy efficiency assessment module is used to process the second stage of supply according to the second adjusted carbon quota, obtain the second actual supply characteristic parameters and the second carbon footprint, and calculate the second carbon profit and loss and the second energy efficiency score in combination with the second adjusted carbon quota, wherein the second energy efficiency score is the ratio of the second adjusted carbon quota to the second carbon footprint. The compensation scoring module is used to calculate the second supply change coefficient based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and to calculate the quota change coefficient based on the second adjusted carbon quota, to compensate the second energy efficiency score to obtain the second compensated energy efficiency score, and to continue to perform carbon footprint energy efficiency collaborative assessment calculation for all links based on the second carbon profit and loss to obtain the collaborative energy efficiency score. The compensation scoring module includes: The supply change analysis unit is used to calculate the difference between the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and to obtain the second supply change coefficient. The quota change analysis unit is used to calculate the difference between the second adjusted carbon quota and the second carbon quota, and to obtain the second quota change coefficient. A similarity calculation unit is used to calculate the similarity between the second supply change coefficient and the second quota change coefficient to obtain a second energy efficiency correction coefficient. The compensation scoring unit is used to perform compensation calculations on the second energy efficiency score using the second energy efficiency correction coefficient to obtain a second compensated energy efficiency score. The compensation scoring module further includes: The cyclic calculation unit is used to continue calculating the third compensation energy efficiency score of the third stage based on the second carbon profit and loss, and to calculate and obtain the total energy efficiency score of all stages. The scoring output unit is used to calculate the collaborative energy efficiency score based on all energy efficiency scores.

2. The supply chain carbon footprint and energy efficiency synergistic assessment system based on carbon quotas according to claim 1, characterized in that, The first energy efficiency assessment module includes: The parameter testing unit is used to test and obtain the first supply characteristic parameters and the first carbon footprint after the first stage of supply processing in the supply chain. The first carbon quota acquisition unit is used to acquire the first carbon quota in the first stage. The first energy efficiency scoring unit is used to calculate the ratio of the first carbon quota to the first carbon footprint, which is used as the first energy efficiency score. The first profit and loss analysis unit is used to calculate the difference between the first carbon quota and the first carbon footprint as the first carbon profit and loss.

3. The supply chain carbon footprint and energy efficiency synergistic assessment system based on carbon quotas according to claim 1, characterized in that, The second stage prediction module includes: The second carbon quota acquisition unit is used to acquire the second carbon quota in the second stage. The second allocation unit is used to calculate the sum of the second carbon quota and the first carbon surplus / deficit to obtain the second adjusted carbon quota; The supply forecasting unit is used to forecast the supply process of the second stage based on the first supply characteristic parameter and the second carbon quota, and obtain the second forecast supply characteristic parameter.

4. The supply chain carbon footprint and energy efficiency synergistic assessment system based on carbon quotas according to claim 3, characterized in that, The supply forecasting unit includes: The model calling unit is used to call the second-stage predictor, wherein the second-stage predictor is constructed using machine learning and is trained using the first set of supply feature parameters of the samples, the second set of carbon quotas of the samples, and the second set of predicted supply feature parameters of the samples. The prediction output unit is used to input the first supply characteristic parameter and the second carbon quota into the second stage predictor, and to obtain the second predicted supply characteristic parameter by prediction output.

5. The supply chain carbon footprint and energy efficiency synergistic assessment system based on carbon quotas according to claim 1, characterized in that, The second energy efficiency assessment module includes: The data acquisition unit is used to perform second-stage supply processing according to the second adjusted carbon quota, and to acquire the second actual supply characteristic parameters and the second carbon footprint after the processing is completed. The second energy efficiency rating unit is used to calculate the ratio of the second adjusted carbon quota to the second carbon footprint to obtain the second energy efficiency rating. The second profit and loss analysis unit is used to calculate the difference between the second adjusted carbon quota and the second carbon footprint to obtain the second carbon profit and loss.

6. A supply chain carbon footprint and energy efficiency synergistic assessment method based on carbon quotas, characterized in that, include: The first supply characteristic parameter and first carbon footprint of the first link in the supply chain are obtained, and the first carbon profit and loss and the first energy efficiency score are calculated by combining the first carbon quota of the first link. The supply characteristic parameter refers to the output, processing time and resource input of the supply process, and the first energy efficiency score is the ratio of the first carbon quota and the first carbon footprint. Obtain the second carbon allowance for the second stage, calculate the second adjusted carbon allowance based on the first carbon profit and loss, and predict the supply processing of the second stage based on the first supply characteristic parameter and the second carbon allowance to obtain the second predicted supply characteristic parameter. The second adjusted carbon allowance is the sum of the second carbon allowance and the first carbon profit and loss. The second stage of supply processing is carried out according to the second adjusted carbon quota to obtain the second actual supply characteristic parameters and the second carbon footprint. The second carbon profit and loss and the second energy efficiency score are calculated in combination with the second adjusted carbon quota, wherein the second energy efficiency score is the ratio of the second adjusted carbon quota to the second carbon footprint. The second supply change coefficient is calculated based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and the quota change coefficient is calculated based on the second adjusted carbon quota. The second energy efficiency score is compensated to obtain the second compensated energy efficiency score. The carbon footprint energy efficiency synergy assessment calculation of all links is carried out based on the second carbon profit and loss to obtain the synergistic energy efficiency score. Specifically, a second supply change coefficient is calculated based on the second predicted supply characteristic parameter and the second actual supply characteristic parameter, and a quota change coefficient is calculated based on the second adjusted carbon quota. The second energy efficiency score is then compensated to obtain a second compensated energy efficiency score, including: Calculate the difference between the second predicted supply characteristic parameter and the second actual supply characteristic parameter to obtain the second supply change coefficient; Calculate the difference between the second adjusted carbon quota and the second carbon quota to obtain the second quota change coefficient; Calculate the similarity between the second supply change coefficient and the second quota change coefficient to obtain the second energy efficiency correction coefficient; The second energy efficiency correction coefficient is used to compensate the second energy efficiency score to obtain the second compensated energy efficiency score. Among these, based on the second carbon profit and loss, a synergistic assessment of carbon footprint and energy efficiency is conducted across all stages to obtain a synergistic energy efficiency score, including: Based on the second carbon profit and loss, the third compensation energy efficiency score of the third stage is calculated, and the total energy efficiency score of all stages is obtained. Based on the total energy efficiency score, the synergistic energy efficiency score is calculated.

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