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MATLAB基于改进云物元的模拟机协同训练质量评价

news 2025/11/5 17:52:34

1. 系统总体架构

技术路线框架：

数据采集 → 指标构建 → 云物元模型 → 权重确定 → 质量评价 → 结果分析

2. 核心算法步骤

步骤1：评价指标体系构建

%% 模拟机协同训练质量评价指标体系
% 一级指标
indicators = {'操作技能', '团队协作', '情景逼真度', '训练效果', '系统性能'
};% 二级指标细化
sub_indicators = {{'操作准确性', '反应速度', '程序规范性'},           % 操作技能{'沟通协调', '角色配合', '冲突处理'},               % 团队协作  {'环境真实感', '设备仿真度', '情景复杂度'},          % 情景逼真度{'知识掌握度', '技能提升度', '问题解决能力'},        % 训练效果{'系统稳定性', '响应时效', '数据准确性'}            % 系统性能
};

步骤2：改进云物元模型构建

classdef ImprovedCloudMatterElementpropertiesName                    % 评价指标名称ClassicalDomain         % 经典域 [下限, 上限]NodeDomain             % 节域 [下限, 上限]  ActualValue            % 实测值CloudParameters        % 云参数 [Ex, En, He]MembershipDegree       % 隶属度endmethodsfunction obj = ImprovedCloudMatterElement(name, classical, node)obj.Name = name;obj.ClassicalDomain = classical;obj.NodeDomain = node;% 计算云参数obj.CloudParameters = obj.calculateCloudParameters();endfunction params = calculateCloudParameters(obj)% 基于经典域和节域计算云数字特征Ex = mean(obj.ClassicalDomain);En = (obj.ClassicalDomain(2) - obj.ClassicalDomain(1)) / 6;He = (obj.NodeDomain(2) - obj.NodeDomain(1)) / 36;params = [Ex, En, He];endfunction degree = calculateMembership(obj, actualValue)% 改进的云隶属度计算Ex = obj.CloudParameters(1);En = obj.CloudParameters(2); He = obj.CloudParameters(3);% 生成正态随机数En_prime = normrnd(En, He);% 计算隶属度if En_prime ~= 0degree = exp(-(actualValue - Ex)^2 / (2 * En_prime^2));elsedegree = 0;endobj.ActualValue = actualValue;obj.MembershipDegree = degree;endend
end

步骤3：组合权重确定

function weights = combinedWeighting(data_matrix)% 主观权重 - AHP法subjective_weights = analyticHierarchyProcess();% 客观权重 - 熵权法  objective_weights = entropyWeightMethod(data_matrix);% 组合权重alpha = 0.6;  % 主观权重系数beta = 0.4;   % 客观权重系数weights = alpha * subjective_weights + beta * objective_weights;weights = weights / sum(weights);  % 归一化
endfunction weights = analyticHierarchyProcess()% 构建判断矩阵（示例）judgment_matrix = [1,   3,   2,   1,   2;1/3, 1,   1/2, 1/3, 1/2;1/2, 2,   1,   1/2, 1;1,   3,   2,   1,   2;1/2, 2,   1,   1/2, 1];% 特征向量法求权重[V, D] = eig(judgment_matrix);[~, idx] = max(diag(D));weights = V(:, idx) / sum(V(:, idx));
endfunction weights = entropyWeightMethod(data_matrix)[m, n] = size(data_matrix);% 数据标准化normalized_data = zeros(m, n);for j = 1:nif max(data_matrix(:,j)) ~= min(data_matrix(:,j))normalized_data(:,j) = (data_matrix(:,j) - min(data_matrix(:,j))) / ...(max(data_matrix(:,j)) - min(data_matrix(:,j)));elsenormalized_data(:,j) = 0.5;endend% 计算熵值p = normalized_data ./ sum(normalized_data, 1);p(p == 0) = 1e-10;  % 避免log(0)e = -sum(p .* log(p), 1) / log(m);% 计算权重weights = (1 - e) / sum(1 - e);
end

