Exoskeleton efficiency enhancement type heavy protective clothing multistage fuzzy comprehensive evaluation method

Abstract

The invention discloses a multistage fuzzy comprehensive evaluation method for exoskeleton efficiency enhanced heavy protective clothing, and the method comprises the steps: employing a Delphi consultation method to combine with the function and application scene of the exoskeleton efficiency enhanced heavy protective clothing, and determining a multilayer evaluation index system for evaluating the wearing comfort, the equipment efficiency and the practicality; setting a quantification rule for each evaluation index in the evaluation index system; constructing an indirect discrimination matrix by introducing an importance degree sorting index, performing consistency check to obtain a subjective weight of the evaluation index, obtaining an objective weight of the evaluation index by adopting a CRITIC method, and performing linear combination on the subjective weight and the objective weight according to a principle of solving an equilibrium solution by a game theory to obtain an optimal combination weight; and selecting a membership function according to the numerical characteristics of each evaluation index, determining a comment set, constructing a fuzzy discrimination matrix of the evaluation indexes to the comment set, and performing fuzzy operation by using the optimal combination weight and a fuzzy operator to obtain a fuzzy comprehensive evaluation result.

Description

Technical Field

[0001] This invention relates to the field of radiation protection equipment, specifically to a multi-level fuzzy comprehensive evaluation method for exoskeleton performance-enhancing heavy protective suits. Background Technology

[0002] Combining exoskeleton performance enhancement technology with traditional protective clothing can create exoskeleton-enhanced heavy protective suits, achieving a balance between the high protective performance of heavy protective suits and the high load on the human body. However, currently, there is a lack of scientific evaluation index systems and comprehensive evaluation models to standardize and effectively quantify the protective effect, comfort, and other performance characteristics of such equipment, making it impossible to objectively determine whether the equipment is practical and effective.

[0003] While various assessment methods have been applied in multiple fields, research on the assessment of the effectiveness of individual radiation protection equipment in the nuclear industry is almost nonexistent. Patent CN116797096A proposes a fuzzy comprehensive assessment method for supply chain resilience based on the AHP-entropy weighting method. This method combines subjective weighting using AHP with objective weighting using the entropy weighting method to assign weights to the enterprise supply chain resilience assessment indicators, and then uses fuzzy assessment to determine the overall assessment result. Patent CN116611716A discloses a fuzzy comprehensive assessment method for the technical support of complex equipment. This method assesses the technical support safety of complex equipment based on expert scoring and a pre-defined fuzzy assessment system and rules.

[0004] The existing technologies have the following shortcomings: the existing assessment technology system has not been applied in the nuclear industry, especially the technical research on the assessment of the effectiveness of individual radiation protection equipment, which is almost non-existent in China; existing assessment technologies have problems such as incomplete assessment systems, large influence of subjective factors in single weighting, and unreasonable weighting coefficients; the effectiveness assessment of protective equipment itself has the problem of unstructured assessment indicators and difficulty in quantification. Summary of the Invention

[0005] To achieve the above and other related objectives, this invention discloses a multi-level fuzzy comprehensive evaluation method for exoskeleton performance-enhancing heavy protective suits, comprising: Using the Delphi consultation method and combining the functions and application scenarios of exoskeleton-enhanced heavy protective clothing, a multi-level evaluation index system was determined to evaluate wearing comfort, equipment effectiveness, and practicality. Quantitative rules are set for each evaluation indicator in the evaluation indicator system; The subjective weights of the evaluation indicators are obtained by constructing an indirect discrimination matrix by introducing an importance ranking index and performing a consistency test. The objective weights of the evaluation indicators are obtained by using the CRITIC method based on the standard deviation and correlation of the sample data. The optimal combined weights are obtained by linearly combining the subjective and objective weights according to the principle of finding equilibrium solutions in game theory. Based on the numerical characteristics of each evaluation indicator, a membership function is selected to determine a set of comments including "excellent, good, average, poor, and terrible". A fuzzy discrimination matrix of the evaluation indicators on the set of comments is constructed. Fuzzy operations are performed using the optimal combination weights and fuzzy operators to obtain the fuzzy comprehensive evaluation results of the exoskeleton performance-enhancing heavy protective suit. Based on the fuzzy comprehensive evaluation results, targeted optimization and iteration were carried out on the shortcomings of the exoskeleton performance-enhanced heavy protective suit in terms of comfort, equipment effectiveness, and practicality evaluation indicators.

