Medicine cold-chain logistics enterprise recommendation method based on Fermat fuzzy set

Abstract

The invention discloses a medicine cold-chain logistics enterprise recommendation method based on a Fermat fuzzy set. The method comprises the steps of obtaining a transportation quality data set of a plurality of alternative logistics enterprises under evaluation indexes; constructing an expert evaluation matrix through the Fermat fuzzy linguistic variables, and evaluating the importance of experts through the Fermat fuzzy linguistic variables so as to determine the weight of each expert; giving a language evaluation matrix through a Fermat fuzzy language variable and converting the language evaluation matrix into a Fermat fuzzy number; obtaining a Fermat fuzzy group evaluation matrix based on the expert weight and a Fermat fuzzy Einstein interactive integration operator, and performing standardization; based on a Fermat fuzzy set score function and an Einstein interactive integration operator, objective and subjective weights of evaluation indexes are obtained through a Fermat fuzzy variable coefficient method and a Fermat fuzzy step-by-step weight evaluation ratio analysis method respectively, and then a comprehensive index weight is obtained; and according to a Fermat fuzzy set-based scoring function and an Einstein integration operator, obtaining a sequence through an improved comprehensive compromise solution method so as to determine an optimal enterprise.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method for recommending pharmaceutical cold chain logistics enterprises based on Fermat fuzzy sets. Background Art

[0002] Pharmaceutical cold chain logistics, a core component of ensuring drug quality and safety, encompasses the entire process of transporting, storing, and distributing temperature-sensitive pharmaceuticals. Transport quality is directly linked to drug effectiveness and patient health. Especially with the surge in demand for high-end pharmaceutical products like vaccines and biologics, establishing an accurate and reliable transport quality recommendation system has become an industry imperative. However, existing recommendation methods have significant shortcomings in addressing complex uncertainties, dynamic risks, green logistics, and the integration of multi-dimensional indicators, limiting the scientific and practical nature of evaluation results.

[0003] Fermat fuzzy sets, as an emerging uncertainty modeling tool, have a stronger information-carrying capacity than traditional fuzzy sets by expanding the cubic space of membership and non-membership. They can accurately characterize the complex state of "high uncertainty but critical risk" in the pharmaceutical cold chain. However, existing research has not yet recommended the integration of Fermat fuzzy sets with pharmaceutical cold chain transportation quality, and their advantages have not been fully explored. Specifically, there is a lack of a multi-level indicator hierarchical aggregation algorithm based on Fermat fuzzy sets, making it difficult to achieve an evaluation mapping from the device level to the system level; a dynamic weight optimization mechanism for Fermat fuzzy sets is not designed, making it impossible to reflect the impact of sudden risks on indicator importance in real time; and a data-driven evaluation framework that integrates Fermat fuzzy sets with machine learning is lacking, which restricts the value extraction of large-scale cold chain data. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets, which can improve the robustness and adaptability of the evaluation system.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for recommending pharmaceutical cold chain logistics enterprises based on Fermat fuzzy sets, comprising the following steps:

[0006] S10, obtaining a transportation quality data set of multiple candidate logistics companies under evaluation indicators;

[0007] S20, construct an expert evaluation matrix for each logistics enterprise's transportation quality information under the evaluation index through Fermat fuzzy linguistic variables; evaluate the importance of experts through Fermat fuzzy linguistic variables and then determine the weight of each expert;

[0008] S30, giving a linguistic evaluation matrix of each logistics enterprise under the evaluation index through Fermat fuzzy linguistic variables and converting it into a Fermat fuzzy number; at the same time, obtaining a Fermat fuzzy group evaluation matrix based on expert weights and Fermat fuzzy Einstein interactive integration operator; and determining a standardized Fermat fuzzy group evaluation matrix;

[0009] S40, the objective weight and subjective weight of the evaluation index are obtained respectively by the Fermat fuzzy coefficient of variation method and the Fermat fuzzy stepwise weight evaluation ratio analysis method, and then the comprehensive index weight of the evaluation index is obtained according to the linear integration method;

[0010] S50, based on the Fermat fuzzy set scoring function and Einstein integration operator, obtains the ranking of alternative logistics companies through an improved comprehensive compromise solution method, thereby determining the company with the best logistics transportation quality.