3. 完整评价模型实现

%% 模拟机协同训练质量评价主程序
classdef SimulatorTrainingQualityEvaluationpropertiesEvaluationIndicators     % 评价指标体系CloudMatterElements      % 云物元集合Weights                 % 组合权重EvaluationResults       % 评价结果TrainingData           % 训练数据endmethodsfunction obj = SimulatorTrainingQualityEvaluation()obj = obj.initializeEvaluationSystem();endfunction obj = initializeEvaluationSystem(obj)% 初始化评价指标体系obj.EvaluationIndicators = {'操作准确性',   [0.8, 1.0], [0.6, 1.0];'反应速度',     [0.7, 0.9], [0.5, 1.0]; '程序规范性',   [0.9, 1.0], [0.7, 1.0];'沟通协调',     [0.8, 1.0], [0.6, 1.0];'角色配合',     [0.8, 1.0], [0.6, 1.0];'环境真实感',   [0.7, 0.9], [0.5, 1.0];'系统稳定性',   [0.9, 1.0], [0.7, 1.0];'技能提升度',   [0.8, 1.0], [0.6, 1.0]};% 创建云物元对象obj.CloudMatterElements = cell(size(obj.EvaluationIndicators, 1), 1);for i = 1:length(obj.CloudMatterElements)name = obj.EvaluationIndicators{i, 1};classical = obj.EvaluationIndicators{i, 2};node = obj.EvaluationIndicators{i, 3};obj.CloudMatterElements{i} = ImprovedCloudMatterElement(name, classical, node);endendfunction results = evaluateTrainingQuality(obj, training_data)% 训练质量综合评价[num_samples, num_indicators] = size(training_data);% 计算权重obj.Weights = combinedWeighting(training_data);% 计算各样本的综合评价结果comprehensive_scores = zeros(num_samples, 1);membership_matrix = zeros(num_samples, num_indicators);quality_levels = cell(num_samples, 1);for i = 1:num_samplessample_scores = zeros(num_indicators, 1);for j = 1:num_indicators% 计算各指标隶属度membership = obj.CloudMatterElements{j}.calculateMembership(training_data(i, j));membership_matrix(i, j) = membership;sample_scores(j) = membership * obj.Weights(j);end% 综合得分comprehensive_scores(i) = sum(sample_scores);% 质量等级判定quality_levels{i} = obj.determineQualityLevel(comprehensive_scores(i));end% 存储结果obj.EvaluationResults.ComprehensiveScores = comprehensive_scores;obj.EvaluationResults.MembershipMatrix = membership_matrix;obj.EvaluationResults.QualityLevels = quality_levels;obj.TrainingData = training_data;results = obj.EvaluationResults;endfunction level = determineQualityLevel(obj, score)% 根据得分确定质量等级if score >= 0.9level = '优秀';elseif score >= 0.8level = '良好';elseif score >= 0.7level = '中等';elseif score >= 0.6level = '合格';elselevel = '不合格';endendfunction visualizeResults(obj)% 结果可视化figure('Position', [100, 100, 1400, 900]);% 1. 综合得分分布subplot(2, 3, 1);scores = obj.EvaluationResults.ComprehensiveScores;histogram(scores, 10, 'FaceColor', [0.2 0.6 0.8], 'EdgeColor', 'none');title('训练质量综合得分分布', 'FontSize', 12, 'FontWeight', 'bold');xlabel('综合得分');ylabel('频数');grid on;% 2. 各指标权重分布subplot(2, 3, 2);pie(obj.Weights, obj.EvaluationIndicators(:,1));title('评价指标权重分布', 'FontSize', 12, 'FontWeight', 'bold');% 3. 