Enterprise transportation supplier evaluation method and device fusing carbon emission factors

Abstract

The invention relates to the field of supply chain screening, in particular to an enterprise transportation supplier evaluation method and device fusing carbon emission factors. According to the method, influences of multiple factors such as the carbon emission condition, the cargo transportation volume, the expenditure amount and the mileage of a transportation supplier in two statistical periods are comprehensively considered, the performance conditions of the supplier in multiple dimensions such as the carbon emission, the transportation efficiency and the expenditure amount are comprehensively evaluated, and optimal management of an enterprise transportation supply chain is achieved; meanwhile, the carbon emission efficiency is incorporated into an evaluation system for screening suppliers, the trend of green development is met, and the comprehensive management requirement of an enterprise for a green supply chain is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an enterprise transportation supplier evaluation method and device fusing carbon emission factors. BACKGROUND

[0002] With the acceleration of economic globalization and market integration, the complexity of enterprise supply chain is increasing. Modern enterprise supply chain covers many links, including raw material procurement, product manufacturing, warehouse management and transportation distribution, etc. Each link is interrelated and interdependent. Under such background, the enterprise transportation supply chain as the key link connecting production and sales, its importance is increasingly prominent.

[0003] At present, the traditional supplier qualification capability evaluation usually processes the supplier qualification information by manual information processing. First, the management of transportation supply chain involves many factors, such as transportation cost, transportation time, transportation quality and service level, etc. These factors are interwoven, making it difficult for enterprises to fully weigh when selecting and evaluating transportation suppliers. Secondly, the global attention to green development is increasing, and carbon emission has become an important consideration factor in enterprise operation. Enterprises need to bear social responsibility, reduce carbon emission and achieve sustainable development while pursuing economic benefits. This requires enterprises to effectively control carbon emission in supply chain management, especially in transportation, and choose low-carbon and environmentally friendly transportation mode and suppliers. The existing transportation supply chain screening methods have certain limitations. Some traditional methods mainly focus on cost analysis, taking transportation price as the main decision basis, ignoring other important factors in the transportation process, such as transportation punctuality, cargo integrity and service quality, etc. In addition, these methods often lack attention to environmental factors such as carbon emission, which cannot meet the current demand of enterprises for green supply chain management.

[0004] Therefore, it is particularly important to provide a supplier green evaluation method and device to improve the information data processing efficiency of suppliers, realize the optimization management of supply chain, and meet the trend of green development at the same time. SUMMARY

[0005] The purpose of the present application is to provide an enterprise transportation supplier evaluation method and device fusing carbon emission factors, which can realize supply chain optimization and meet the requirements of green development.

[0006] According to the first aspect of the present application, an enterprise transportation supplier evaluation method fusing carbon emission factors is provided, comprising: S1. receiving the first carbon emission data group and the second carbon emission data group sent by the plurality of transportation supply enterprises of the main enterprise; S2. obtaining, according to the operation data of the principal enterprise, a first freight transportation volume data set, a second freight transportation volume data set, a first expenditure amount data set, a second expenditure amount data set, a first transportation mileage data set and a second transportation mileage data set of a plurality of transportation supply enterprises; S3. taking the first carbon emission data set, the first freight transportation volume data set, the first expenditure amount data set and the first transportation mileage data set as a first period data set, and calculating a first comprehensive evaluation index according to the first period data set; S4. taking the second carbon emission data set, the second freight transportation volume data set, the second expenditure amount data set and the second transportation mileage data set as a second period data set, and calculating a second comprehensive evaluation index according to the second period data set; S5. calculating a supply variation data set according to the first comprehensive evaluation index and the second comprehensive evaluation index; S6. calculating a carbon emission efficiency data set, a transportation efficiency data set and an expenditure efficiency data set according to the second period data set; S7. calculating an enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

[0007] In the method of the first aspect of the application, in step S3, the first comprehensive evaluation index is calculated according to the first period data set, which includes: calculating a principal component score data set of each transportation supply enterprise according to the first period data set; determining the number of principal components used for calculating the first comprehensive evaluation index according to the principal component score data set; and calculating the first comprehensive evaluation index according to the principal component score data set and the number of principal components.

[0008] In the method of the first aspect of the application, the step of calculating the principal component score data set of each transportation supply enterprise according to the first period data set in step S3 includes: performing standardization processing on the first period data set by using a range standardization method to obtain a first standardized data set; calculating a covariance matrix and a correlation coefficient matrix of the first standardized data set; calculating eigenvalues and eigenvectors of the covariance matrix and the correlation coefficient matrix; and calculating the principal component score data set of each transportation supply enterprise according to the eigenvectors.

[0009] The method of the first aspect of the application, the refinement step in step S3 determines the number of principal components for calculating the first comprehensive evaluation index, comprising: calculating the principal component variance contribution rate of each transport supply enterprise according to the principal component score data set; determining the first principal component number according to the principal component variance contribution rate and the preset contribution rate threshold; verifying whether the first principal component number meets the actual business needs of the main enterprise according to the preset enterprise cycle planning, if it meets, the first principal component number is obtained as the number of principal components; otherwise, adjust the contribution threshold, return to step, determine the first principal component number according to the principal component variance contribution rate and the preset contribution rate threshold.

[0010] The method of the first aspect of the application, step S6. The carbon emission efficiency data set, the transport efficiency data set and the expenditure efficiency data set are calculated according to the second cycle data set, comprising: calculating the carbon emission efficiency data set according to the second carbon emission data set and the second freight transport volume data set; calculating the transport efficiency data set according to the second transport mileage data set and the second freight transport volume data set; calculating the expenditure efficiency data set according to the second expenditure amount data set and the second transport mileage data set.

[0011] The method of the first aspect of the application further comprises: determining the unqualified situation data of the transport supply enterprise according to the enterprise score list; determining the elimination enterprise list according to the preset elimination rule and the unqualified situation data. And the elimination of supply chain enterprise list; remove the elimination supply chain enterprise in the enterprise score list to obtain the updated enterprise score list; determining the optimization supply chain enterprise list according to the updated enterprise score list and the transport demand of the enterprise.

[0012] The method of the first aspect of the application, step according to the updated enterprise score list and the transport demand of the enterprise to determine the optimization supply chain enterprise list, comprising: According to the freight transport volume data in the second freight transport volume data set, judge whether the total freight transport volume of the enterprise in the updated enterprise score list can meet the transport demand; In the case that the total freight transport volume can meet the transport demand, the freight transport volume of the enterprise in the updated enterprise score list is accumulated and summed, and when the value of the freight transport volume accumulated and summed is first greater than or equal to the transport demand, the enterprise that has been accumulated is taken as the optimization supply chain enterprise list; In the case that the total freight transport volume cannot meet the transport demand, the candidate carbon emission data set, the candidate transport efficiency data set, the candidate expenditure efficiency data set and the freight transport capacity data set of the enterprise in the green supply candidate library in the last cycle are obtained; Determine the chain supplement enterprise list according to the candidate carbon emission data set, the candidate transport efficiency data set, the candidate expenditure efficiency data set and the freight transport capacity data set; The updated enterprise score list and the chain supplement enterprise list are merged as an optimized supply chain enterprise list.