[0006] Preferably, the multi-level evaluation index system for assessing wearing comfort, equipment effectiveness, and practicality, determined by employing the Delphi consultation method in conjunction with the functions and application scenarios of the exoskeleton-enhanced heavy protective suit, includes: Based on the disciplines of ergonomics and wearable equipment design, this study takes a two-pronged approach, considering both subjective and objective evaluation. Following the principles of feasibility, conditionality, effectiveness, and consistency in selecting evaluation indicators, the study screens candidate indicators affecting the effectiveness of individual radiation protection equipment by distributing questionnaires to a designated population and conducting multiple rounds of feedback. The evaluation indicators then include: subjective comfort, waist tightness / pressure, movement tracking, lower limb fatigue, shoulder weight reduction effect, energy consumption comparison, protective performance, ease of donning and doffing, maximum load, failure rate, and adaptability.

[0007] Preferably, the quantitative rules for setting each evaluation indicator in the evaluation indicator system include: The subjective comfort is expressed as a subjective feeling rating given by personnel who have worked wearing the work exoskeleton protective suit, based on a subjective evaluation scale. The waist tightness pressure is expressed as the static pressure value when the belt pressure sensor is placed at the position of the waist strap and the exoskeleton protective suit is worn; in order to ensure the accuracy of the measurement results, multiple measurements are taken and the average value is taken; ; In the formula, This is a measurement of lumbar pressure. Measure the waist area multiple times; The average pressure on the waist; The motion following capability is represented by the worker wearing an exoskeleton protective suit, while simultaneously detecting the lower limb posture signals of the exoskeleton and the human body; ; In the formula, This represents the human body's posture signal; The better the motion tracking of both, the better the positional signal of the exoskeleton. The closer to 1; The degree of lower limb fatigue is expressed by comparing the root mean square amplitude of the surface electromyography (EMG) signal when the human body is completely relaxed with the reference value, using the same conditions of wearing an exoskeleton for weight-bearing and not wearing an exoskeleton for weight-bearing, and comparing the degree of muscle fatigue in the two cases. ; ; In the formula, , The levels of muscle fatigue were measured when wearing an exoskeleton versus not wearing one. These are sEMG sampling values ​​when wearing an exoskeleton under load; These are sEMG sample values ​​when the user is not wearing an exoskeleton and is under load. These are sEMG sample values ​​during relaxation; The shoulder weight reduction effect is expressed as the average value of reference pressure data measured at three points: the top of the shoulder, the front of the shoulder, and the back of the shoulder, respectively, as the final data result. ; ; ; In the formula, This is a pressure measurement value; The number of measurements taken at a specific point on the shoulder; This refers to the pressure value at a specific point on the shoulder. Average pressure on the shoulder; F s2 Shoulder load pressure when wearing an exoskeleton; F s1 To reduce the workload and stress on staff when not wearing exoskeletons; For shoulder weight reduction; The energy consumption comparison is represented by a comparison of heart rate values ​​under three conditions: no exoskeleton and no load, no exoskeleton and load, and wearing an exoskeleton and load. The protective performance is expressed as the neutron shielding rate; The term "portable donning and doffing" refers to the time T required for a subject to put on and take off protective clothing under normal temperature experimental conditions. The maximum load is expressed as the maximum load F that the exoskeleton can withstand; The failure rate is expressed as the ratio of the number of parts that fail to the total number of parts within a certain period of time. ; ; In the formula, This represents the total number of samples that failed after testing. The number of samples before testing; For testing time; The adaptability refers to the degree to which the exoskeleton protective suit adapts to people of different body types.

[0008] Preferably, the subjective weights of the evaluation indicators obtained by constructing an indirect discrimination matrix through the introduction of an importance ranking index and performing a consistency test include: Based on the 1-9 scale and its inverse scaling method, an original discriminant matrix is ​​constructed to compare the importance of each pair of evaluation indicators; The importance ranking index of each evaluation indicator is calculated based on the ranking results of each evaluation indicator in the original discriminant matrix. The relative importance of the benchmark evaluation indicator is represented by the ratio of the maximum ranking index to the minimum ranking index. An indirect discriminant matrix is ​​constructed using this relative importance, so that the difference in the ranking index of each evaluation indicator is mapped from the original interval to an interval that better reflects the relative importance. The maximum eigenvalue and its corresponding eigenvector are obtained from the indirect discrimination matrix, and the eigenvectors are normalized to obtain the subjective weights of each evaluation index. Construct a consistency ratio (CR). If the CR is less than 0.1, the indirect discrimination matrix is ​​considered to have passed the consistency test; otherwise, the expert scoring results are readjusted.

