Smart canteen data analysis system

Smart canteen data analysis system ｜ Big data analysis ｜ Data visualization

Demo

• Repo：https://github.com/sylvanding/AI-Restaurant-Data-Analysis
• Project presentations （ Simulate the real running environment ）：http://analysis.sylvanding.online
• Data display static page ：http://sylvanding.github.io/AI-Restaurant-Data-Analysis

• Username: admin
• Password: 123456

Table of Contents

• AI Restaurant Data Analysis
  • Demo
  • Background
  • Features
  • Database
  • Data processing
  • ARIMA Time series forecast sales
  • The front frame
  • Back end framework
  • Business requirements 1
    • 1.1 Demand for design
    • 1.2 Core code
    • 1.3 Effect display
  • Business demand 2
    • 2.1 Demand for design
    • 2.2 Core code
    • 2.3 Effect display
  • Business requirement 3
    • 3.1 Demand for design
    • 3.2 Core code
    • 3.3 Effect display
  • Business demand 4
    • 4.1 Demand for design
    • 4.2 Core code
    • 4.3 Effect display
  • Business needs five
    • 5.1 Demand for design
    • 5.2 Core code
    • 5.3 Effect display
  • Business needs 6
    • 6.1 Demand for design
    • 6.2 Core code
    • 6.3 Effect display
  • Business needs 7
    • 7.1 Demand for design
    • 7.2 Core code
    • 7.3 Effect display
  • Overall effect display
  • Conclusion
    • Meet the expected project results
    • Where the project needs to be improved
  • Contributor

Background

With the continuous expansion of college enrollment , The traditional canteen production mode and management concept have become the bottleneck of the development of colleges and universities .

Due to the diversity of College Students' dietary needs , Personalization is increasingly complex , The traditional canteen has gradually lost its competitiveness . Because of the lack of student satisfaction feedback , Or it takes a long time to get feedback , Single channel , Make the canteen operation and management lag , Unable to find and improve problems in time . In terms of canteen finance , A large amount of form data is needed to reflect the canteen sales , More complicated , Error prone .

Features

• The background of the intelligent canteen data analysis system can clear and count the daily canteen dish sales data , Return the turnover by day 、 Order quantity 、 Visitors flowrate 、 Average score of dishes .
• The intelligent canteen data analysis system calculates what nutrients are contained in the food consumed in each meal , Both students and the canteen can see the nutrients ingested today at a glance through the canteen management system platform .
• After the user finishes eating , You can comment on the dishes in the canteen through the scoring function , The background of smart canteen data analysis system will collect these data , These data will then be sent to the Canteen Manager .

Database

Data volume ：

• food surface 100 strip
• menu surface 106370 strip
• orderform surface 35496 strip
• user surface 500 strip

Data processing

ARIMA Time series forecast sales

Here is the top of the restaurant's sales 27 Days as source data , Predict the end of the restaurant 3 Days of sales , That is, set the number of prediction steps to 3 Step , The final result is shown below .

The grey line is for training 27 Data , The black line is the prediction of future value , The red line is 95% Upper and lower limits of confidence interval . That is to say, the real value in the future is 95% The probability of falling within this range . besides , You can see the use of ARIMA The result of long-term prediction by this method is trend .

Thus, based on ARIMA Time series restaurant sales forecast , The data for the last three days of predicted restaurant sales are 14733、15457 and 16058, Observation shows that the results obtained are not much different from the source data , In line with the price trend .

The front frame

• The front end uses Bootstrap Responsive layout design , Compatible with devices with different resolutions , To provide users with a better visual experience .
• Data visualization uses ApexCharts. Compare with Echarts,ApexCharts Lighter weight , The icon is simple and beautiful , High degree of customization . however ApexCharts The documentation is incomplete , It brings difficulty to development .
• Request remote data to adopt jQuery Of AJAX Realization , Request all data asynchronously when entering the website , Then render the chart , Data visualization .
• After basically realizing the front-end static web page style , establish JSP Dynamic web pages , Realize login, data request and other businesses .
• JSP Medium JSON Parsing using Alibaba Fastjson, Realization json Object and the JavaBean Object conversion .
• Use Maven management JAVA project , Use Git Project version control .

Back end framework

Back end adoption Spring+SpringMVC+Mybatis+Redis frame .