[0013] In the method of the first aspect of the application, step S7. The enterprise score list is calculated according to the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set. A hierarchical structure model for calculating the enterprise score list is constructed, and the hierarchical structure model includes a target layer, a criterion layer and a scheme layer; the target layer includes the enterprise score list, and the criterion layer includes four factors of supply variation, carbon emission efficiency, transportation efficiency and expenditure efficiency. A judgment matrix is constructed, and the 1-9 scale method is used to quantify the relative importance between supply variation, carbon emission efficiency, transportation efficiency and expenditure efficiency, and the specific meanings of the scales are as follows: 1 represents that the two factors are equally important; 3 represents that one factor is slightly important; 5 represents that one factor is obviously important; 7 represents that one factor is very important; 9 represents that one factor is absolutely important; 2, 4, 6 and 8 are intermediate values of the above judgments, representing the case between two adjacent importance levels. The judgment matrix is normalized and consistency checked to obtain a weight data set. The enterprise score list is calculated according to the weight data set, the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

[0014] In the method of the first aspect of the application, step S5. The supply variation data set is calculated according to the first comprehensive evaluation index and the second comprehensive evaluation index, which includes determining a change score standard according to the first comprehensive evaluation index and the second comprehensive evaluation index, and determining the supply variation data set according to the change score standard, wherein the supply variation data set includes the change score of each supplier.

[0015] According to the second aspect of the application, an enterprise transportation supplier evaluation device integrating carbon emission factors is provided, which includes: A data receiving module is configured to receive a first carbon emission data set and a second carbon emission data set sent by a plurality of transportation supply enterprises of a main enterprise. A data processing module is configured to obtain a first cargo transportation volume data set, a second cargo transportation volume data set, a first expenditure amount data set, a second expenditure amount data set, a first transportation mileage data set and a second transportation mileage data set of the plurality of transportation supply enterprises according to operation data of the main enterprise. The data processing module is further configured to take the first carbon emission data set, the first cargo transportation volume data set, the first expenditure amount data set and the first transportation mileage data set as a first period data set, and calculate a first comprehensive evaluation index according to the first period data set. The data processing module is further configured to: take the second carbon emission data set, the second cargo transportation volume data set, the second expenditure amount data set, and the second transportation mileage data set as a second period data set, and calculate a second comprehensive evaluation index according to the second period data set; The data processing module is further configured to: calculate a supply variation data set according to the first comprehensive evaluation index and the second comprehensive evaluation index; calculate a carbon emission efficiency data set, a transportation efficiency data set, and an expenditure efficiency data set according to the second period data set; and calculate an enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set, and the expenditure efficiency data set.

[0016] The scheme has the following beneficial effects: 1. Through multi-period dynamic evaluation, the performance changes of the transportation supplier in the time dimension are measured by comprehensively considering multiple factors such as carbon emission, cargo transportation volume, expenditure amount, mileage, etc. in different statistical periods, accidental deviation caused by single-period evaluation is avoided, the stability and reliability of the evaluation result are improved, the operation status and performance of the supplier are comprehensively evaluated, and scientific and accurate selection is facilitated, and the misjudgment problem caused by a single index is solved.

[0017] 2. Promote the sustainable development of the supply chain: the carbon emission efficiency is included in the evaluation system and its influence is highlighted, which helps to encourage the supplier to take energy-saving and emission-reducing measures, improve resource utilization efficiency, and reduce negative impact on the environment. Through quantitative analysis of the carbon emission per unit of transportation volume, the enterprise can not only pay attention to cost and efficiency when selecting a transportation supplier, but also effectively identify low-carbon and high-efficiency partners, which helps to guide the transportation enterprise to optimize the transportation path, improve the cargo carrying rate, and reduce the empty running behavior, thereby reducing carbon emission from the source and helping to achieve the national "double carbon" goal.

[0018] 3. Optimize cost management: comprehensively consider transportation efficiency and expenditure efficiency, so that the enterprise can identify the supplier with excellent performance in cost control, and at the same time, the supplier is prompted to optimize the transportation route, improve the transportation load rate, etc., thereby reducing the transportation cost and improving the cost efficiency of the enterprise.

[0019] 4. Establish a mechanism for eliminating and supplementing the chain to ensure that the stability of the supply chain and the green upgrade are carried out simultaneously: By providing a scientific scoring mechanism and designing rules for eliminating unqualified suppliers and a supplementing mechanism, when a high-carbon or low-efficiency enterprise is eliminated, if the remaining transportation capacity cannot meet the demand of the enterprise, the system will automatically select a supplementing enterprise that meets the carbon emission, efficiency, etc. standards from the green alternative library. This mechanism ensures that the enterprise can promote green transformation while ensuring normal operation without affecting the normal operation due to the rupture of the supply chain.

[0020] 5. By integrating carbon emission factors into the enterprise transportation supplier evaluation device, the system supports customized adjustments by enterprises, enhancing the applicability and flexibility of the solution. In selecting the number of principal components, the system allows for manual intervention and adjustment of contribution thresholds, taking into account the company's actual business priorities, thereby retaining the principal components most relevant to the company's business. This mechanism enables the evaluation model to be dynamically adjusted according to the focus of different industries, such as paying more attention to carbon reduction or cost control, thus improving the system's adaptability and practicality.

[0021] 6. The model can be extended to other industries, such as retail, e-commerce, and express delivery. Furthermore, the parameter settings and weight allocation in the evaluation model can be flexibly adjusted according to specific application scenarios, demonstrating strong versatility. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without exceeding the scope of protection claimed by the present invention.

[0023] Figure 1 This is a flowchart illustrating an embodiment 1000 of the enterprise transportation supplier evaluation method that incorporates carbon emission factors according to the present invention. Figure 2 for Figure 1 A flowchart illustrating the detailed steps of step S3 in Example 1000; Figure 3 for Figure 2 A flowchart illustrating the detailed steps of step S31 in the middle section; Figure 4 for Figure 2 A flowchart illustrating the detailed steps of step S32 in the middle section; Figure 5 for Figure 1 A flowchart illustrating the detailed steps of step S5 in Example 1000; Figure 6 for Figure 1 A flowchart illustrating the detailed steps of step S6 in Example 1000; Figure 7 for Figure 1 A flowchart illustrating the detailed steps of step S7 in Example 1000; Figure 8 This is a flowchart illustrating an embodiment 2000 of the enterprise transportation supplier evaluation method that incorporates carbon emission factors according to the present invention. Figure 9 for Figure 8A flowchart of the detailed steps of step S211 in embodiment 2000; Figure 10 A schematic diagram of embodiment 3000 of a fusion carbon emission factor-based enterprise transportation supplier evaluation device of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0025] Reference Figure 1 , Figure 1 A flowchart of embodiment 1000 of a fusion carbon emission factor-based enterprise transportation supplier evaluation method of the present application. As shown in Figure 1 the method in embodiment 1000 includes steps S1-S7.

