CN116452193A - Visual database system for express packaging waste and grid management method - Google Patents
Visual database system for express packaging waste and grid management method Download PDFInfo
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Abstract
The invention provides an express packaging waste visualization database system and a gridding management method, which relate to the technical platform construction of a current hot spot express packaging waste recycling and underprogram based on the express packaging waste recycling of college students; on the basis of each dormitory building, the demand side is a college student group who is frequently purchased online, and college students are encouraged and advocated to secondarily recycle the express packaging waste by constructing a waste recycling platform, so that the functions of reasonably distributing recycling resources of the express packaging waste, reducing the express waste, reducing carbon emission and reducing environmental pollution are realized; and the campus express service is taken as a core, big data is introduced on the basis of the substitution express service, cloud computing is carried out in cooperation with a cloud server, data visualization is realized, service platforms such as recovery, social contact, information and data are built, and multi-side contact of government, universities, enterprises and college students is initially promoted.
Description
Technical Field
The invention belongs to the technical field of recycling of packaging waste, and particularly relates to an express packaging waste visualization database system and a gridding management method.
Background
The online shopping can realize a convenient, rapid and economic consumption mode from door to door, and with the rapid development of the Internet and the mobile Internet, the online shopping becomes an indispensable part of the daily life of people, but with the continuous increase of the express business volume. On the one hand, due to the large energy consumption during transportation, express delivery has become one of the most important global carbon emission sources. On the other hand, the continuous and rapid increase of the online shopping proportion also causes the problems of express packaging sequelae and the like. Most of the express packaging waste mainly consists of recoverable packaging materials such as cartons, packaging bags and the like, however, most of the existing express packaging waste is mixed into household garbage, and enters a landfill site and an incineration site together with the household garbage, so that not only is the waste of data formed, but also environmental influences such as generation of carbon dioxide, methane and other room gases to cause global warming are brought.
In the prior colleges 82 of certain market, the number of college students 168.29 ten thousand, the number of online shopping orders of colleges and cartons generated by the online shopping orders of the colleges and the universities is exponentially multiplied, and in the early investigation, the average number of the college teachers and students buying express delivery exceeds the average level of society, a large number of express delivery packages flow into the colleges each month, the number of the produced express delivery package waste is huge, and the secondary recycling ratio is low. Where the carton is the highest, the recycling value of such waste is the highest.
The logistics enterprises also need the behavior data of the users to conduct market segmentation for the current huge order quantity so as to improve the distinction between the logistics enterprises and other logistics enterprises. By tracking and analyzing the logistics data, the logistics big data application can make intelligent decisions and suggestions for logistics enterprises according to conditions. In logistics decision-making, big data technology application relates to competitive environment analysis, logistics supply and demand matching, logistics resource optimization and configuration and the like. In competitive environment analysis, in order to maximize the benefits, it is necessary to comprehensively analyze competitors and predict their behaviors and movements so as to know about the partner that should be selected in a certain area or in a certain special period. In the aspect of matching the logistics supply and the demand, the logistics supply and demand conditions of a specific period and a specific area need to be analyzed, so that reasonable distribution management is performed. In the aspect of logistics resource optimization and configuration, the method mainly relates to transportation resources, storage resources and the like. The logistics market has strong dynamic property and randomness, the market change condition needs to be analyzed in real time, the current logistics demand information is extracted from massive data, and meanwhile, the configured and to-be-configured resources are optimized, so that the reasonable utilization of the logistics resources is realized.
Disclosure of Invention
The invention aims to solve the technical problems that: the visual database system and the grid management method for the express packaging waste are used for reasonably distributing the recovery resources of the express packaging waste.