[0009] Preferably, the objective weights of the evaluation indicators obtained using the CRITIC method based on the standard deviation and correlation of the sample data include: For multiple samples to be evaluated, an original data matrix is ​​constructed based on the evaluation index, where the matrix elements are the scores of a certain sample under a certain evaluation index. The original data matrix is ​​dimensionless according to both positive and negative evaluation metrics to obtain a dimensionless data matrix. The degree of variation and fluctuation in the values ​​of each evaluation indicator is characterized by calculating the standard deviation of each evaluation indicator across all samples. The correlation coefficient between each evaluation indicator is calculated to characterize the correlation and conflict between different evaluation indicators, and the conflict between each evaluation indicator and other evaluation indicators is quantified. The information content of each evaluation indicator is calculated based on the standard deviation and conflict quantity. The greater the information content, the higher the objective importance. The information content of each evaluation indicator is then normalized to obtain the objective weight.

[0010] Preferably, the optimal combined weights are obtained by linearly combining subjective and objective weights based on the principle of finding equilibrium solutions in game theory, including: The subjective weight vector and the objective weight vector are combined linearly to construct the evaluation index combination weight vector, where the sum of the linear combination coefficients is 1. Using the sum of the deviations between the combined weight vector and the subjective and objective weight vectors as the objective function, an optimization model based on the principle of finding equilibrium solutions in game theory is established. The optimal linear combination coefficients are obtained by solving the first derivative condition that minimizes the objective function. The optimal combination coefficients are obtained by normalizing the optimal linear combination coefficients, and the optimal combination weights of each evaluation index are calculated from these coefficients.

[0011] Preferably, the step of selecting a membership function based on the numerical characteristics of each evaluation index, determining a set of comments including "excellent, good, average, poor, and terrible", constructing a fuzzy discrimination matrix of the evaluation index on the set of comments, and performing fuzzy operations using the optimal combination weights and fuzzy operators to obtain the fuzzy comprehensive evaluation result of the exoskeleton performance-enhancing heavy protective suit includes: The evaluation indicators are constructed as a set of indicator factors; The evaluation level set reflecting the performance of the exoskeleton-enhanced heavy protective suit is set as "excellent, good, average, poor, and terrible", and the membership function is selected according to the dimension and numerical distribution of each evaluation index. A fuzzy discrimination matrix is ​​constructed based on the membership values ​​of each evaluation index under each rating level. Fuzzy operators are used to perform fuzzy operations on the fuzzy discrimination matrix and the optimal combination weights to obtain the discrimination matrix. The discrimination matrix is ​​normalized to obtain the fuzzy evaluation set, and thus the fuzzy comprehensive evaluation result is obtained.

[0012] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0013] By adopting the above technical solutions, an improved AHP-CRITIC subjective and objective comprehensive weighting method is proposed. This method eliminates the problems of large subjective factors and unreasonable weight coefficients in the traditional single weighting method, making the weighting results of the decision more scientific. By introducing the fuzzy comprehensive evaluation method, the problem of unstructured and difficult-to-quantify efficacy indicators of exoskeleton protective clothing is solved, improving the reliability and accuracy of the evaluation results. This enables the quantitative evaluation of the efficacy and performance of individual radiation protection equipment, filling the gap in related fields. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 This is a flowchart of an embodiment of the present invention; Figure 2A flowchart for determining evaluation indicators in embodiments of the present invention; Figure 3 An evaluation index system constructed for embodiments of the present invention; Figure 4 This is a graph of the membership function in an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Reference Figure 1 This invention provides a multi-level fuzzy comprehensive evaluation method for exoskeleton performance-enhancing heavy protective suits, including: Reference Figure 2 Using the Delphi consultation method, reasonable evaluation indicators were selected by assessing the functions and application scenarios of the exoskeleton performance-enhancing heavy protective suit. Experts studied and discussed these indicators, and after multiple rounds of information feedback and screening, a set of evaluation indicators was obtained when opinions tended to be unified, and an evaluation indicator system was established. Quantitative rules are set for each evaluation indicator in the evaluation indicator system; The subjective weights of the evaluation indicators are obtained by constructing an indirect discrimination matrix by introducing an importance ranking index and performing a consistency test. The objective weights of the evaluation indicators are obtained by using the CRITIC method based on the standard deviation and correlation of the sample data. The optimal combined weights are obtained by linearly combining the subjective and objective weights according to the principle of finding equilibrium solutions in game theory. Based on the numerical characteristics of each evaluation indicator, a membership function is selected to determine a set of comments including "excellent, good, average, poor, and terrible". A fuzzy discrimination matrix of the evaluation indicators on the set of comments is constructed. Fuzzy operations are performed using the optimal combination weights and fuzzy operators to obtain the fuzzy comprehensive evaluation results of the exoskeleton performance-enhancing heavy protective suit. Based on the fuzzy comprehensive evaluation results, targeted optimization and iteration were carried out on the shortcomings of the exoskeleton performance-enhanced heavy protective suit in terms of comfort, equipment effectiveness, and practicality evaluation indicators.