[0026] In step S1, a fusion carbon emission factor-based enterprise transportation supplier evaluation device (for example, a processor, which will be taken as an example hereinafter) receives first carbon emission data sets and second carbon emission data sets sent by a plurality of transportation supply enterprises of a main enterprise.

[0027] In some specific embodiments, the main enterprise has a plurality of transportation supply enterprises providing logistics transportation services, and the transportation supply enterprises jointly form a transportation supply chain of the main enterprise. Optionally, the main enterprise informs each transportation supply enterprise of a data template and requirements of carbon emission data to be collected in advance, which include specific forms of carbon emission data to be provided. For example, the main enterprise requires the transportation supply enterprises to provide carbon emission amounts subdivided by transportation vehicles and transportation routes, and specifies time granularity and total time range of the provided data. For example, the carbon emission data provided by the transportation supply enterprises is counted in a monthly granularity.

[0028] In some specific embodiments, the first carbon emission data sets are carbon emission data of the transportation supply enterprises in a first statistical period, and the second carbon emission data sets are carbon emission data of the transportation supply enterprises in a second statistical period. Optionally, the time granularity and time range of the first statistical period and the second statistical period should be consistent, and other influencing factors should be avoided as much as possible. For example, when selecting the statistical periods, the climate conditions between the selected first statistical period and the second statistical period should not be greatly different, so as to avoid affecting the calculation results. A preferred implementation is that the first statistical period and the second statistical period are two consecutive natural years.

[0029] In some embodiments, at step S2, the processor obtains a first freight transportation volume data set, a second freight transportation volume data set, a first expenditure amount data set, a second expenditure amount data set, a first transportation mileage data set and a second transportation mileage data set of the plurality of transportation supply enterprises according to the operation data of the main enterprise.

[0030] In some embodiments, the processor extracts the expenditure amount, freight transportation volume and transportation mileage data of each transportation supply enterprise within the first statistical period and the second statistical period from the operation data of the main enterprise. The operation data of the enterprise includes data in the databases of the financial management system, the logistics management system and the like. In some embodiments, when the main enterprise extracts data from the internal system, it should ensure that the extracted data is consistent with the data obtained at step S1 in terms of time granularity and time length range.

[0031] In some embodiments, the first freight transportation volume data set and the second freight transportation volume data set are respectively the freight transportation volume data of each transportation supply enterprise within the first statistical period and the second statistical period of the main enterprise; the first expenditure amount data set and the second expenditure amount data set are respectively the expenditure amount data of each transportation supply enterprise within the first statistical period and the second statistical period of the main enterprise; and the first transportation mileage data set and the second transportation mileage data set are respectively the transportation mileage data of each transportation supply enterprise within the first statistical period and the second statistical period of the main enterprise. Optionally, the unit of the freight transportation volume data is ton, the unit of the transportation mileage data is kilometer, and the unit of the expenditure amount data is ten thousand yuan.

[0032] In some embodiments, at step S3, the processor takes the first carbon emission data set, the first freight transportation volume data set, the first expenditure amount data set and the first transportation mileage data set as a first period data set, and calculates a first comprehensive evaluation index according to the first period data set.

[0033] In some embodiments, at step S3, the processor calculates a principal component score data set of each of the transportation supply enterprises according to the first period data set. In some embodiments, at step S3, the processor first needs to perform standardization processing on the data in the first period data set, then calculates the covariance matrix and the correlation coefficient matrix according to the processed standardized data, further calculates the eigenvalue and the eigenvector, and then calculates the principal component score of each transportation supply enterprise according to the eigenvector to generate the principal component score data set.

[0034] In some embodiments, at step S3, the processor determines the number of principal components used to calculate the first comprehensive evaluation index according to the principal component score data set. In some embodiments, the processor calculates the principal component variance contribution rate of each transport supply enterprise according to the principal component score data set, and then determines the first principal component number according to the principal component variance contribution rate and a preset contribution rate threshold. Then, the processor verifies whether the first principal component number meets the actual business needs of the principal enterprise according to the preset enterprise cycle plan. If yes, the first principal component number is taken as the number of principal components. Otherwise, the contribution rate threshold is adjusted, and the first principal component number is determined again according to the principal component variance contribution rate and the preset contribution rate threshold.

[0035] In some embodiments, at step S3, the processor calculates the first comprehensive evaluation index according to the principal component score data set and the number of principal components. In some embodiments, at step S3, for each transport supply enterprise, the processor takes the number of principal components as the number of principal components to be accumulated, and then performs weighted summation on the principal components in the principal component score data set, where the weight value is the variance contribution rate of each principal component. The result obtained is the first comprehensive evaluation index.

[0036] In some embodiments, at step S4, the processor takes the second carbon emission data set, the second freight transport volume data set, the second expenditure amount data set and the second transport mileage data set as the second cycle data set, and calculates the second comprehensive evaluation index according to the second cycle data set. Alternatively, the method used in step S4 is the same as that in step S3, and the only difference is that the data processed in step S3 is the data in the first statistical period, and the data processed in step S4 is the data in the second statistical period. Therefore, the detailed steps of step S4 are not described in detail here.

[0037] In some embodiments, at step S5, the processor calculates the supply variation data set according to the first comprehensive evaluation index and the second comprehensive evaluation index. In some embodiments, at step S5, the processor determines the change score standard according to the first comprehensive evaluation index and the second comprehensive evaluation index, and further determines the supply variation data set according to the change score standard. The supply variation data set includes the change condition score of each supplier.

[0038] Alternatively, at step S5, for each transport supply enterprise, the processor calculates the change rate of the corresponding comprehensive evaluation index and its distribution, and then further determines the corresponding relationship between the change rate interval range and the change score. This corresponding relationship reflects the score corresponding to the interval of the change rate, which can be used as the change score standard to measure the evaluation of the change condition of each transport supply enterprise.

[0039] In some embodiments, at step S6, the processor calculates the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set according to the second carbon emission data set and the second freight transportation volume data set, the second transportation mileage data set and the second freight transportation volume data set, and the expenditure amount data set and the second transportation mileage data set.

[0040] In some embodiments, at step S6, the processor calculates the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set according to the second carbon emission data set and the second freight transportation volume data set, the second transportation mileage data set and the second freight transportation volume data set, and the expenditure amount data set and the second transportation mileage data set.

[0041] In some embodiments, at step S7, the processor calculates the enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

[0042] In some embodiments, at step S7, the processor constructs a hierarchical model for calculating the enterprise score list. Optionally, the hierarchical model includes a target layer, a criterion layer and a scheme layer; the target layer includes the enterprise score list, and the criterion layer includes the four factors of supply variation, carbon emission efficiency, transportation efficiency and expenditure efficiency.

[0043] In some embodiments, at step S7, the processor constructs a judgment matrix and quantifies the relative importance between the supply variation, the carbon emission efficiency, the transportation efficiency and the expenditure efficiency by using the 1-9 scale method. Optionally, the specific meanings of the scales are as follows: 1, indicating that the two factors are equally important; 3, indicating that one factor is slightly important; 5, indicating that one factor is obviously important; 7, indicating that one factor is very important; 9, indicating that one factor is absolutely important; 2, 4, 6 and 8 are intermediate values of the above judgments, indicating the cases between two adjacent importance levels.