The technical scheme adopted by the invention for solving the technical problems is as follows: a visual database system for express packaging waste comprises a user side, a database, an applet and a website; the user end comprises a student end, a collector end, an university end and an enterprise end; the student end and the collector end input data into the database through the applet of the mobile device; the data input by the student end are registration information comprising names, university information, academic numbers, mobile phone numbers and mailboxes, and order information of express delivery service, express pickup service and express garbage recovery service are reserved for each time; the data input by the collector terminal is order confirmation information comprising user information and recovered article information; the enterprise end and the institution end access the data in the authority through the background of the website; the enterprise end is used for checking the recycling amount and proportion information of the garbage such as the cartons, the plastic bags, the foam and the like; the college end is used for checking information including student names, colleges, school numbers and garbage collection amount; the applet comprises a registration page, a home page function page, a user reservation interface, a recovery function module, an integral presentation module and a statistical information module; the registration page is used for a user to input registration information and login information, and jumps to the home page function page when the login information is consistent with the registration information; the home page function page is used for a user to check reservation information and point rewards and guides the user to use corresponding services; the user reservation interface is used for a user to input reservation information; the recycling function module is used for inputting order related information comprising user information and recycling article information; the point presenting module and the statistical information module are used for counting and managing user rewards; the statistical information module is also used for counting the recovery amount of the waste received by the collector in a period of time; the website comprises a homepage and a database visualization interface; the database visual interface is used for regularly displaying the statistics of the production quantity of the express package waste, the statistics of the recovery ratio of the express package and the statistics of the specification ratio of the express package.
According to the scheme, the cost amount of the mailing express service and the pickup express service is determined by the user parcel and the service route; the express garbage collection service is freely opened to users, and comprises off-line website service and on-line processing service.
According to the scheme, the applet adopts the regional meshing technology, creates a map instance and performs secondary development and deployment to realize dot division in a target region; the applet is also used for assessment and compensation management of the contractors.
According to the scheme, the database is based on the statistical data of the express packaging garbage which is not currently available, and based on the combination of research, model and data analysis, a visualized database is built by using a shiny package of R language.
A gridding management method for express packaging waste comprises the following specific steps:
s1: the student end reserves the latest waste recovery point through the applet and enters a reservation interface to fill in the name, the mobile phone, the address and the reservation time; selecting the type of the recycled waste and the nearest recycling point, submitting a reservation, and finding the nearest site and the nearest collector by the system through a map and navigation service;
s2: the recycling agent side obtains the weight of the recycled objects through weighing and fills in the reservation list of the user to complete recycling;
the applet sends a request for updating the reservation list and ending the reserved timing task to the background of the website, calculates the contribution value and the point of the user and presents the contribution value and the point;
s3: the background of the website records the total weight of the articles recovered by the receiver every day, and stores the data in a database; the front end of the website realizes a data visualization function through an open source frame Echart.js and canvas 2D drawing technology, and the data is visually displayed on a chart in the form of a recovery rate curve within one week, one month and one year;
s4: the enterprise terminal displays the distribution and the list of the cooperative schools and provides options for auditing the cooperative application of the schools or adding the cooperative schools; displaying all the collection points of the school and corresponding collector information, managing the collectors, and performing addition, deletion and correction on the information including daily collection amount of the collectors.
Further, in the step S1, the small program predicts the related data of the express package based on the gray model and the BP neural network, and the method comprises the following steps:
s11: selecting data; in order to ensure data continuity, screening or processing is carried out on the data in a data acquisition stage;
s12: a predictive model; establishing a gray prediction and BP neural network combined simulation for the annual month time corresponding to the express traffic;
known reference data column x (0) ＝(x (0) (1)，x (0) (2)，…x (0) (n)) and 1 time of accumulation to generate a sequence (1-AGO),
x (1) ＝(x (1) (1)，x (1) (2)，…x (1) (n))＝x (0) ＝(x (0) (1)，x (0) (1)+x (0) (2)，x (0) (1)+…+x (0) (n))
x (1) mean generation column z of (1) (1) ＝(z (1) (2)，z (1) (3)，…，z (1) (n))，
z (1) (k)＝0.5x (1) (k)+0.5x (1) (k-1)，k＝2，3…n
Establishing an ash differential equation:
x (0) (k)+az (1) (k)＝b，k＝2，3，…n，
the corresponding whitening differential equation is:
s13: checking and processing data; performing checking treatment on the known data columns to ensure the feasibility of the modeling method; according to the reference number series x (0) ＝(x (0) (1)，x (0) (2)，…x (0) (n)) calculating the step ratio of the sequences:
if all the level ratios lambda (k) fall within the acceptable coverageIn, then sequence x (0) Gray prediction can be performed as data of the model GM (1, 1); otherwise, it is necessary to pair the sequence x (0) Performing comparison conversion treatment to make the ratio conversion treatment fall in the capacity coverage, namely taking a proper constant c to perform translation conversion:
y (0) (k)＝x (0) (k)+c，k＝1，2…，n
let sequence y (0) ＝(y (0) (1)，y (0) (2)，…y (0) The stage ratio of (n)) is:
s14: establishing a model; the GM (1, 1) model is built from the whitened differential equation, representing a gray model with 1 st order differential equation and only 1 variable, the prediction formula is as follows:
k＝0，1，…，n-1，…
s15: and (5) checking the predicted value.