• Use SSM The framework implements eight modules, a total of seventeen interfaces
• Use for some nonparametric interfaces with slow back-end processing AOP Integration of cross cutting and weaving Redis, Yes Response Serialize storage
• use c3p0 Connection pool improves reuse and reduces database pressure
• Use for some nonparametric interfaces with slow back-end processing AOP Integration of cross cutting and weaving Redis, Yes Response Serialize storage
• Abandon the traditional JDBC, use c3p0 Connection pooling reduces the pressure on the database
• Log in to the business and end the password twice MD5 encryption , By writing Cookie and Redis Single sign on

Version of the iteration ：

7.09 Project construction and server environment construction , Realized business 1 The interface of ： Orders are aggregated by time

7.10 Discover business 1 The interface of Bug, The avoidance method is given , Realized business 5 The interface of ： Differences in consumption levels between men and women

7.11 Optimize the business 1 The interface of ： The returned data is in ascending order of time period , Optimize the business 5 The interface of ： Change the Chinese parameter name to English , Realized business 8 The interface of ： Return the turnover by day 、 Order quantity 、 Visitors flowrate 、 Average score

7.12 Parameter judgment is added to some interfaces , Realized business 2 The interface of ： Return to the hottest 、 Cold door 、 Highest sales 、 The lowest list of dishes , I've been looking for a packaged version for a day bug…

7.13 Realized business 4 The interface of ： People flow in three time periods every day

7.14 At the request of the front end, the business 8 Interface for integration , The framework integrates redis, It implements the administrator login interface and adds related tool classes , Add cross domain request configuration , Achieve business 11 The interface of ： Paging query

7.15 For the business 4 Add more query data items , Integrated total transaction volume interface , Deleted the original total transaction volume interface , Add the interface of average nutritional value of three meals , Fixed the business 8 The interface parameters are 1 It will explode when bug

7.16 Integrated for some nonparametric interfaces redis newly added , Modify some data interfaces and improve the login interface

7.18 Realization Cookie Verification interface

Business requirements 1

1.1 Demand for design

Smart canteen system login request business

1.2 Core code

public class AdminLoginServiceImpl implements AdminLoginService{
    


    @Resource
    private RedisUtil redisUtil;
    private AdminLoginMapper adminLoginMapper;

    public void setAdminLoginMapper(AdminLoginMapper adminLoginMapper) {
    
        this.adminLoginMapper = adminLoginMapper;
    }

    @Override
    public int login(String username, String password, HttpServletRequest request, HttpServletResponse response) {
    

        // Get the user name according to the user name 
        Admin admin = adminLoginMapper.getAdminByUsername(username);
        if(null == admin){
    
            // The user doesn't exist 
            return 0;
        }
        // password verifiers 
        if(!MD5util.formPassToDBPass(password,admin.getSalt()).equals(admin.getPassword())){
    
            // Wrong password 
            return 1;
        }
        // Generate Cookie
        String ticket = UUIDUtil.uuid();
        //Session Deposit in redis
        if(redisUtil.hasKey(username)){
    
            // Single sign on requires deleting the user's existing Session
            redisUtil.del((String) redisUtil.get(username));
            redisUtil.del(username);
        }
        redisUtil.set(ticket,username,2000);
        redisUtil.set(username,ticket,2000);
        // write in Cookie
        CookieUtil.setCookie(request,response,"ticket",ticket,-1);
        // Landing successful 
        return 2;
    }

    @Override
    public int getUserByCookie(HttpServletRequest request, HttpServletResponse response) {
    
        String Cookie = CookieUtil.getCookieValue(request,"ticket");
        if(redisUtil.hasKey(Cookie)){
    
            // Reset cache expiration time 
            redisUtil.expire(Cookie,2000);
            redisUtil.expire((String) redisUtil.get(Cookie),2000);
            //0 Indicates the login status 
            return 0;
        }
        //1 Indicates that you are not logged in , The front end needs to redirect the login page 
        return 1;
    }
}

1.3 Effect display

Business demand 2

2.1 Demand for design

Displays the total number of orders for the day 、 Average order score 、 Total passenger flow 、 Total turnover

2.2 Core code

--  The average score of the order of the day 
SELECT avg(X.result1) AS RESULT
FROM(SELECT menu_id,avg(mark) AS result1 FROM menu
WHERE menu_id IN
(SELECT menu_id FROM orderform WHERE DATE(TIME)='2022-06-29')
GROUP BY menu_id) X;
 
--  Total orders of the day 
SELECT COUNT(*) AS num
FROM orderform
WHERE DATE(TIME)='2022-06-29';
 
--  Total number of visitors for the day 
SELECT COUNT(DISTINCT user_id) AS num
FROM orderform
WHERE DATE(TIME)='2022-06-29';
 
--  The total trading volume of the day 
SELECT SUM(total_money)
FROM orderform
WHERE DATE(TIME)='2022-06-29';

2.3 Effect display

Business requirement 3

3.1 Demand for design

Show the total daily turnover in a month 、 Total visitors 、 Average order score 、 Total order quantity . Normalize the data , Display the change trend of data .