[0044] In some embodiments, at step S7, the processor normalizes the judgment matrix and performs consistency check to obtain a weight data set. In some embodiments, at step S7, the processor calculates the enterprise score list according to the weight data set, the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

[0045] Figure 2 For Figure 1 A flowchart of the detailed steps of step S3 in embodiment 1000 is shown in FIG. 2. As shown in FIG. 2, step S3 includes steps S31-S33.

[0046] In some embodiments, at step S31, the processor calculates the principal component score data set of each transportation supply enterprise according to the first period data set.

[0047] At step S31, the processor first normalizes the data in the first period data set, then calculates the covariance matrix and the correlation coefficient matrix according to the processed normalized data, further calculates the eigenvalues and eigenvectors, and then calculates the principal component scores of each transport supply enterprise according to the eigenvectors, and generates a principal component score data set. Each principal component score is the weighted sum of each data in the first period data set, reflecting the comprehensive performance of the transport supply enterprise under the action of multiple variables, and each transport supply enterprise obtains multiple principal component scores through calculation. For example, the first principal component of the transport supply enterprise A can be expressed as: Principal component 1 = w1 x carbon emission data + w2 x freight volume + w3 x expenditure amount + w4 x mileage wherein w1, w2, w3, and w4 are elements of the eigenvector corresponding to the first principal component, indicating the weight of multiple variables in the principal component.

[0048] In some embodiments, at step S32, the processor determines the number of principal components used to calculate the first comprehensive evaluation index according to the principal component score data set.

[0049] At step S32, the processor calculates the principal component variance contribution rate of each transport supply enterprise according to the principal component score data set, and then determines the first principal component number according to the principal component variance contribution rate and the preset contribution rate threshold. Then, according to the preset enterprise period planning, it is verified whether the first principal component number meets the actual business needs of the main enterprise. If it meets, the first principal component number is taken as the number of principal components. Otherwise, the contribution threshold is adjusted, and the first principal component number is determined according to the principal component variance contribution rate and the preset contribution rate threshold.

[0050] In some embodiments, step S32 first preliminarily determines the first principal component number by the cumulative contribution rate method, and then determines the final value of the first principal component number by the professional judgment method combined with the actual needs of the enterprise. In the process of verifying whether the first principal component number meets the actual business needs of the main enterprise according to the preset enterprise period planning, the load of the principal component is analyzed first, that is, the correlation between the principal component and each original variable is understood, then the meaning of the principal component is explained, and finally the number of principal components to be retained is determined according to the current actual business focus of the enterprise.

[0051] For example, in some embodiments, the proportion of carbon emission data in the first principal component is high, and the proportion of expenditure amount in the second principal component is high. If the actual needs of the enterprise are mainly to reduce carbon emissions, the first principal component needs to be retained, and the second principal component can be discarded.

[0052] In some embodiments, at step S33, the processor calculates the first comprehensive evaluation index according to the principal component score data set and the number of principal components.

[0053] In some embodiments, at step S33, the processor calculates, for each transport supply enterprise, the number of principal components as the number of principal components that need to be accumulated, and performs a weighted summation of the principal components in the principal component score data set, where the weight value is the variance contribution rate of each principal component, and the result is the first comprehensive evaluation index.

[0054] The comprehensive evaluation index comprehensively and objectively evaluates the performance level of the supplier in the current statistical period from multiple angles.

[0055] Figure 3 For Figure 2 The flowchart of the detailed steps of step S31 is shown in FIG. 3. As shown in FIG. 3, step S31 includes steps S311-S314.

[0056] At step S311, the first period data set is standardized by using the range standardization method to obtain a first standardized data set. In some embodiments, the specific steps of using the range standardization method to perform standardization at step S311 include calculating the maximum value and the minimum value of each index, and performing standardization on each index of each transport supply enterprise according to the following formula: ; For example, in some embodiments, the maximum value of the cargo transportation volume is 500 tons, the minimum value is 400 tons, and the cargo transportation volume of supplier A is 450 tons. After standardization calculation, the cargo transportation volume data of supplier A is 0.5.

[0057] At step S312, the covariance matrix and the correlation coefficient matrix of the first standardized data set are calculated. At step S313, the eigenvalues and eigenvectors of the covariance matrix and the correlation coefficient matrix are calculated.

[0058] In some embodiments, at steps S312 and S313, the eigenvalues and eigenvectors are obtained by calculating the data in the first standardized data set, and the eigenvalues and eigenvectors can reflect the contribution degree characteristics of each factor corresponding to each supplier.

[0059] At step S314, the principal component score data set of each transport supply enterprise is calculated according to the eigenvectors.

[0060] In some embodiments, the principal component score data set includes multiple principal component scores of multiple transport supply enterprises. Each principal component score is a weighted sum of the data in the first period data set, reflecting the comprehensive performance of the transport supply enterprise under the action of multiple variables, and each transport supply enterprise obtains multiple principal component scores by calculation. For example, the first principal component of transport supply enterprise A can be expressed as: Principal component 1 = w1 x carbon emission data + w2 x freight volume + w3 x expenditure amount + w4 x mileage Wherein, w1, w2, w3, w4 are elements of the eigenvector corresponding to the first principal component, indicating the weight of the multiple variables in the principal component.

[0061] Figure 4 To Figure 2 The flowchart of the refinement step of step S32 is shown in FIG. 4. As shown in FIG. 4, step S32 includes steps S321-S324.

[0062] In step S321, the processor calculates the principal component variance contribution rate of each transport service provider according to the principal component score data set. In step S322, the number of first principal components is determined according to the principal component variance contribution rate and the preset contribution rate threshold.

[0063] In some embodiments, through steps S321 and S322, the processor first determines the number of first principal components by the cumulative contribution rate method. Specifically, in step S321, the processor calculates the principal component variance contribution rate; in step S322, the sum of the first two, the first three, and the first n principal component variance contribution rates is sequentially calculated by accumulation, and after each summation, the sum is compared with the contribution rate threshold. When the sum of the contribution rates is first greater than or equal to the contribution rate threshold, the number of principal components that have been accumulated is selected as the number of first principal components.

[0064] In step S323, it is verified whether the number of first principal components meets the actual business needs of the main enterprise according to the preset enterprise cycle planning. If yes, the number of first principal components is obtained as the number of principal components; otherwise, in step S324, the contribution threshold is adjusted, and the number of first principal components is determined according to the principal component variance contribution rate and the preset contribution rate threshold.

[0065] In some embodiments, in steps S323 and S324, the processor determines the final number of first principal components according to the preset enterprise cycle planning. Specifically, verifying whether the number of first principal components meets the actual business needs of the main enterprise includes: first, performing a load analysis on the principal components, i.e., understanding the correlation between the principal components and each original variable, then explaining the meaning of the principal components, and finally determining the number of principal components to be retained through the focus of the actual business of the enterprise.