Further, in the step S15, the inspection method includes:
s151: checking relative errors; is provided withCalculating relative error:
if delta (k) < 0.2, then the general requirement is considered to be met; if delta (k) < 0.1, then higher requirements are deemed to be met;
s152: step D, checking a level ratio deviation value; first by reference number series x (0) (k-1)、x (0) (k) Calculating a step ratio lambda (k), and then solving a step ratio deviation of response by using a development coefficient alpha:
if |ρ (k) | < 0.2, then the general requirement is considered to be met; if |ρ (k) | < 0.1, then higher requirements are considered to be met;
s153: predicting and forecasting; and obtaining a predicted value in a specified time zone by using a GM (1, 1) model, and giving a predicted forecast of response according to the requirements of actual problems.
Further, the BP neural network is trained according to a study mode of a teacher, and the specific steps are as follows:
when a pair of learning modes are provided for a network, the activation values of neurons of the learning modes are transmitted from an input layer to an output layer through each hidden layer, and each neuron in the output layer outputs a network response corresponding to the input mode;
adopting an error back propagation algorithm, and correcting each connection weight layer by layer from an output layer through each hidden layer and finally returning to an input layer according to the principle of reducing the error between expected output and actual output;
with the continuous correction of the error back propagation training, the accuracy of the network response to the input mode is continuously improved.
A computer storage medium having stored therein a computer program executable by a computer processor for performing a method of grid management of packaging waste for an express delivery.
The beneficial effects of the invention are as follows:
1. the invention discloses an express packaging waste visualization database system and a gridding management method, which relate to the technical platform construction of a current hot spot express packaging waste recycling and underprogram based on the express packaging waste recycling of college students; on the basis of each dormitory building, the demand side is a college student group who is frequently purchased online, and college students are encouraged and advocated to secondarily recycle the express packaging waste by constructing a waste recycling platform, so that the functions of reasonably distributing recycling resources of the express packaging waste, reducing the express waste, reducing carbon emission and reducing environmental pollution are realized; and the campus express service is taken as a core, big data is introduced on the basis of the substitution express service, cloud computing is carried out in cooperation with a cloud server, data visualization is realized, service platforms such as recovery, social contact, information and data are built, and multi-side contact of government, universities, enterprises and college students is initially promoted.
2. The invention aims at promoting the recovery chain of the express packaging waste, is based on the college student corridor as the gridding management, and is provided with the special service functions of recovery rewarding mechanism, one-key reservation, express inquiry, recovery statistics and the like, thereby inspiring and creating a new self-service recovery mode of low-carbon and low-consumption material flow packaging recovery and reuse. The advantages of taking root at universities are fully played, employment enthusiasm of colleges and universities is stimulated, factors such as participation degree of garbage collection of express packages and the like are comprehensively considered by combining with the activity of small programs of users, and based on a certain service range, a mode that a collector is from a client is adopted to recruit the collector, so that the users can submit the application of the collector in the small programs; the student self-value-improving platform provides a new part-time channel platform for students, and can obtain a certain subsidy while improving the self-value.
Provides a more scientific comprehensive assessment scheme for the institutions. The current moral practice evaluation of most institutions has the phenomena of no compliance of a system, no compliance of data and no compliance of rules, so that the comprehensive evaluation finally becomes 'cultural evaluation', 'match evaluation'. The universities and colleges can provide big data reference for comprehensive evaluation through the product, and the universities and colleges can be helped to normalize the behavioral moral evaluation scheme in comprehensive evaluation by using the big data. The method adds a new mode for student quality assessment of the universities and the universities can build a student comprehensive quality assessment system by utilizing Internet, cloud computing and big data analysis means according to the data related to student garbage classification in the building recycling bar database.