[0017] Preferably, the multi-level evaluation index system for assessing wearing comfort, equipment effectiveness, and practicality, determined by employing the Delphi consultation method in conjunction with the functions and application scenarios of the exoskeleton-enhanced heavy protective suit, includes: Reference Figure 3Based on ergonomics and wearable equipment design, this invention takes a two-pronged approach, considering both subjective and objective evaluation. Following the principles of feasibility, conditionality, effectiveness, and consistency in selecting evaluation indicators, it screens candidate evaluation indicators affecting the effectiveness of individual radiation protection equipment by distributing questionnaires to a designated population and conducting multiple rounds of feedback. The evaluation indicators include: subjective comfort, waist tightness / pressure, movement tracking, lower limb fatigue, shoulder weight reduction effect, energy consumption comparison, protective performance, ease of donning and doffing, maximum load, failure rate, and adaptability.

[0018] Preferably, the quantitative rules for setting each evaluation indicator in the evaluation indicator system include: The subjective comfort is expressed as a subjective feeling rating given by personnel who have worked wearing the work exoskeleton protective suit, based on a subjective evaluation scale. The waist tightness pressure is expressed as the static pressure value when the belt pressure sensor is placed at the position of the waist strap and the exoskeleton protective suit is worn; in order to ensure the accuracy of the measurement results, multiple measurements are taken and the average value is taken; ; In the formula, This is a measurement of lumbar pressure. Measure the waist area multiple times; The average pressure on the waist; The motion following capability is represented by the worker wearing an exoskeleton protective suit, while simultaneously detecting the lower limb posture signals of the exoskeleton and the human body; ; In the formula, This represents the human body's posture signal; The better the motion tracking of both, the better the positional signal of the exoskeleton. The closer to 1; The degree of lower limb fatigue is expressed by comparing the root mean square amplitude of the surface electromyography (EMG) signal when the human body is completely relaxed with the reference value, using the same conditions of wearing an exoskeleton for weight-bearing and not wearing an exoskeleton for weight-bearing, and comparing the degree of muscle fatigue in the two cases. ; ; In the formula, , The levels of muscle fatigue were measured when wearing an exoskeleton versus not wearing one. These are sEMG sampling values ​​when wearing an exoskeleton under load; These are sEMG sample values ​​when the user is not wearing an exoskeleton and is under load. These are sEMG sample values ​​during relaxation; The shoulder weight reduction effect is expressed as the average value of reference pressure data measured at three points: the top of the shoulder, the front of the shoulder, and the back of the shoulder, respectively, as the final data result. ; ; ; In the formula, This is a pressure measurement value; The number of measurements taken at a specific point on the shoulder; This refers to the pressure value at a specific point on the shoulder. Average pressure on the shoulder; F s2 Shoulder load pressure when wearing an exoskeleton; F s1 To reduce the workload and stress on staff when not wearing exoskeletons; For shoulder weight reduction; The energy consumption comparison is represented by a comparison of heart rate values ​​under three conditions: no exoskeleton and no load, no exoskeleton and load, and wearing an exoskeleton and load. The protective performance is expressed as the neutron shielding rate; The term "portable donning and doffing" refers to the time T required for a subject to put on and take off protective clothing under normal temperature experimental conditions. The maximum load is expressed as the maximum load F that the exoskeleton can withstand; The failure rate is expressed as the ratio of the number of parts that fail to the total number of parts within a certain period of time. ; ; In the formula, This represents the total number of samples that failed after testing. The number of samples before testing; For testing time; The adaptability refers to the degree to which the exoskeleton protective suit adapts to people of different body types.