[0066] For example, in some embodiments, the proportion of carbon emission data in the first principal component is high, and the proportion of expenditure amount in the second principal component is high. The first principal component represents the comprehensive carbon emission situation, and the second principal component represents the comprehensive expenditure situation. Then, if the actual demand of the enterprise is to reduce carbon emission as the main task, the first principal component needs to be retained, and the second principal component can be discarded.

[0067] Figure 5 For Figure 1 Flowchart of the detailed steps of step S5 in embodiment 1000. As shown in Fig. 5, step S5 includes step S51-S52.

[0068] In some embodiments, in step S51, the processor determines the change score standard according to the first comprehensive evaluation index and the second comprehensive evaluation index. In step S52, the supply change data set is determined according to the change score standard, which includes the change score of each supplier.

[0069] Optionally, in step S51, for each transportation supply enterprise, the processor calculates the change rate of its corresponding comprehensive evaluation index and its distribution, and then further determines the corresponding relationship between the change rate interval range and the change score. This corresponding relationship reflects the score corresponding to the interval of the change rate, which can be used as the change score standard to measure the evaluation of the change of each transportation supply enterprise.

[0070] For example, in some embodiments, the score of the change rate greater than 10% is 5, the score of the change rate in the range of 5%-10% is 4, the score of the change rate in the range of 0-5% is 3, the score of the change rate in the range of -5%-0 is 2, the score of the change rate in the range of -10%- -5% is 1, and the score of the change rate less than -10% is 0. For example, the change rate of supplier A is 3%, and the change score of supplier A is 3.

[0071] Figure 6 For Figure 1 Flowchart of the detailed steps of step S6 in embodiment 1000. As shown in Fig. 6, step S6 includes step S61-S63.

[0072] In step S6, the processor calculates the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set according to various data in the second period data set, which respectively reflect the efficiency of the transportation supply enterprise in carbon emission, transportation capacity and expenditure amount.

[0073] In some embodiments, in step S61, the processor calculates the carbon emission efficiency data set according to the second carbon emission data set and the second cargo transportation volume data set. Optionally, the carbon emission efficiency data is the ratio of the cargo transportation volume data and the carbon emission data, which represents the carbon emission value of the transportation supply enterprise when transporting unit mass of cargo.

[0074] In some embodiments, in step S62, the processor calculates a transportation efficiency data set according to the second transportation mileage data set and the second cargo transportation volume data set. Optionally, the transportation efficiency data is the ratio of the cargo transportation volume data and the transportation mileage data, indicating the mileage needed for transporting unit mass of cargo.

[0075] In some embodiments, in step S63, the processor calculates an expenditure efficiency data set according to the second expenditure amount data set and the second transportation mileage data set. Optionally, the expenditure efficiency data is the ratio of the cargo transportation volume data and the expenditure amount data, indicating the amount spent for transporting unit mass of cargo.

[0076] Figure 7 For Figure 1 A flowchart of the detailed steps of step S7 in embodiment 1000 is shown in FIG. 7. As shown in FIG. 7, step S7 includes steps S71-S74.

[0077] In some embodiments, in step S71, the processor constructs a hierarchical model for calculating the enterprise score list, the hierarchical model including a target layer, a criterion layer, and a scheme layer; the target layer including the enterprise score list, the criterion layer including four factors of supply variation, carbon emission efficiency, transportation efficiency, and expenditure efficiency. In step S71, the scheme layer includes each specific transportation supply enterprise.

[0078] In step S72, the processor constructs a judgment matrix, using the 1-9 scale method to quantify the relative importance between each two of the supply variation, carbon emission efficiency, transportation efficiency, and expenditure efficiency, the specific meanings of the scales being as follows: 1, indicating that the two factors are equally important; 3, indicating that one factor is slightly more important; 5, indicating that one factor is obviously more important; 7, indicating that one factor is much more important; 9, indicating that one factor is absolutely more important; 2, 4, 6, and 8 being intermediate values of the above judgments, indicating situations between two adjacent importance levels.

[0079] For example, in some embodiments, the supply variation is slightly more important than the carbon emission efficiency, and thus a scale of 3 is assigned to the corresponding position of the judgment matrix; the supply variation is obviously more important than the transportation efficiency, and thus a scale of 5 is assigned.

[0080] In some embodiments, the constructed judgment matrix is a 4x4 matrix, in which the rows and columns respectively contain the four criterion layer factors; the values on the diagonal of the matrix are 1, because the values on the diagonal represent the comparison results of the factors with themselves; the elements above the diagonal of the matrix are the importance values assigned according to the scales; and the elements below the diagonal of the matrix are the reciprocals of the elements corresponding to the elements above the diagonal of the matrix.

[0081] In some embodiments, in step S73, the processor performs normalization processing and consistency check on the judgment matrix to obtain a weight data set, the specific steps including: The normalization processing includes: normalizing each column of the judgment matrix, and calculating the proportion of each element in the total of the column. Then, the normalized matrix is added by row to obtain the weight value of each factor. Finally, the weight values are normalized so that the sum is 1; The consistency check includes: calculating the consistency index (CI), the random consistency index (RI), and the consistency ratio (CR), wherein: CI = (λmax - n) / (n - 1), where λmax is the maximum eigenvalue of the judgment matrix, and n is the order of the judgment matrix (here, 4); RI is the random consistency index, and for a 4-order judgment matrix, RI is about 0.90; CR = CI / RI, if CR < 0.1, the judgment matrix has satisfactory consistency.

[0082] For example, in some embodiments, after calculation, CI = 0.05, RI = 0.90, then CR = 0.05 / 0.90 ≈ 0.0556 < 0.1, the judgment matrix passes the consistency check.

[0083] In some embodiments, in step S74, the processor calculates the enterprise score list according to the weight data set, the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set, and the expenditure efficiency data set. Step S74 specifically includes: according to the weight data in the weight data set, weighting and summing the data of each transportation supply enterprise in the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set, and the expenditure efficiency data set, and sorting, thereby obtaining the enterprise score list.

[0084] Figure 8 A flowchart of an embodiment 2000 of the enterprise transportation supplier evaluation method of the present application which fuses carbon emission factors. As shown in FIG. 8, the method in embodiment 2000 includes steps S201-S211. Among them, steps S201-S207 are the same as steps S1-S7 in embodiment 1000 in FIG. 1, and will not be repeated here.

[0085] In some embodiments, in step S208, the processor determines the unqualified condition data of the transportation supply enterprise according to the enterprise score list. Optionally, the unqualified condition data includes the number of consecutive unqualified periods and the cumulative number of unqualified periods.

[0086] In some embodiments, in step S208, the processor compares the score of each transport supply enterprise in the enterprise score list with the preset unqualified threshold, and adds 1 to the cumulative unqualified period number of the enterprise when the enterprise score is lower than the unqualified threshold. At the same time, it is judged whether the score of the enterprise in the previous statistical period is lower than the unqualified threshold, and the value of the continuous unqualified period number is determined according to the unqualified situation in the previous statistical period.