3.2 Core code

-- 30 Daily order average score 
SELECT DATE(TIME) AS TIME,avg(X.avg_menu) AS avg_mark
FROM orderform,
(SELECT menu_id,avg(mark) AS avg_menu
FROM menu
GROUP BY menu_id) AS X
WHERE X.menu_id=orderform.menu_id
GROUP BY DATE(TIME)
ORDER BY DATE(TIME) LIMIT 30;

-- 30 Daily total trading volume 
SELECT DATE(TIME)AS TIME, SUM(total_money)
FROM orderform
GROUP BY DATE(TIME) ORDER BY TIME LIMIT 30;

-- 30 Daily order quantity 
SELECT DATE(TIME)AS TIME, COUNT(*) AS order_num
FROM orderform
GROUP BY DATE(TIME) ORDER BY TIME LIMIT 30;

-- 30 Daily total visitors 
SELECT DATE(TIME)AS TIME, COUNT(DISTINCT user_id) AS order_num
FROM orderform
GROUP BY DATE(TIME) ORDER BY TIME LIMIT 30;

3.3 Effect display

Business demand 4

4.1 Demand for design

Predict the total turnover of the next day 、 Total visitors 、 Average order score 、 Total order quantity

4.2 Core code

clear all;
clc;
filename='D:\Desktop\ Distributed tasks \ARIMA Time series prediction results \transaction_data.csv';
Y=csvread(filename,1,1,[1,1,27,1]);
Y=ceil(Y);
Y=Y';
plot(Y)

%ACF and PACF chart 
figure
autocorr(Y)
figure
parcorr(Y)

% Smoothness test ,yd1_h_adf =1,yd1_h_kpss =0, Pass the test 
y_h_adf = adftest(Y);
y_h_kpss = kpsstest(Y);

%  First order difference , The result is stable . If it is still unstable , Calculate the difference again , Until it passes the inspection 
Yd1 = diff(Y);
yd1_h_adf = adftest(Yd1);
yd1_h_kpss = kpsstest(Yd1);

%Yd2 Convert to column vector 
Yd1=Yd1';
Y=Y';

LOGL = zeros(4,4); % Initialize
PQ = zeros(4,4);
for p = 1:4
    for q = 1:4
        Mdl = arima(p,1,q);
        [~,~,logL] = estimate(Mdl,Yd1,'Display','off');
        LOGL(p,q) = logL;
        PQ(p,q) = p + q;
     end
end

%reshape  Refactoring arrays 
LOGL = reshape(LOGL,16,1);
PQ = reshape(PQ,16,1);
[~,bic] = aicbic(LOGL,PQ+1,100);
a=reshape(bic,4,4);

%  Find the best lags value  x=2,y=1, The corresponding ARMA（2,1） Model 
a_max=max(a(:));
[x,y]=find(a==min(a(:)));


Mdl = arima(x, 1, y);  % The second variable value is 1, First order difference 
EstMdl = estimate(Mdl,Y);
[res,~,logL] = infer(EstMdl,Y);   %res That is, residual 

stdr = res/sqrt(EstMdl.Variance);
figure('Name',' Residual test ')
subplot(2,3,1)
plot(stdr)
title('Standardized Residuals')
subplot(2,3,2)
histogram(stdr,10)
title('Standardized Residuals')
subplot(2,3,3)
autocorr(stdr)
subplot(2,3,4)
parcorr(stdr)
subplot(2,3,5)
qqplot(stdr)
% The above figure shows the result of residual test .
% Standardized Residuals Is to check whether the residual is close to the normal distribution , The ideal residual should be close to the normal distribution ;
% ACF and PACF Test autocorrelation and partial autocorrelation of residuals , The ideal result should be that there are no points beyond the blue line in the graph ;
%  The last one QQ The figure is to check whether the residual is close to the positive distribution , In the ideal result, the blue dot should be close to the red line .