[0019] Preferably, the subjective weights of the evaluation indicators obtained by constructing an indirect discrimination matrix through the introduction of an importance ranking index and performing a consistency test include: Based on the 1-9 scale and its inverse scaling method, an original discriminant matrix is ​​constructed to compare the importance of each pair of evaluation indicators; The importance ranking index of each evaluation indicator is calculated based on the ranking results of each evaluation indicator in the original discriminant matrix. The relative importance of the benchmark evaluation indicator is represented by the ratio of the maximum ranking index to the minimum ranking index. An indirect discriminant matrix is ​​constructed using this relative importance, so that the difference in the ranking index of each evaluation indicator is mapped from the original interval to an interval that better reflects the relative importance. The maximum eigenvalue and its corresponding eigenvector are obtained from the indirect discrimination matrix, and the eigenvectors are normalized to obtain the subjective weights of each evaluation index. Construct a consistency ratio (CR). If the CR is less than 0.1, the indirect discrimination matrix is ​​considered to have passed the consistency test; otherwise, the expert scoring results are readjusted.

[0020] As described above, in a preferred embodiment of the present invention, obtaining the subjective weight includes the following steps: A1: Distribute questionnaires to experts and users in the field, and score the importance of each indicator using a 1-9 scale and its reciprocal. Compare the indicators using a pairwise comparison method. and The importance of constructing the discriminant matrix ; ; Based on the discriminant matrix, the importance of each indicator is ranked by index. Represented as: ; Select the largest sorting index With minimum sorting index The relative importance of the ratio as a baseline indicator ,Right now: ; The relative importance of each indicator is represented, thus obtaining the indirect discrimination matrix. and its elements for: , ; A2: Based on the indirect discriminant matrix The largest eigenvalue The corresponding eigenvector Solve for the weight vector of each index, where the largest eigenvalue is... satisfy ; In the formula, is the matrix order, and also the total number of evaluation metrics in this embodiment.

[0021] The weight vector of each indicator is the eigenvector. elements Since the sum of the weight coefficients of each indicator must be 1, the feature vector needs to be normalized to obtain the normalized feature vector. Wherein, the weight vector satisfies: ; A3: Establish a consistency evaluation model (CI) to test the consistency of the indicators. ; Introducing the random consistency index RI to measure the magnitude of CI: ; A test coefficient CR is introduced to determine whether the current discriminant matrix passes the consistency test. If CR < 0.1, it is considered to have passed. The formula for determining the value of CR is as follows: .

[0022] Preferably, the objective weights of the evaluation indicators obtained using the CRITIC method based on the standard deviation and correlation of the sample data include: For multiple samples to be evaluated, an original data matrix is ​​constructed based on the evaluation index, where the matrix elements are the scores of a certain sample under a certain evaluation index. The original data matrix is ​​dimensionless according to both positive and negative evaluation metrics to obtain a dimensionless data matrix. The degree of variation and fluctuation in the values ​​of each evaluation indicator is characterized by calculating the standard deviation of each evaluation indicator across all samples. The correlation coefficient between each evaluation indicator is calculated to characterize the correlation and conflict between different evaluation indicators, and the conflict between each evaluation indicator and other evaluation indicators is quantified. The information content of each evaluation indicator is calculated based on the standard deviation and conflict quantity. The greater the information content, the higher the objective importance. The information content of each evaluation indicator is then normalized to obtain the objective weight.

[0023] Preferably, in a preferred embodiment of the present invention, obtaining the objective weight includes: B1: For m samples to be evaluated, select the above n evaluation indicators for evaluation, and construct the original data matrix as follows: ; In the formula, Representing the The first evaluation sample The scores of each evaluation indicator.

[0024] B2: Dimensionless processing of all data: The positive evaluation indicators are handled as follows: ; The reverse evaluation index is handled as follows: ; In the formula, It is the first The first evaluation sample The dimensionless values ​​of each evaluation indicator; It is the first The maximum value of each evaluation indicator in all evaluation samples; It is the first The minimum value of each evaluation indicator among all evaluation samples.

[0025] B3: Standard deviation is used to represent the fluctuations in the values ​​of each indicator, expressed as: ; In the formula, Indicates the first The average of the indicators; Indicates the first The standard deviation of each indicator.