[0087] In step S209, the processor determines the eliminated supply chain enterprise list according to the preset elimination rule and the unqualified situation data. In some embodiments, the elimination rule includes: the continuous unqualified period number exceeds a first threshold, or, the cumulative unqualified period number exceeds a second threshold, or, the minimum required score difference exceeds a third threshold. Among them, the cumulative unqualified period number and the continuous unqualified period number represent the long-term comprehensive performance and the short-term comprehensive performance of the transport supply enterprise, and the minimum required score difference represents the difference between the enterprise score and the minimum requirement, that is, the lower limit of the enterprise performance. In some embodiments, in step S209, the transport supply enterprise that meets the elimination rule has already failed to meet the business capability requirements of the main enterprise, and can be eliminated so as not to undertake the transport business of the enterprise.

[0088] In some embodiments, the processor completes the determination of the optimized supply chain enterprise list in steps S210 and S211. In step S201, the processor removes the eliminated supply chain enterprises in the enterprise score list to obtain an updated enterprise score list, and the updated enterprise score list includes transport supply enterprises that meet the enterprise comprehensive capability requirements.

[0089] In step S211, the processor determines the optimized supply chain enterprise list according to the updated enterprise score list and the transport demand of the enterprise. Specifically, it includes: First, the processor determines whether the total sum of the freight transportation volume of the enterprises in the updated enterprise score list can meet the transport demand according to the freight transportation volume data in the second freight transportation volume data set; In the case that the total sum of the freight transportation volume can meet the transport demand, the processor accumulates and sums the freight transportation volume of the enterprises in the updated enterprise score list, and when the value of the accumulated sum of the freight transportation volume is first greater than or equal to the transport demand, the enterprise that has been accumulated is taken as the optimized supply chain enterprise list; In the case that the total sum of the freight transportation volume cannot meet the transport demand, the processor obtains the candidate carbon emission data set, candidate transportation efficiency data set, candidate expenditure efficiency data set and freight transportation capacity data set of the enterprise in the last period from the green supply candidate library; The processor determines a complementary chain enterprise list according to the alternative carbon emission data set, the alternative transportation efficiency data set, the alternative expenditure efficiency data set, and the freight transportation capacity data set, merges the updated enterprise score list and the complementary chain enterprise list as an optimized supply chain enterprise list.

[0090] Figure 9 To Figure 8 A flowchart of the detailed steps of step S211 in the embodiment 2000 is shown in FIG. 9. As shown in FIG. 9, step S211 includes steps S2111-S2115.

[0091] In step S2111, the processor determines whether the total freight transportation volume of the enterprises in the updated enterprise score list can meet the transportation demand volume according to the freight transportation volume data in the second freight transportation volume data set. The transportation demand volume is the total demand for freight transportation of the main enterprise in the next statistical period. In step S2111, the processor extracts the freight transportation volume data corresponding to the transportation supply enterprises in the enterprise score list and sums the data to determine whether the transportation supply enterprises after the elimination of the eliminated enterprises can meet the transportation demand of the enterprises.

[0092] In step S2112, in the case where the total freight transportation volume can meet the transportation demand volume, the processor accumulatively sums the freight transportation volume of the enterprises in the updated enterprise score list, and when the value of the accumulative sum of the freight transportation volume is first greater than or equal to the transportation demand volume, the enterprises that have been accumulated are selected as the optimized supply chain enterprise list.

[0093] In some embodiments, in step S2112, the transportation supply enterprises after the elimination of the eliminated enterprises can meet the transportation demand of the enterprises, and then, according to the ranking of the enterprises in the updated enterprise score list, a plurality of transportation supply enterprises with higher scores and which can just meet the transportation demand of the enterprises are selected as the transportation suppliers.

[0094] In step S2113, in the case where the total freight transportation volume cannot meet the transportation demand volume, the processor obtains the alternative carbon emission data set, the alternative transportation efficiency data set, and the alternative expenditure efficiency data set, and the freight transportation capacity data set of the enterprises in the green supply alternative library in the last period. In step S2114, the processor determines a complementary chain enterprise list according to the alternative carbon emission data set, the alternative transportation efficiency data set, the alternative expenditure efficiency data set, and the freight transportation capacity data set. In step 2115, the processor merges the updated enterprise score list and the complementary chain enterprise list as an optimized supply chain enterprise list. In step S2115, the processor merges the updated enterprise score list and the complementary chain enterprise list as an optimized supply chain enterprise list.

[0095] In some embodiments, in step S2113 and step S2114, the processor screens the enterprise from the green supply alternative library for chain supplement. In which, the screening of the supply enterprise also needs to comprehensively consider the carbon emission data, transportation efficiency, expenditure efficiency and cargo transportation capacity of the supplier.

[0096] In some embodiments, in step S2114, the processor determines the chain supplement enterprise according to the green collaboration chain supplement principle. Specifically, in some embodiments, the processor uses an analytic hierarchy process model to determine the weight proportion of the four factors of carbon emission data, transportation efficiency, expenditure efficiency and cargo transportation capacity, and then determines the comprehensive score of the enterprise in the green supply alternative library according to the weight proportion, and determines the chain supplement enterprise list according to the comprehensive score of the enterprise and the cargo transportation capacity of the enterprise.

[0097] In some embodiments, in step S2114, the processor determines the chain supplement enterprise list according to the system carbon emission minimization principle. Specifically, the enterprise obtains an alternative carbon emission data set from the green supply alternative library, and calculates the total carbon emission after chain supplement, so that the carbon emission data after chain supplement is minimized.

[0098] Figure 10 An embodiment 3000 of a device for evaluating a transportation supplier of an enterprise considering carbon emission factors of the present application is shown in a schematic diagram. As shown in the embodiment 3000, the device includes a data receiving module 31 and a data processing module 32. Figure 10

[0099] In some embodiments, the data receiving module 31 receives the first carbon emission data set and the second carbon emission data set sent by the plurality of transportation supply enterprises of the main enterprise.

[0100] In some embodiments, the main enterprise has a plurality of transportation supply enterprises providing logistics transportation services, and the transportation supply enterprises jointly form the transportation supply chain of the main enterprise. Optionally, the main enterprise informs each transportation supply enterprise in advance of the data template and requirements of the carbon emission data to be collected, which includes the specific form of the carbon emission data to be provided. For example, the main enterprise requires the transportation supply enterprise to provide the carbon emission amount subdivided by transportation vehicle and transportation route, and specifies the time granularity and total time range of the provided data. For example, the carbon emission data provided by the transportation supply enterprise is statistically monthly.

[0101] ​In some embodiments, the first carbon emission data set is the carbon emission data of the transport supply enterprise in the first statistical period, and the second carbon emission data set is the carbon emission data of the transport supply enterprise in the first statistical period. Optionally, the time granularity and the time range of the first statistical period and the second statistical period should be consistent, and other influencing factors should be avoided as much as possible. For example, when selecting the statistical period, the climate conditions between the selected first statistical period and the second statistical period should not be greatly different, so as to avoid affecting the calculation result. A preferred embodiment is that the first statistical period and the second statistical period are two consecutive natural years.

