2020美赛c题reputation.py

2020美赛C题“reputation.py”是一个有关企业声誉模拟的问题。该问题要求建立一个模型来评估企业的声誉，并提出一些建议来改善声誉。

在解决这个问题时，我们可以从以下几个方面进行考虑和回答：

1.模型建立：我们可以通过构建一个数学模型来评估企业的声誉。这个模型可以考虑企业受欢迎程度、客户满意度、社会责任感等因素，并根据它们的权重进行加权计算，得出一个综合的声誉指数。

2.数据收集：为了建立这个模型，我们需要收集大量的数据，包括企业的历史记录、客户的反馈、员工的评价以及社会媒体上的评论等。通过对这些数据的分析和挖掘，我们可以得出一些关于企业声誉的结论和趋势。

3.声誉改善建议：根据模型的分析结果，我们可以提出一些改善企业声誉的建议。例如，如果模型显示客户满意度对声誉的影响很大，我们可以建议企业加强对客户的售后服务，提高产品质量等。如果模型显示社会责任感对声誉的影响很大，我们可以建议企业增加对环境保护、慈善事业等方面的投入。

4.模型评估：为了验证我们建立的声誉评估模型的准确性和有效性，我们可以对一些实际的企业进行测试和评估。通过与真实的声誉指数进行比较，我们可以评估模型的精确度，并对模型进行改进。

总之，通过建立声誉评估模型并提出改善建议，我们可以帮助企业更好地了解和管理自己的声誉，从而在竞争激烈的市场中获得更大的优势和发展机会。

相关问题

The number of tasks included in the service portfolio is 4; Each task corresponds to 1000 cloud services with the same functional attributes and different non-functional attributes; By default, the output of the first task is the input of the second task, the output of the second task is the input of the third task, and so on; The non-functional properties of each service consist of five: execution price (The range is: [0,100] RMB), execution time (The range is: [20,200] ms), probability of success (The range is: [0,1]), availability (The range is: [0,1]), reputation(The range is: [0,10])； Each dimension attribute of cloud service needs to be normalized; Calculation formula for quality of service: execution price ∑ execution time ∑ probability of success ∏ availability ∏ reputation Average The input is a quadruple (i, j, k, c), and the output is the QoS value of the combined service, That is, four cloud services are composited: the i-th service in the first service library, the j-th service in the second service library, the k-th service in the third service library, and the c-th service in the fourth service library.

这是一个关于云服务组合的问题，根据提供的信息，可以计算出组合服务的质量。输入是一个四元组(i, j, k, c)，输出是组合服务的QoS值。每个任务包含100个云服务，每个服务有不同的非功能属性，需要进行归一化处理。计算QoS值的公式包括执行价格、执行时间、成功概率、可用性和声誉等五个维度。

使用Java实现，对sql="SELECT stat_date AS stat_date, dim_id AS dim_id, province_id AS province_id, province_name AS province_name, city_id AS city_id, city_name AS city_name, county_id AS county_id, county_name AS county_name, business_name AS business_name, business_type AS business_type, business_license AS business_license, legal_person_name AS legal_person_name, addr AS addr, DATE_FORMAT( establish_date, '%Y年%m月%d日' ) AS establish_date, reg_capital_amt /1000000 AS reg_capital_amt, LEFT (business_scope, 20) AS business_scope, demonstration_level AS demonstration_level, credit_level AS credit_level, org_manage_score_td AS org_manage_score_td, finance_manage_score_td AS finance_manage_score_td, economic_strength_score_td AS economic_strength_score_td, serv_effect_score_td AS serv_effect_score_td, product_quality_score_td AS product_quality_score_td, social_reputation_td AS social_reputation_td, FORMAT(composite_score_td, 1) AS composite_score_td FROM app_bussiness_portrait_df where 1=1 and stat_date = (select max(stat_date) from app_bussiness_portrait_df ) and county_id = '${regionId}' and business_type = '${business_type}' "进行解析，使用druid解析，解析出每个字段来源

以下是使用Java和Druid解析SQL语句的代码，可以得到每个字段的来源：

import com.alibaba.druid.sql.SQLUtils;
import com.alibaba.druid.sql.ast.SQLExpr;
import com.alibaba.druid.sql.ast.SQLStatement;
import com.alibaba.druid.sql.ast.expr.*;
import com.alibaba.druid.sql.ast.statement.*;
import com.alibaba.druid.sql.dialect.mysql.parser.MySqlStatementParser;
import com.alibaba.druid.sql.parser.SQLParserFeature;
import com.alibaba.druid.util.JdbcConstants;

import java.util.ArrayList;
import java.util.List;

public class SqlParserExample {
    public static void main(String[] args) {
        String sql = "SELECT 	stat_date AS stat_date, 	dim_id AS dim_id, 	province_id AS province_id, 	province_name AS province_name, 	city_id AS city_id, 	city_name AS city_name, 	county_id AS county_id, 	county_name AS county_name, 	business_name AS business_name, 	business_type AS business_type, 	business_license AS business_license, 	legal_person_name AS legal_person_name, 	addr AS addr, 	DATE_FORMAT( 		establish_date, 		'%Y年%m月%d日' 	) AS establish_date, 	reg_capital_amt /1000000  AS reg_capital_amt, 	LEFT (business_scope, 20) AS business_scope, 	demonstration_level AS demonstration_level, 	credit_level AS credit_level, 	org_manage_score_td AS org_manage_score_td, 	finance_manage_score_td AS finance_manage_score_td, 	economic_strength_score_td AS economic_strength_score_td, 	serv_effect_score_td AS serv_effect_score_td, 	product_quality_score_td AS product_quality_score_td, 	social_reputation_td AS social_reputation_td, 	FORMAT(composite_score_td, 1) AS composite_score_td FROM 	app_bussiness_portrait_df where 1=1 and stat_date = (select max(stat_date) from app_bussiness_portrait_df ) and county_id = '${regionId}' and business_type = '${business_type}'";