[0026] B4: The conflict between different indicators is represented by the correlation coefficient: ; In the formula, Indicates the first The first indicator and the first The correlation coefficient of each indicator.

[0027] No. The result of quantifying the conflict between an indicator and other indicators for: ; B5: Obtained from step B4. The amount of information contained in each indicator : ; In the formula, The larger the value, the higher the number of... The greater the role of an evaluation indicator in the overall evaluation system, the greater its weight will be.

[0028] B6: In summary, the objective weights of the indicator set can be obtained. , of which Objective weight values ​​of each indicator for: .

[0029] Preferably, the optimal combined weights are obtained by linearly combining subjective and objective weights based on the principle of finding equilibrium solutions in game theory, including: The subjective weight vector and the objective weight vector are combined linearly to construct the evaluation index combination weight vector, where the sum of the linear combination coefficients is 1. Using the sum of the deviations between the combined weight vector and the subjective and objective weight vectors as the objective function, an optimization model based on the principle of finding equilibrium solutions in game theory is established. The optimal linear combination coefficients are obtained by solving the first derivative condition that minimizes the objective function. The optimal combination coefficients are obtained by normalizing the optimal linear combination coefficients, and the optimal combination weights of each evaluation index are calculated from these coefficients.

[0030] Preferably, in a preferred embodiment of the present invention, obtaining the optimal combination weights specifically includes: Subjective weight and objective weight The weights of the index combination are obtained through linear combination. for: In the formula, , These are the coefficients of the linear combination.

[0031] Based on the principle of finding equilibrium solutions in game theory, an objective function is established to make the combined weights and and To find the optimal linear combination coefficients, the sum of the deviations must be minimized. , Thus, the optimal combination weights are obtained. The objective function and constraints are as follows: ; According to the principle of differentiation, when the above objective function reaches its minimum value, it satisfies the condition for the first derivative: ; Combining the above two equations, we obtain the linear combination coefficients. , The optimal combination coefficients are obtained by normalization. , for: ; Final optimal combination weights for: .

[0032] Preferably, the step of selecting a membership function based on the numerical characteristics of each evaluation index, determining a set of comments including "excellent, good, average, poor, and terrible", constructing a fuzzy discrimination matrix of the evaluation index on the set of comments, and performing fuzzy operations using the optimal combination weights and fuzzy operators to obtain the fuzzy comprehensive evaluation result of the exoskeleton performance-enhancing heavy protective suit includes: The evaluation indicators are constructed as a set of indicator factors; The evaluation level set reflecting the performance of the exoskeleton-enhanced heavy protective suit is set as "excellent, good, average, poor, and terrible", and the membership function is selected according to the dimension and numerical distribution of each evaluation index. A fuzzy discrimination matrix is ​​constructed based on the membership values ​​of each evaluation index under each rating level. Fuzzy operators are used to perform fuzzy operations on the fuzzy discrimination matrix and the optimal combination weights to obtain the discrimination matrix. The discrimination matrix is ​​normalized to obtain the fuzzy evaluation set, and thus the fuzzy comprehensive evaluation result is obtained.

[0033] Preferably, the above method specifically includes the following in the embodiments of the present invention: The selected indicators constitute the indicator factor set for evaluating the effectiveness of exoskeleton protective clothing. : ; Five levels—Excellent, Good, Average, Poor, and Terrible—were defined as the evaluation criteria for this exoskeleton protective suit. : ; Determine the integrated weight value of each indicator. The optimal weight combination is adopted: ; Reference Figure 4 Construct membership functions based on different comment sets. , , , , ; ; ; ; ; Construct a fuzzy discriminant matrix using the membership values ​​of each indicator in the comment set. : .

[0034] In the formula, Indicates the first The indicator factors in the first The membership degree value under each comment element is obtained by substituting the values ​​of the indicator factors into the corresponding membership function.

[0035] S4.5: Using fuzzy operators to analyze the fuzzy discrimination matrix with optimal combination weights Perform fuzzy calculations to obtain the index set. For the collection of comments Evaluation vector : Will The discriminant matrix, which forms the basis of the decision set to the target set, can be normalized to obtain the fuzzy evaluation set. This allows for multi-level fuzzy comprehensive evaluation of the system.