[0102] In some embodiments, the data processing module 32 obtains the first freight transportation volume data set, the second freight transportation volume data set, the first expenditure amount data set, the second expenditure amount data set, the first transportation mileage data set and the second transportation mileage data set of the plurality of transport supply enterprises according to the operation data of the main enterprise.

[0103] In some embodiments, the data processing module 32 extracts the expenditure amount, the freight transportation volume and the transportation mileage data of each transport supply enterprise in the first statistical period and the second statistical period from the operation data in the main enterprise. The operation data of the enterprise includes the data in the database of the financial management system, the logistics management system and the like. In some embodiments, when the main enterprise extracts data from the internal system, it should be ensured that the extracted data is consistent with the data obtained by the data receiving module 31 in terms of time granularity and time range.

[0104] In some embodiments, the first freight transportation volume data set and the second freight transportation volume data set are the freight transportation volume data of each transport supply enterprise of the main enterprise in the first statistical period and the second statistical period, respectively; the first expenditure amount data set and the second expenditure amount data set are the expenditure amount data of each transport supply enterprise of the main enterprise in the first statistical period and the second statistical period, respectively; and the first transportation mileage data set and the second transportation mileage data set are the transportation mileage data of each transport supply enterprise of the main enterprise in the first statistical period and the second statistical period, respectively. Optionally, the unit of the freight transportation volume data is ton, the unit of the transportation mileage data is kilometer, and the unit of the expenditure amount data is ten thousand yuan.

[0105] In some embodiments, the data processing module 32 takes the first carbon emission data set, the first freight transportation volume data set, the first expenditure amount data set and the first transportation mileage data set as a first period data set, and calculates the first comprehensive evaluation index according to the first period data set.

[0106] In some embodiments, the data processing module 32 calculates the principal component score data set of each of the transport supply enterprises according to the first period data set. In some embodiments, the data processing module 32 first normalizes the data in the first period data set, then calculates the covariance matrix and the correlation coefficient matrix according to the processed normalized data, further calculates the eigenvalue and the eigenvector, and finally calculates the principal component score of each of the transport supply enterprises according to the eigenvector, and generates the principal component score data set.

[0107] In some embodiments, the data processing module 32 determines the number of principal components for calculating the first comprehensive evaluation index according to the principal component score data set. In some embodiments, the data processing module 32 calculates the principal component variance contribution rate of each of the transport supply enterprises according to the principal component score data set, then determines the first principal component number according to the principal component variance contribution rate and the preset contribution rate threshold, and finally verifies whether the first principal component number meets the actual business needs of the principal enterprise according to the preset enterprise period planning, if yes, the first principal component number is taken as the number of principal components, otherwise, the contribution rate threshold is adjusted, and the first principal component number is determined according to the principal component variance contribution rate and the preset contribution rate threshold.

[0108] In some embodiments, the data processing module 32 calculates the first comprehensive evaluation index according to the principal component score data set and the number of principal components. In some embodiments, for each of the transport supply enterprises, the data processing module 32 takes the number of principal components as the number of principal components to be accumulated, and performs weighted summation on the principal components in the principal component score data set, wherein the weight value is the variance contribution rate of each principal component, and the result is the first comprehensive evaluation index.

[0109] In some embodiments, the data processing module 32 takes the second carbon emission data set, the second freight transport volume data set, the second expenditure amount data set and the second transport mileage data set as the second period data set, and calculates the second comprehensive evaluation index according to the second period data set. Optionally, the data processing module 32 adopts the same method as that for processing the data in the first period data set, and only the processed data is different. Therefore, the detailed steps of calculating the second comprehensive evaluation index according to the second period data set are not described herein.

[0110] In some embodiments, the data processing module 32 calculates the supply change data set according to the first comprehensive evaluation index and the second comprehensive evaluation index. In some embodiments, the data processing module 32 determines the change score standard according to the first comprehensive evaluation index and the second comprehensive evaluation index, and further determines the supply change data set according to the change score standard, wherein the supply change data set includes the change condition score of each of the suppliers.

[0111] Optionally, for each transport supply enterprise, the data processing module 32 calculates the change rate of its corresponding comprehensive evaluation index and its distribution, and then further determines the corresponding relationship between the change rate interval range and the change score. This corresponding relationship reflects the score corresponding to the interval of the change rate, which can be used as a change score standard to measure the evaluation of the change of each transport supply enterprise.

[0112] In some embodiments, the data processing module 32 calculates the carbon emission efficiency data set, the transport efficiency data set and the expenditure efficiency data set according to the second period data set.

[0113] In some embodiments, the data processing module 32 calculates the carbon emission efficiency data set according to the second carbon emission data set and the second freight transport volume data set, calculates the transport efficiency data set according to the second transport mileage data set and the second freight transport volume data set, and calculates the expenditure efficiency data set according to the expenditure amount data set and the second transport mileage data set.

[0114] In some embodiments, the data processing module 32 calculates the enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transport efficiency data set and the expenditure efficiency data set.

[0115] In some embodiments, the data processing module 32 constructs a hierarchical structure model for calculating the enterprise score list. Optionally, the hierarchical structure model includes a target layer, a criterion layer and a scheme layer; the target layer includes the enterprise score list, and the criterion layer includes four factors of supply variation, carbon emission efficiency, transport efficiency and expenditure efficiency.

[0116] In some embodiments, the data processing module 32 constructs a judgment matrix and quantifies the relative importance between supply variation, carbon emission efficiency, transport efficiency and expenditure efficiency by using the 1-9 scale method. Optionally, the specific meanings of the scale are as follows: 1, indicating that the two factors are equally important; 3, indicating that one factor is slightly important; 5, indicating that one factor is obviously important; 7, indicating that one factor is very important; 9, indicating that one factor is absolutely important; 2, 4, 6 and 8 are intermediate values of the above judgments, indicating the situation between two adjacent importance levels.

[0117] In some embodiments, the data processing module 32 performs normalization processing and consistency check on the judgment matrix to obtain a weight data set. In some embodiments, the data processing module 32 calculates the enterprise score list according to the weight data set, the supply variation data set, the carbon emission efficiency data set, the transport efficiency data set and the expenditure efficiency data set.

[0118] The above has carried out the detailed introduction to the embodiment of the application, the principle and implementation mode of the application have been described in this paper by applying specific examples, the above embodiment description is only used for helping understanding the method of the application and its core idea. Meanwhile, the changes or deformations made by the person skilled in the art according to the idea of the application, based on the specific implementation mode and application range of the application, all belong to the protection range of the application. In summary, the content of the specification should not be understood as the limitation of the application.