[0011] Furthermore, the step S20 includes the steps of:

[0012] There are p alternative pharmaceutical cold chain logistics companies represented by H = {H1, H2, ..., H p ] and q pharmaceutical cold chain logistics transportation quality evaluation index sets are expressed as C = {C1, C2, ..., C q}, evaluation index C j The weight is and satisfy

[0013] E={E1,E2,…,E L} is a group of evaluation experts participating in the evaluation, expert E l The weight is γ l , and satisfies γ l >0,

[0014] Experts' Fermat fuzzy evaluation matrix for pharmaceutical cold chain logistics companies under the evaluation indicators is:

[0015]

[0016] in, For Expert E l For pharmaceutical cold chain logistics companies i In the evaluation index C j Fermat fuzzy transport quality information under Experts' assessment of the degree of subordination of pharmaceutical cold chain logistics companies under the evaluation indicators. For Expert E l For pharmaceutical cold chain logistics companies i In the evaluation index C jThe non-membership degree under [1] is [1]; p is the total number of pharmaceutical cold chain logistics companies, and q is the total number of Fermat fuzzy transportation quality information.

[0017] Furthermore, the importance of experts is evaluated by Fermat fuzzy linguistic variables to determine the weight of each expert:

[0018]

[0019] Among them, γ l is the expert weight of the lth expert, E l is the lth expert, L is the total number of experts, χ l is the membership degree of the lth expert, φ l is the non-membership degree of the lth expert.

[0020] Furthermore, in step S30, the Fermat fuzzy group evaluation matrix is ​​determined to be:

[0021]

[0022] where f ij =(χ ij ,φ ij ) is the Fermat fuzzy group evaluation information

[0023]

[0024] The standardized Fermat fuzzy group evaluation matrix is:

[0025] in, is the standardized Fermat fuzzy evaluation information.

[0026] Furthermore, in the step S40, it includes:

[0027] Determining the objective weights of evaluation indicators includes the following steps:

[0028] (1) The standardized Fermat fuzzy group evaluation matrix Converted into a score function matrix

[0029] (2) According to the score function matrix, obtain the mean value of the jth evaluation index

[0030] (3) Based on the mean, calculate the mean square error D of the jth evaluation index j ;

[0031] (4) Calculate the coefficient of variation Z of the jth evaluation indicator based on the mean square error and the mean j ;

[0032] (5) Normalize the coefficient of variation of each evaluation indicator to obtain the objective weight of each indicator

[0033] Determining the subjective weights of evaluation indicators includes the following steps:

[0034] (1) The subjective importance of the evaluation indicators is given by Fermat fuzzy language terms, and then the fusion value of the subjective importance of the evaluation indicators is obtained by using Fermat fuzzy Einstein interactive weighted average, and the score of the fusion value is obtained by using Fermat fuzzy score function;

[0035] (2) Obtain the ranking of the selected evaluation indicators by arranging the score values ​​in descending order;

[0036] (3) Calculate the comparative importance coefficient σ of the evaluation index j :

[0037] (4) According to the comparative importance coefficient, the initial subjective weight of the evaluation index is obtained. j ;

[0038] (5) Based on the initial subjective weight, the final subjective weight of the evaluation index is obtained The comprehensive weight of the evaluation indicators is determined as follows: Based on the determined objective weight of the indicators and the determined subjective weight of the indicators, the comprehensive weight of the evaluation indicators is determined.

[0039] Furthermore, the step S40 includes the steps of:

[0040] S401, determining the Fermat fuzzy weighted sum evaluation information of each candidate enterprise according to the Fermat fuzzy Einstein interactive weighted average integration operator;

[0041] S402, determining Fermat fuzzy weighted product evaluation information of each candidate enterprise according to a Fermat fuzzy Einstein interactive weighted geometric integration operator;

[0042] S403, determining a score value of the Fermat fuzzy weighted sum evaluation information of each candidate enterprise according to the Fermat fuzzy score function;

[0043] S404, determining a score value of the Fermat fuzzy weighted product evaluation information of each candidate enterprise according to the Fermat fuzzy score function;

[0044] S405, respectively calculating the arithmetic average strategy value, relative score strategy value and compromise balance strategy value of each candidate enterprise;

[0045] S406, determining the comprehensive evaluation value of the alternative solutions and obtaining the ranking of the alternative logistics companies, thereby determining the company with the best logistics and transportation quality.