[0036] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0037] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0038] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0039] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-level fuzzy comprehensive evaluation method for exoskeleton performance-enhancing heavy protective suits, characterized in that, include: Using the Delphi consultation method and combining the functions and application scenarios of exoskeleton-enhanced heavy protective clothing, a multi-level evaluation index system was determined to evaluate wearing comfort, equipment effectiveness, and practicality. Quantitative rules are set for each evaluation indicator in the evaluation indicator system; The subjective weights of the evaluation indicators are obtained by constructing an indirect discrimination matrix by introducing an importance ranking index and conducting a consistency test. The objective weights of the evaluation indicators are obtained by using the CRITIC method based on the standard deviation and correlation of the sample data. The optimal combined weights are obtained by linearly combining the subjective and objective weights according to the principle of finding equilibrium solutions in game theory. Based on the numerical characteristics of each evaluation indicator, a membership function is selected to determine a set of comments including "excellent, good, average, poor, and terrible". A fuzzy discrimination matrix of the evaluation indicators on the set of comments is constructed. Fuzzy operations are performed using the optimal combination weights and fuzzy operators to obtain the fuzzy comprehensive evaluation results of the exoskeleton performance-enhancing heavy protective suit. Based on the fuzzy comprehensive evaluation results, targeted optimization and iteration were carried out on the shortcomings of the exoskeleton performance-enhanced heavy protective suit in terms of comfort, equipment effectiveness, and practicality evaluation indicators.

2. The method according to claim 1, characterized in that, The Delphi consultation method, combined with the functions and application scenarios of exoskeleton-enhanced heavy protective clothing, was used to determine a multi-level evaluation index system for assessing wearing comfort, equipment effectiveness, and practicality. This system includes: Based on the disciplines of ergonomics and wearable equipment design, this study takes a two-pronged approach, considering both subjective and objective evaluation. Following the principles of feasibility, conditionality, effectiveness, and consistency in selecting evaluation indicators, the study screens candidate indicators affecting the effectiveness of individual radiation protection equipment by distributing questionnaires to a designated population and conducting multiple rounds of feedback. The evaluation indicators then include: subjective comfort, waist tightness / pressure, movement tracking, lower limb fatigue, shoulder weight reduction effect, energy consumption comparison, protective performance, ease of donning and doffing, maximum load, failure rate, and adaptability.

3. The method according to claim 2, characterized in that, The quantitative rules for setting each evaluation indicator in the evaluation indicator system include: The subjective comfort is expressed as a subjective feeling rating given by personnel who have worked wearing the work exoskeleton protective suit, based on a subjective evaluation scale. The waist tightness pressure is expressed as the static pressure value when the belt pressure sensor is placed at the position of the waist strap and the exoskeleton protective suit is worn; in order to ensure the accuracy of the measurement results, multiple measurements are taken and the average value is taken; ； In the formula, This is a measurement of lumbar pressure. Measure the waist area multiple times; The average pressure on the waist; The motion following capability is represented by the worker wearing an exoskeleton protective suit, while simultaneously detecting the lower limb posture signals of the exoskeleton and the human body; ； In the formula, This represents the human body's posture signal; The better the motion tracking of both, the better the positional signal of the exoskeleton. The closer to 1; The degree of lower limb fatigue is expressed by comparing the root mean square amplitude of the surface electromyography (EMG) signal when the human body is completely relaxed with the reference value, using the same conditions of wearing an exoskeleton for weight-bearing and not wearing an exoskeleton for weight-bearing, and comparing the degree of muscle fatigue in the two cases. ； ； In the formula, , The levels of muscle fatigue were measured when wearing an exoskeleton versus not wearing one. These are sEMG sampling values ​​when wearing an exoskeleton under load; These are sEMG sample values ​​when the user is not wearing an exoskeleton and is under load. These are sEMG sample values ​​during relaxation; The shoulder weight reduction effect is expressed as the average value of reference pressure data measured at three points: the top of the shoulder, the front of the shoulder, and the back of the shoulder, respectively, as the final data result. ； ； ； In the formula, This is a pressure measurement value; The number of measurements taken at a specific point on the shoulder; This refers to the pressure value at a specific point on the shoulder. Average pressure on the shoulder; F s2 Shoulder load pressure when wearing an exoskeleton; F s1 To reduce the workload and stress on staff when not wearing exoskeletons; For shoulder weight reduction; The energy consumption comparison is represented by a comparison of heart rate values ​​under three conditions: no exoskeleton and no load, no exoskeleton and load, and wearing an exoskeleton and load. The protective performance is expressed as the neutron shielding rate; The term "portable donning and doffing" refers to the time T required for a subject to put on and take off protective clothing under normal temperature experimental conditions. The maximum load is expressed as the maximum load F that the exoskeleton can withstand; The failure rate is expressed as the ratio of the number of parts that fail to the total number of parts within a certain period of time. ; ； In the formula, This represents the total number of samples that failed after testing. The number of samples before testing; For testing time; The adaptability refers to the degree to which the exoskeleton protective suit adapts to people of different body types.