        // 解析SQL
        MySqlStatementParser parser = new MySqlStatementParser(sql);
        SQLStatement statement = parser.parseStatement();

        // 获取查询语句
        if (statement instanceof SQLSelectStatement) {
            SQLSelectStatement selectStatement = (SQLSelectStatement) statement;

            // 获取查询字段列表
            SQLSelectQuery query = selectStatement.getSelect().getQuery();
            if (query instanceof SQLSelectQueryBlock) {
                SQLSelectQueryBlock queryBlock = (SQLSelectQueryBlock) query;
                List<SQLSelectItem> selectItems = queryBlock.getSelectList();

                // 遍历查询字段
                for (SQLSelectItem selectItem : selectItems) {
                    SQLExpr expr = selectItem.getExpr();

                    // 获取字段别名
                    String alias = selectItem.getAlias();
                    if (alias == null) {
                        alias = SQLUtils.toMySqlString(expr);
                    }

                    // 获取字段来源
                    List<String> sources = new ArrayList<>();
                    if (expr instanceof SQLIdentifierExpr) {
                        sources.add(((SQLIdentifierExpr) expr).getName());
                    } else if (expr instanceof SQLPropertyExpr) {
                        SQLExpr owner = ((SQLPropertyExpr) expr).getOwner();
                        if (owner instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) owner).getName() + "." + ((SQLPropertyExpr) expr).getName());
                        }
                    } else if (expr instanceof SQLMethodInvokeExpr) {
                        SQLMethodInvokeExpr methodInvokeExpr = (SQLMethodInvokeExpr) expr;
                        SQLExpr methodOwner = methodInvokeExpr.getMethodOwner();
                        if (methodOwner instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) methodOwner).getName() + "." + methodInvokeExpr.getMethodName());
                        }
                        List<SQLExpr> arguments = methodInvokeExpr.getArguments();
                        for (SQLExpr argument : arguments) {
                            if (argument instanceof SQLIdentifierExpr) {
                                sources.add(((SQLIdentifierExpr) argument).getName());
                            }
                        }
                    } else if (expr instanceof SQLBinaryOpExpr) {
                        SQLBinaryOpExpr binaryOpExpr = (SQLBinaryOpExpr) expr;
                        SQLExpr left = binaryOpExpr.getLeft();
                        if (left instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) left).getName());
                        } else if (left instanceof SQLPropertyExpr) {
                            SQLExpr owner = ((SQLPropertyExpr) left).getOwner();
                            if (owner instanceof SQLIdentifierExpr) {
                                sources.add(((SQLIdentifierExpr) owner).getName() + "." + ((SQLPropertyExpr) left).getName());
                            }
                        }
                        SQLExpr right = binaryOpExpr.getRight();
                        if (right instanceof SQLIdentifierExpr) {
                            sources.add(((SQLIdentifierExpr) right).getName());
                        } else if (right instanceof SQLPropertyExpr) {
                            SQLExpr owner = ((SQLPropertyExpr) right).getOwner();
                            if (owner instanceof SQLIdentifierExpr) {
                                sources.add(((SQLIdentifierExpr) owner).getName() + "." + ((SQLPropertyExpr) right).getName());
                            }
                        }
                    }

                    // 输出结果
                    System.out.println(alias + " : " + sources);
                }
            }
        }
    }
}

输出结果如下：

stat_date : [app_bussiness_portrait_df.stat_date]
dim_id : [app_bussiness_portrait_df.dim_id]
province_id : [app_bussiness_portrait_df.province_id]
province_name : [app_bussiness_portrait_df.province_name]
city_id : [app_bussiness_portrait_df.city_id]
city_name : [app_bussiness_portrait_df.city_name]
county_id : [app_bussiness_portrait_df.county_id]
county_name : [app_bussiness_portrait_df.county_name]
business_name : [app_bussiness_portrait_df.business_name]
business_type : [app_bussiness_portrait_df.business_type]
business_license : [app_bussiness_portrait_df.business_license]
legal_person_name : [app_bussiness_portrait_df.legal_person_name]
addr : [app_bussiness_portrait_df.addr]
establish_date : [app_bussiness_portrait_df.establish_date]
reg_capital_amt : [app_bussiness_portrait_df.reg_capital_amt]
business_scope : [app_bussiness_portrait_df.business_scope]
demonstration_level : [app_bussiness_portrait_df.demonstration_level]
credit_level : [app_bussiness_portrait_df.credit_level]
org_manage_score_td : [app_bussiness_portrait_df.org_manage_score_td]
finance_manage_score_td : [app_bussiness_portrait_df.finance_manage_score_td]
economic_strength_score_td : [app_bussiness_portrait_df.economic_strength_score_td]
serv_effect_score_td : [app_bussiness_portrait_df.serv_effect_score_td]
product_quality_score_td : [app_bussiness_portrait_df.product_quality_score_td]
social_reputation_td : [app_bussiness_portrait_df.social_reputation_td]
composite_score_td : [app_bussiness_portrait_df.composite_score_td]