[0046] Furthermore, in step S40:

[0047] Determine the Fermat fuzzy weighted sum evaluation information of each candidate enterprise as follows:

[0048]

[0049] The Fermat fuzzy weighted product evaluation information of each candidate enterprise is determined as:

[0050]

[0051] Furthermore, in step S40:

[0052] The score of the Fermat fuzzy weighted sum evaluation information for each candidate enterprise is determined as follows:

[0053]

[0054] The score value of the Fermat fuzzy weighted product evaluation information of each candidate enterprise is determined as follows:

[0055]

[0056] Furthermore, in step S40, the arithmetic mean strategy value of each candidate enterprise is determined. Relative scoring strategy value and trade-off balance strategy value They are:

[0057]

[0058]

[0059] Furthermore, the ranking of candidate companies is determined as follows:

[0060]

[0061] The beneficial effects of adopting this technical solution are:

[0062] This paper, for the first time, incorporates Fermat fuzzy set theory into the evaluation of pharmaceutical cold chain logistics and transportation quality, constructing a comprehensive evaluation model that integrates dynamic weight adjustment, multi-source data fusion, and emergency scenario adaptation. By designing membership generation rules compatible with Fermat fuzzy sets, developing a weight update algorithm based on a time-decay function, and establishing a weighted fusion mechanism for conflicting evidence, the robustness and adaptability of the evaluation system in uncertain environments are significantly improved, providing core technical support for the precise supervision and continuous optimization of the pharmaceutical cold chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1The figure is a flow chart of a pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.

[0065] In this embodiment, see Figure 1 As shown, the present invention proposes a method for recommending pharmaceutical cold chain logistics enterprises based on Fermat fuzzy sets, comprising the following steps:

[0066] S10, obtaining a transportation quality data set of multiple candidate logistics companies under evaluation indicators;

[0067] S20, construct an expert evaluation matrix for each logistics enterprise's transportation quality information under the evaluation index through Fermat fuzzy linguistic variables; evaluate the importance of experts through Fermat fuzzy linguistic variables and then determine the weight of each expert;

[0068] S30, giving a linguistic evaluation matrix of each logistics enterprise under the evaluation index through Fermat fuzzy linguistic variables and converting it into a Fermat fuzzy number; at the same time, obtaining a Fermat fuzzy group evaluation matrix based on expert weights and Fermat fuzzy Einstein interactive integration operator; and determining a standardized Fermat fuzzy group evaluation matrix;

[0069] S40, the objective weight and subjective weight of the evaluation index are obtained respectively by the Fermat fuzzy coefficient of variation method and the Fermat fuzzy stepwise weight evaluation ratio analysis method, and then the comprehensive index weight of the evaluation index is obtained according to the linear integration method;

[0070] S50, based on the Fermat fuzzy set scoring function and Einstein integration operator, obtains the ranking of alternative logistics companies through an improved comprehensive compromise solution method, thereby determining the company with the best logistics transportation quality.

[0071] The transport quality includes:

[0072] Refrigeration Equipment Performance: Refrigeration efficiency, insulation performance, and failure rates of refrigerated trucks, air containers, cold storage, and other equipment. Clean Energy Use: Storage facilities powered by new energy vehicles (e.g., electric trucks, hydrogen fuel cell vehicles) and renewable energy (solar, wind). Carbon Emission Intensity: Carbon emissions per unit of freight transport distance (e.g., g CO2 / ton-kilometer). Emissions can be reduced by optimizing routes and increasing loading rates. Energy Consumption Control: Reducing idle travel and idling time of transportation vehicles and improving engine or battery efficiency.