4. The method according to claim 1, characterized in that, The subjective weights of the evaluation indicators obtained by constructing an indirect discriminant matrix through the introduction of an importance ranking index and performing a consistency test include: Based on the 1-9 scale and its inverse scaling method, an original discriminant matrix is ​​constructed to compare the importance of each pair of evaluation indicators; The importance ranking index of each evaluation indicator is calculated based on the ranking results of each evaluation indicator in the original discriminant matrix. The relative importance of the benchmark evaluation indicator is represented by the ratio of the maximum ranking index to the minimum ranking index. An indirect discriminant matrix is ​​constructed using this relative importance, so that the difference in the ranking index of each evaluation indicator is mapped from the original interval to an interval that better reflects the relative importance. The maximum eigenvalue and its corresponding eigenvector are obtained from the indirect discrimination matrix, and the eigenvectors are normalized to obtain the subjective weights of each evaluation index. Construct a consistency ratio (CR). If the CR is less than 0.1, the indirect discrimination matrix is ​​considered to have passed the consistency test; otherwise, the expert scoring results are readjusted.

5. The method according to claim 1, characterized in that, The objective weights of the evaluation indicators obtained using the CRITIC method based on the standard deviation and correlation of sample data include: For multiple samples to be evaluated, an original data matrix is ​​constructed based on the evaluation index, where the matrix elements are the scores of a certain sample under a certain evaluation index. The original data matrix is ​​dimensionless according to the positive evaluation index and the negative evaluation index respectively, to obtain the dimensionless data matrix; The degree of variation and fluctuation in the values ​​of each evaluation indicator is characterized by calculating the standard deviation of each evaluation indicator across all samples. The correlation coefficient between each evaluation indicator is calculated to characterize the correlation and conflict between different evaluation indicators, and the conflict between each evaluation indicator and other evaluation indicators is quantified. The information content of each evaluation indicator is calculated based on the standard deviation and conflict quantity. The greater the information content, the higher the objective importance. The information content of each evaluation indicator is then normalized to obtain the objective weight.

6. The method according to claim 1, characterized in that, Based on the principle of finding equilibrium solutions in game theory, the optimal combination of subjective and objective weights is obtained by linearly combining them, including: The subjective weight vector and the objective weight vector are combined linearly to construct the evaluation index combination weight vector, where the sum of the linear combination coefficients is 1. Using the sum of the deviations between the combined weight vector and the subjective and objective weight vectors as the objective function, an optimization model based on the principle of finding equilibrium solutions in game theory is established. The optimal linear combination coefficients are obtained by solving the first derivative condition that minimizes the objective function. The optimal combination coefficients are obtained by normalizing the optimal linear combination coefficients, and the optimal combination weights of each evaluation index are calculated from these coefficients.

7. The method according to claim 1, characterized in that, The process involves selecting a membership function based on the numerical characteristics of each evaluation indicator, determining a set of comments including "excellent, good, average, poor, and terrible," constructing a fuzzy discrimination matrix of the evaluation indicators on the comment set, and performing fuzzy operations using the optimal combination weights and fuzzy operators to obtain the fuzzy comprehensive evaluation results of the exoskeleton performance-enhancing heavy protective suit, including: The evaluation indicators are constructed as a set of indicator factors; The evaluation level set reflecting the performance of the exoskeleton-enhanced heavy protective suit is set as "excellent, good, average, poor, and terrible", and the membership function is selected according to the dimension and numerical distribution of each evaluation index. A fuzzy discrimination matrix is ​​constructed based on the membership values ​​of each evaluation index under each rating level. Fuzzy operators are used to perform fuzzy operations on the fuzzy discrimination matrix and the optimal combination weights to obtain the discrimination matrix. The discrimination matrix is ​​normalized to obtain the fuzzy evaluation set, and thus the fuzzy comprehensive evaluation result is obtained.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1-7.

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