% Durbin-Watson  Statistics is the most commonly used autocorrelation measure in econometric analysis 
diffRes0 = diff(res);  
SSE0 = res'*res;
DW0 = (diffRes0'*diffRes0)/SSE0; % Durbin-Watson statistic,
%  This value is close to 2, Then it can be considered that there is no first-order correlation in the sequence .

%% 5. forecast 
step = 3; %  The number of prediction steps is  3
[forData,YMSE] = forecast(EstMdl,step,'Y0',Y);   
lower = forData - 1.96*sqrt(YMSE); %95 Lower confidence interval 
upper = forData + 1.96*sqrt(YMSE); %95 Upper limit of confidence interval 

plot(forData)
disp(forData)

figure()
plot(Y,'Color',[.7,.7,.7]);
hold on
h1 = plot(length(Y):length(Y)+step,[Y(end);lower],'r:','LineWidth',2);
plot(length(Y):length(Y)+step,[Y(end);upper],'r:','LineWidth',2)
h2 = plot(length(Y):length(Y)+step,[Y(end);forData],'k','LineWidth',2);
legend([h1 h2],'95%  confidence interval ',' Predictive value ',...
	     'Location','NorthWest')
title('Forecast')
hold off

4.3 Effect display

Business needs five

5.1 Demand for design

The average intake of nutrients in three meals in a month （ Including calories 、 protein 、 fat 、 carbohydrates 、 vitamin ）

5.2 Core code

SELECT (CASE 
WHEN HOUR(TIME) BETWEEN 6 AND 9 THEN 'morning' 
WHEN HOUR(TIME) BETWEEN 10 AND 13 THEN 'afternoon' 
WHEN HOUR(TIME) BETWEEN 16 AND 20 THEN 'evening' END) AS timeperiod,
SUM(calorie)/COUNT( DISTINCT order_id) AS avg_calorie,
SUM(carbohydrate)/COUNT( DISTINCT order_id) AS avg_arbohydrate,
SUM(fat)/COUNT( DISTINCT order_id) AS avg_fat,SUM(protein)/COUNT( DISTINCT order_id) AS avg_protein,
SUM(vitamin)/COUNT( DISTINCT order_id) AS avg_vitamin
FROM menu,orderform,food
WHERE menu.menu_id=orderform.menu_id  AND food.food_id= menu.food_id AND MONTH(TIME)='6'

5.3 Effect display

Business needs 6

6.1 Demand for design

The most popular 10 dish 、 The worst score 10 dish

6.2 Core code

-- The worst evaluation n dish ( The least popular )
SELECT food.food_id,food.name,avg(mark) AS avg_markFROM menu,foodWHERE menu.food_id=food.food_idGROUP BY food_id ORDER BY avg_mark ASC LIMIT 10;

-- The highest sales 10 dish ( The most popular )
 SELECT food.food_id,food.name,COUNT(*) AS order_numFROM menu,foodWHERE menu.food_id=food.food_idGROUP BY food_id ORDER BY order_num DESC LIMIT 10;

6.3 Effect display

Business needs 7

7.1 Demand for design

According to recent 60 Order data （ Pagination display ）, Can support conditional query

7.2 Core code

Database query code

SELECT order_id,user_id, X.content,TIME,total_money FROM
(SELECT menu_id,group_concat(food.name) AS content
FROM menu,food
WHERE menu.food_id = food.food_id
GROUP BY menu.menu_id) AS X,orderform
WHERE X.menu_id=orderform.menu_id
ORDER BY TIME DESC LIMIT 60 ;

java Code

package com.jiang.service;

import com.jiang.mapper.OrderMapper;
import com.jiang.pojo.Order;

import java.util.List;

public class OrderListServiceImpl implements OrderListService{
    

    private OrderMapper orderMapper;

    public void setOrderMapper(OrderMapper orderMapper) {
    
        this.orderMapper = orderMapper;
    }

    @Override
    public List<Order> getList() {
    
        return orderMapper.getOrderListByPage();
    }
}

7.3 Effect display

Overall effect display

Conclusion

Meet the expected project results

• Achieved dish scoring 、 Healthy diet 、 Order management 、 A series of functions such as background management .
• Realize the login function .
• Completed the prediction of turnover ARIMA Time series sales forecasting algorithm .

Where the project needs to be improved

• Realize the data analysis of multiple canteens .
• Achieve the proportion of food nutrition standards for three meals .
• Realize the addition, deletion, modification and query of data in real time on the web page .

Contributor