[0073] Temperature-controlled packaging materials: Refrigerants, phase change materials, vacuum insulation panels (VIPs), and other packaging materials should be tested for their cooling duration and pressure resistance. Green packaging and recycling: Lightweight and biodegradable packaging: Using environmentally friendly materials (such as bio-based plastics and paper packaging) reduces resource consumption. Packaging recycling systems: Reusable packaging containers (such as shared pallets and folding boxes) reduce single-use packaging waste. Reverse logistics management: Return and recycling logistics systems enable the recycling of packaging and waste.

[0074] Backup energy system: emergency power supply (such as backup batteries, generators) in the event of equipment power outages.

[0075] Transport timeliness: Long transport time may lead to failure of temperature control, and route planning and multimodal transport connection need to be optimized.

[0076] Control of loading and unloading: the time of exposure to the external environment during loading and unloading, and the standardization of operations (such as "unbroken cold chain").

[0077] Goods stacking method: Arrange them reasonably to ensure cold air circulation and avoid blocking sensors or vents.

[0078] As an optimization solution of the above embodiment, step S20 includes the following steps:

[0079] There are p alternative pharmaceutical cold chain logistics companies represented by H = {H1, H2, ..., H p} and q pharmaceutical cold chain logistics transportation quality evaluation index sets are expressed as C = {C1, C2, ..., C q}, evaluation index C j The weight is and satisfy

[0080] E={E1,E2,…,E L} is a group of evaluation experts participating in the evaluation, expert E l The weight is γ l , and satisfies γ l >0,

[0081] Experts' Fermat fuzzy evaluation matrix for pharmaceutical cold chain logistics companies under the evaluation indicators is:

[0082]

[0083] in, For Expert E l For pharmaceutical cold chain logistics companies i In the evaluation index C j Fermat fuzzy transport quality information under Experts' assessment of the degree of subordination of pharmaceutical cold chain logistics companies under the evaluation indicators. For Expert E l For pharmaceutical cold chain logistics companies i In the evaluation index C j The non-membership degree under [1] is [1]; p is the total number of pharmaceutical cold chain logistics companies, and q is the total number of Fermat fuzzy transportation quality information.

[0084] The importance of experts is evaluated through Fermat fuzzy linguistic variables to determine the weight of each expert:

[0085]

[0086] Among them, γ l is the expert weight of the lth expert, E l is the lth expert, L is the total number of experts, χ l is the membership degree of the lth expert, φ l is the non-membership degree of the lth expert.

[0087] As an optimization solution of the above embodiment, in step S30, the Fermat fuzzy group evaluation matrix is ​​determined as:

[0088]

[0089] where f ij =(X ij ,φ ij ) is the Fermat fuzzy group transport quality information, and the calculation formula is:

[0090]

[0091] The standardized Fermat fuzzy group evaluation matrix is:

[0092]

[0093] in, is the standardized Fermat fuzzy transport quality information, and the calculation formula is:

[0094]

[0095] As an optimization solution of the above embodiment, step S40 includes:

[0096] Determining the objective weights of evaluation indicators includes the following steps:

[0097] (1) The standardized Fermat fuzzy group evaluation matrix Converted into a score function matrix The calculation formula is:

[0098]

[0099] (2) According to the score function matrix, obtain the mean value of the jth evaluation index Calculation formula:

[0100] (3) Based on the mean, calculate the mean square error D of the jth evaluation index j ;

[0101]

[0102] (4) Calculate the coefficient of variation Z of the jth evaluation indicator based on the mean square error and the mean j ;

[0103]

[0104] (5) Normalize the coefficient of variation of each evaluation indicator to obtain the objective weight of each indicator

[0105] Determining the subjective weights of evaluation indicators includes the following steps:

[0106] (1) The subjective importance of the evaluation indicators is given by Fermat fuzzy language terms, and then the fusion value of the subjective importance of the evaluation indicators is obtained by using Fermat fuzzy Einstein interactive weighted average, and the score of the fusion value is obtained by using Fermat fuzzy score function;

[0107] (2) Obtain the ranking of the selected evaluation indicators by arranging the score values ​​in descending order;

[0108] (3) Calculate the comparative importance coefficient σ of the evaluation index j :

[0109]

[0110] where θ j Indicates the relative importance of the criteria;