Claims

1. A method of evaluating a transportation provider for a business with a factor of carbon emissions, characterized by, Comprising: S1. Receiving a plurality of transportation supply enterprises of a main enterprise sending a first carbon emission data set and a second carbon emission data set; S2. Obtaining a first freight transportation volume data set, a second freight transportation volume data set, a first expenditure amount data set, a second expenditure amount data set, a first transportation mileage data set and a second transportation mileage data set of the plurality of transportation supply enterprises according to operation data of the main enterprise; S3. Taking the first carbon emission data set, the first freight transportation volume data set, the first expenditure amount data set and the first transportation mileage data set as a first period data set, and calculating a first comprehensive evaluation index according to the first period data set; S4. Taking the second carbon emission data set, the second freight transportation volume data set, the second expenditure amount data set and the second transportation mileage data set as a second period data set, and calculating a second comprehensive evaluation index according to the second period data set; S5. Calculating a supply variation data set according to the first comprehensive evaluation index and the second comprehensive evaluation index; S6. Calculating a carbon emission efficiency data set, a transportation efficiency data set and an expenditure efficiency data set according to the second period data set; S7. Calculating an enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

2. The method of claim 1, wherein the carbon emission factor of the enterprise transportation provider is fused. In step S3, the first comprehensive evaluation index is calculated according to the first period data set, comprising: calculating a principal component score data set of each transportation supply enterprise according to the first period data set; determining a principal component number for calculating the first comprehensive evaluation index according to the principal component score data set; calculating the first comprehensive evaluation index according to the principal component score data set and the principal component number.

3. The method of claim 2, wherein the carbon emission factor is integrated into the evaluation of the transportation provider. The principal component score data set of each transportation supply enterprise is calculated according to the first period data set, comprising: standardizing the first period data set by range standardization method to obtain a first standardized data set; calculating a covariance matrix and a correlation coefficient matrix of the first standardized data set; calculating eigenvalues and eigenvectors of the covariance matrix and the correlation coefficient matrix; calculating the principal component score data set of each transportation supply enterprise according to the eigenvectors.

4. The method of claim 2, wherein the carbon emission factor of the enterprise transportation provider is fused. The principal component number for calculating the first comprehensive evaluation index is determined, comprising: calculating a principal component variance contribution rate of each transportation supply enterprise according to the principal component score data set; determining a first principal component number according to the principal component variance contribution rate and a preset contribution rate threshold; verifying whether the first principal component number meets actual business needs of the main enterprise according to a preset enterprise period plan, if yes, taking the first principal component number as the principal component number; otherwise, adjusting the contribution rate threshold, and returning to step S3 to determine the first principal component number according to the principal component variance contribution rate and the preset contribution rate threshold.

5. The method of claim 1, wherein the carbon emission factor of the enterprise transportation provider is fused. The carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set are calculated according to the second period data set in step S6, comprising: calculating the carbon emission efficiency data set according to the second carbon emission data set and the second freight transport volume data set; calculating the transport efficiency data set according to the second transport mileage data set and the second freight transport volume data set; calculating the expenditure efficiency data set according to the second expenditure amount data set and the second transport mileage data set.

6. The method of claim 1, wherein the carbon emission factor of the enterprise transportation provider is fused. Further comprising: determining unqualified condition data of the transport supply enterprises according to the enterprise score list; determining a list of eliminated enterprises and a list of eliminated supply chain enterprises according to preset elimination rules and the unqualified condition data; removing the eliminated supply chain enterprises in the enterprise score list to obtain an updated enterprise score list; determining an optimized supply chain enterprise list according to the updated enterprise score list and transport demand of enterprises.

7. The method of claim 6, wherein the carbon emission factor is integrated into the evaluation of the transportation provider. The determining an optimized supply chain enterprise list according to the updated enterprise score list and transport demand of enterprises comprises: judging whether the total freight transport volume of enterprises in the updated enterprise score list can meet the transport demand according to the freight transport volume data in the second freight transport volume data set; in the case that the total freight transport volume can meet the transport demand, accumulating and summing the freight transport volume of enterprises in the updated enterprise score list, and when the value of the accumulated sum is first greater than or equal to the transport demand, the enterprises that have been accumulated are taken as the optimized supply chain enterprise list; in the case that the total freight transport volume cannot meet the transport demand, obtaining the alternative carbon emission data set, the alternative transport efficiency data set, the alternative expenditure efficiency data set and the freight transport capacity data set of enterprises in the green supply alternative library in the last period; determining a chain supplement enterprise list according to the alternative carbon emission data set, the alternative transport efficiency data set, the alternative expenditure efficiency data set and the freight transport capacity data set; merging the updated enterprise score list and the chain supplement enterprise list as the final optimized supply chain enterprise list.

8. The method of claim 1, wherein the carbon emission factor of the enterprise transportation provider is fused. The S7. calculating an enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transport efficiency data set and the expenditure efficiency data set comprises: constructing a hierarchical structure model for calculating the enterprise score list, the hierarchical structure model comprising a target layer, a criterion layer and a scheme layer; the target layer comprising the enterprise score list, and the criterion layer comprising four factors of supply variation, carbon emission efficiency, transport efficiency and expenditure efficiency; constructing a judgment matrix, and quantifying the relative importance between the supply variation, the carbon emission efficiency, the transport efficiency and the expenditure efficiency by using a 1-9 scale method; the specific meanings of the scale are as follows: 1, indicating that two factors are equally important; 3, indicating that one factor is slightly important; 5, indicating that one factor is obviously important; 7, indicating that one factor is very important; 9, indicating that one factor is absolutely important; 2, 4, 6 and 8 are intermediate values of the above judgments, indicating the cases between two adjacent importance levels; The judgment matrix is normalized and consistency checked to obtain a weight data set; The enterprise score list is calculated according to the weight data set, the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

9. The method of claim 1, wherein the carbon emission factor of the enterprise transportation provider is fused. The S5. The supply variation data set is calculated according to the first comprehensive evaluation index and the second comprehensive evaluation index, and includes: The change score standard is determined according to the first comprehensive evaluation index and the second comprehensive evaluation index; The supply variation data set is determined according to the change score standard, and the supply variation data set includes the change score of each supplier.

10. An enterprise transportation provider evaluation apparatus that fuses carbon emission factors, characterized by, The S5. The supply variation data set is calculated according to the first comprehensive evaluation index and the second comprehensive evaluation index, and includes: The data receiving module is configured to receive a first carbon emission data set and a second carbon emission data set sent by a plurality of transportation supply enterprises of a main enterprise; The data processing module is configured to obtain a first cargo transportation volume data set, a second cargo transportation volume data set, a first expenditure amount data set, a second expenditure amount data set, a first transportation mileage data set and a second transportation mileage data set of the plurality of transportation supply enterprises according to the operation data of the main enterprise; The data processing module is further configured to take the first carbon emission data set, the first cargo transportation volume data set, the first expenditure amount data set and the first transportation mileage data set as a first period data set, and calculate a first comprehensive evaluation index according to the first period data set; The data processing module is further configured to take the second carbon emission data set, the second cargo transportation volume data set, the second expenditure amount data set and the second transportation mileage data set as a second period data set, and calculate a second comprehensive evaluation index according to the second period data set; The data processing module is further configured to calculate a supply variation data set according to the first comprehensive evaluation index and the second comprehensive evaluation index, calculate a carbon emission efficiency data set, a transportation efficiency data set and an expenditure efficiency data set according to the second period data set, and calculate an enterprise score list according to the supply variation data set, the carbon emission efficiency data set, the transportation efficiency data set and the expenditure efficiency data set.

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