质量等级分布subplot(2, 3, 3);levels = obj.EvaluationResults.QualityLevels;level_categories = {'优秀', '良好', '中等', '合格', '不合格'};level_counts = zeros(1, 5);for i = 1:length(levels)idx = find(strcmp(level_categories, levels{i}));level_counts(idx) = level_counts(idx) + 1;endbar(level_counts, 'FaceColor', [0.8 0.4 0.2]);set(gca, 'XTickLabel', level_categories);title('训练质量等级分布', 'FontSize', 12, 'FontWeight', 'bold');ylabel('数量');% 4. 指标隶属度热力图subplot(2, 3, 4);imagesc(obj.EvaluationResults.MembershipMatrix);colorbar;title('各指标隶属度热力图', 'FontSize', 12, 'FontWeight', 'bold');xlabel('评价指标');ylabel('训练样本');set(gca, 'XTick', 1:length(obj.EvaluationIndicators));set(gca, 'XTickLabel', obj.EvaluationIndicators(:,1), 'XTickLabelRotation', 45);% 5. 雷达图 - 平均隶属度subplot(2, 3, 5);avg_membership = mean(obj.EvaluationResults.MembershipMatrix, 1);radar_plot(avg_membership, obj.EvaluationIndicators(:,1));title('各指标平均隶属度雷达图', 'FontSize', 12, 'FontWeight', 'bold');% 6. 时间趋势分析（如果有时间序列数据）subplot(2, 3, 6);plot(scores, '-o', 'LineWidth', 2, 'MarkerSize', 4, 'Color', [0.2 0.7 0.3]);title('训练质量时间趋势', 'FontSize', 12, 'FontWeight', 'bold');xlabel('训练批次');ylabel('综合得分');grid on;sgtitle('模拟机协同训练质量评价综合分析', 'FontSize', 16, 'FontWeight', 'bold');endfunction generateReport(obj)% 生成评价报告fprintf('\n=== 模拟机协同训练质量评价报告 ===\n');fprintf('评价时间: %s\n', datestr(now));fprintf('评价样本数: %d\n', length(obj.EvaluationResults.ComprehensiveScores));avg_score = mean(obj.EvaluationResults.ComprehensiveScores);fprintf('平均综合得分: %.4f\n', avg_score);levels = obj.EvaluationResults.QualityLevels;level_dist = tabulate(levels);fprintf('\n质量等级分布:\n');for i = 1:size(level_dist, 1)fprintf('  %s: %.1f%% (%d个)\n', level_dist{i,1}, level_dist{i,3}, level_dist{i,2});end% 薄弱环节分析avg_membership = mean(obj.EvaluationResults.MembershipMatrix, 1);[min_membership, min_idx] = min(avg_membership);fprintf('\n薄弱环节分析:\n');fprintf('  最薄弱指标: %s (平均隶属度: %.4f)\n', ...obj.EvaluationIndicators{min_idx,1}, min_membership);% 改进建议fprintf('\n改进建议:\n');if min_membership < 0.7fprintf('  * 重点加强"%s"方面的训练\n', obj.EvaluationIndicators{min_idx,1});endif avg_score < 0.8fprintf('  * 建议增加协同训练频次\n');fprintf('  * 优化训练情景设计\n');endendend
end%% 辅助函数 - 雷达图绘制
function radar_plot(data, labels)n = length(data);angles = linspace(0, 2*pi, n+1);angles = angles(1:end-1);% 扩展数据以闭合图形data_radar = [data, data(1)];angles_radar = [angles, angles(1)];polarplot(angles_radar, data_radar, 'LineWidth', 2, 'Color', [0.8 0.2 0.2]);thetaticks(rad2deg(angles));thetaticklabels(labels);rlim([0 1]);
end

4. 应用示例

%% 模拟机协同训练质量评价应用示例
function main_simulator_evaluation()% 清空环境clear; close all; clc;% 生成模拟数据（实际应用中从数据库读取）num_samples = 50;num_indicators = 8;% 模拟训练数据（0-1之间的评分）rng(42);  % 设置随机种子保证可重复性training_data = 0.6 + 0.3 * rand(num_samples, num_indicators);% 创建评价对象evaluator = SimulatorTrainingQualityEvaluation();% 执行评价results = evaluator.evaluateTrainingQuality(training_data);% 可视化结果evaluator.visualizeResults();% 生成评价报告evaluator.generateReport();% 保存结果save('training_evaluation_results.mat', 'evaluator', 'results');fprintf('\n评价完成！结果已保存。\n');
end