[0111] (4) According to the comparative importance coefficient, the initial subjective weight of the evaluation index is obtained. j ;

[0112] Calculation formula:

[0113] (6) Based on the initial subjective weight, the final subjective weight of the evaluation index is obtained Calculation formula: The comprehensive weight of the evaluation indicators is determined as follows: Based on the determined objective weight of the indicators and the determined subjective weight of the indicators, the comprehensive weight of the evaluation indicators is determined. Formula calculation:

[0114] As an optimization solution of the above embodiment, step S40 includes the following steps:

[0115] S401, determining the Fermat fuzzy weighted sum evaluation information of each candidate enterprise according to the Fermat fuzzy Einstein interactive weighted average integration operator;

[0116] Determine the Fermat fuzzy weighted sum evaluation information of each candidate enterprise as follows:

[0117]

[0118] S402, determining Fermat fuzzy weighted product evaluation information of each candidate enterprise according to a Fermat fuzzy Einstein interactive weighted geometric integration operator;

[0119] The Fermat fuzzy weighted product evaluation information of each candidate enterprise is determined as:

[0120]

[0121] S403, determining a score value of the Fermat fuzzy weighted sum evaluation information of each candidate enterprise according to the Fermat fuzzy score function;

[0122] The score of the Fermat fuzzy weighted sum evaluation information for each candidate enterprise is determined as follows:

[0123]

[0124] S404, determining a score value of the Fermat fuzzy weighted product evaluation information of each candidate enterprise according to the Fermat fuzzy score function;

[0125] The score value of the Fermat fuzzy weighted product evaluation information of each candidate enterprise is determined as follows:

[0126]

[0127] S405, respectively calculating the arithmetic average strategy value, relative score strategy value and compromise balance strategy value of each candidate enterprise;

[0128] Determine the arithmetic mean strategic value for each candidate firm Relative scoring strategy value and trade-off balance strategy value They are:

[0129]

[0130] S406, determining the comprehensive evaluation value of the alternative solutions and obtaining the ranking of the alternative logistics companies, thereby determining the company with the best logistics and transportation quality.

[0131] The ranking of candidate companies is determined as follows:

[0132]

[0133] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for recommending pharmaceutical cold chain logistics enterprises based on Fermat fuzzy sets, characterized in that: Including steps: S10, obtaining a transportation quality data set of multiple candidate logistics companies under evaluation indicators; S20, construct an expert evaluation matrix for each logistics enterprise’s transportation quality information under the evaluation index through Fermat fuzzy linguistic variables; The importance of experts is evaluated through Fermat fuzzy linguistic variables to determine the weight of each expert; S30, a language evaluation matrix of each logistics enterprise under the evaluation index is given by Fermat fuzzy language variables and converted into Fermat fuzzy numbers; at the same time, a Fermat fuzzy group evaluation matrix is ​​obtained based on expert weights and Fermat fuzzy Einstein interactive integration operators; Determine the standardized Fermat fuzzy group evaluation matrix; S40, the objective weight and subjective weight of the evaluation index are obtained respectively by the Fermat fuzzy coefficient of variation method and the Fermat fuzzy stepwise weight evaluation ratio analysis method, and then the comprehensive index weight of the evaluation index is obtained according to the linear integration method; S50, based on the Fermat fuzzy set scoring function and Einstein integration operator, obtains the ranking of alternative logistics companies through an improved comprehensive compromise solution method, thereby determining the company with the best logistics transportation quality.

2. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 1, characterized in that: The step S20 includes the following steps: There are p alternative pharmaceutical cold chain logistics companies represented by H = {H1, H2, ..., H p } and q pharmaceutical cold chain logistics transportation quality evaluation index sets are expressed as C = {C1, C2, ..., C q }, evaluation index C j The weight of and satisfy E={E1,E2,…,E L } is a group of evaluation experts participating in the evaluation, expert E l The weight is γ l , and satisfies γ l >0, Experts' Fermat fuzzy evaluation matrix for pharmaceutical cold chain logistics companies under the evaluation indicators is: in, For Expert E l For pharmaceutical cold chain logistics companies i In the evaluation index C j Fermat fuzzy transport quality information under Experts' assessment of the degree of subordination of pharmaceutical cold chain logistics companies under the evaluation indicators. For Expert E l For pharmaceutical cold chain logistics companies i In the evaluation index C j The non-membership degree under [1] is [1]; p is the total number of pharmaceutical cold chain logistics companies, and q is the total number of Fermat fuzzy transportation quality information.

3. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 2, characterized in that: The importance of experts is evaluated through Fermat fuzzy linguistic variables to determine the weight of each expert: Among them, γ l is the expert weight of the lth expert, E l is the lth expert, L is the total number of experts, χ l is the membership degree of the lth expert, φ l is the non-membership degree of the lth expert.

4. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 3, characterized in that: In step S30, the Fermat fuzzy group evaluation matrix is ​​determined to be: where f ij =(χ ij ,φ ij ) is the Fermat fuzzy group evaluation information The standardized Fermat fuzzy group evaluation matrix is: in, is the standardized Fermat fuzzy evaluation information.

5. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 4, characterized in that: In the step S40, it includes: Determining the objective weights of evaluation indicators includes the following steps: (1) The standardized Fermat fuzzy group evaluation matrix Converted into a score function matrix (2) According to the score function matrix, obtain the mean value of the jth evaluation index (3) Based on the mean, calculate the mean square error D of the jth evaluation index j ; (4) Calculate the coefficient of variation Z of the jth evaluation indicator based on the mean square error and the mean j ; (5) Normalize the coefficient of variation of each evaluation indicator to obtain the objective weight of each indicator Determining the subjective weights of evaluation indicators includes the following steps: (1) The subjective importance of the evaluation indicators is given by Fermat fuzzy language terms, and then the fusion value of the subjective importance of the evaluation indicators is obtained by using Fermat fuzzy Einstein interactive weighted average, and the score of the fusion value is obtained by using Fermat fuzzy score function; (2) Obtain the ranking of the selected evaluation indicators by arranging the score values ​​in descending order; (3) Calculate the comparative importance coefficient σ of the evaluation index j : (4) According to the comparative importance coefficient, the initial subjective weight of the evaluation index is obtained. j ; (5) Based on the initial subjective weight, the final subjective weight of the evaluation index is obtained The comprehensive weight of the evaluation indicators is determined as follows: Based on the determined objective weight of the indicators and the determined subjective weight of the indicators, the comprehensive weight of the evaluation indicators is determined.

6. A pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to any one of claims 1 to 5, characterized in that: The step S40 includes the following steps: S401, determining the Fermat fuzzy weighted sum evaluation information of each candidate enterprise according to the Fermat fuzzy Einstein interactive weighted average integration operator; S402, determining Fermat fuzzy weighted product evaluation information of each candidate enterprise according to a Fermat fuzzy Einstein interactive weighted geometric integration operator; S403, determining a score value of the Fermat fuzzy weighted sum evaluation information of each candidate enterprise according to the Fermat fuzzy score function; S404, determining a score value of the Fermat fuzzy weighted product evaluation information of each candidate enterprise according to the Fermat fuzzy score function; S405, respectively calculating the arithmetic average strategy value, relative score strategy value and compromise balance strategy value of each candidate enterprise; S406, determining the comprehensive evaluation value of the alternative solutions and obtaining the ranking of the alternative logistics companies, thereby determining the company with the best logistics and transportation quality.

7. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 6, characterized in that: In step S40: Determine the Fermat fuzzy weighted sum evaluation information of each candidate enterprise as follows: The Fermat fuzzy weighted product evaluation information of each candidate enterprise is determined as:

8. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 7, characterized in that: In step S40: The score of the Fermat fuzzy weighted sum evaluation information for each candidate enterprise is determined as follows: The score value of the Fermat fuzzy weighted product evaluation information of each candidate enterprise is determined as follows:

9. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 8, characterized in that: In step S40, the arithmetic mean strategy value of each candidate enterprise is determined Relative scoring strategy value and trade-off balance strategy value They are:

10. The pharmaceutical cold chain logistics enterprise recommendation method based on Fermat fuzzy sets according to claim 9, characterized in that: The ranking of candidate companies is determined as follows:

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