3. The logistics industry needs a large amount of real data, and the database generated by the method has great reference significance for logistics enterprises, and achieves mutual assistance through cooperation with express dot products in universities. The express delivery point provides recent express delivery data at regular time, and the generation of express delivery packaging waste is quantified. The invention reasonably predicts the express delivery quantity in a special period by combining the data, adjusts the service quantity in advance, and achieves the purpose of recovering the express delivery garbage and keeping the high duty ratio. And the data of the express packaging waste are summarized, and the development trend of the express packaging waste is reasonably predicted by technical means. Through setting up fixed express packaging garbage recovery points, gridding a service area, and reserving a cashier to go to a service business through a small program in a designated area by a user, the problem of 'last kilometer' of express is solved; and judging the difference of the express packaging waste in different areas by combining the data of the earlier investigation, dividing the areas by combining certain data indexes, and setting up express packaging waste points in the areas. Reasonable distribution of the recycling resources of the express packaging garbage is realized, and insufficient utilization and excessive utilization of the resources are avoided. The invention also provides a recycling scheme of easily-lost products such as cartons, damping foam, plastic bags and the like for logistics enterprises, takes a large amount of universities in a certain market as a foundation, helps the logistics enterprises to realize the improvement of recycling rate of the easily-lost products of the enterprises with smaller manpower and material resource cost, helps the logistics enterprises to explore a green transformation scheme with low cost, accumulates transformation experience, accelerates the transformation process of the enterprises, and further fits the economic benefit of sustainable development of the enterprises.
4. The large database of the present invention assists in government agency regulation. The invention quantifies the generation characteristics and development trend of the express packaging waste; the production characteristics and the development trend of the express packaging waste are clear through multiple method systems such as field measurement, investigation and model simulation, and basic data are provided for realizing economic value and environmental benefit of the regional level recoverable packaging waste. The construction of the 'no-waste city' is promoted, the technical advantages of the 'no-waste city' are combined, the regional resources are reasonably allocated, and the coexistence of economic benefit and social value is realized. A test platform is provided for government to implement new garbage collection, a large amount of real and reliable data generated after the platform operates are fed back to the government, a foundation for making new garbage classification details is provided, a set of benign closed loops of making, implementing, feeding back, adjusting and reformulating are formed, the government is helped to find out the best balance point of the implementation scheme of the new garbage collection as soon as possible, the implementation of urban garbage classification is accelerated, and the construction of waste-free cities is accelerated.
Drawings
FIG. 1 is a functional block diagram of an embodiment of the present invention.
FIG. 2 is a registration interface diagram of an embodiment of the present invention.
FIG. 3 is a login interface diagram of an embodiment of the present invention.
Fig. 4 is a user's home page view of an embodiment of the present invention.
Fig. 5 is a diagram of a one-touch reservation interface in accordance with an embodiment of the present invention.
FIG. 6 is a diagram of a select recovery type interface according to an embodiment of the present invention.
FIG. 7 is a diagram of a select recovery point interface in accordance with an embodiment of the present invention.
FIG. 8 is a diagram of a selection time interface according to an embodiment of the present invention.
FIG. 9 is a reservation success interface diagram of an embodiment of the present invention.
Fig. 10 is a graph of scan results for an embodiment of the present invention.
FIG. 11 is an integrated plot of an embodiment of the present invention.
FIG. 12 is a diagram of a point audit of an embodiment of the present invention.
FIG. 13 is a diagram of a points account of an embodiment of the present invention.
Fig. 14 is an integral extraction diagram of an embodiment of the present invention.
Fig. 15 is a recycle log diagram of an embodiment of the invention.
FIG. 16 is a statistical chart of recovery in an embodiment of the present invention.
Fig. 17 is a personal information diagram of an embodiment of the present invention.
FIG. 18 is a top view of a web site in accordance with an embodiment of the present invention.
Fig. 19 is a chart showing statistics of the production amount of various kinds of package waste of express delivery in a university before a shopping mall according to an embodiment of the present invention.
FIG. 20 is a ratio view of various types of packages for a college prior to a shopping mall in accordance with an embodiment of the present invention.
FIG. 21 is a chart of the same type of packaging ratio for different specifications within one week after a shopping mall according to an embodiment of the present invention.
FIG. 22 is a flowchart of the operation of the applet in an embodiment of the invention.
FIG. 23 is a block diagram of a BP neural model according to an embodiment of the invention.
FIG. 24 is a diagram of a functional network code according to an embodiment of the present invention.
FIG. 25 is a database code diagram of an embodiment of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the detailed description.
Referring to fig. 1, the embodiment of the invention performs secondary development based on the hundred-degree map API, constructs a novel business service mode, namely a gridding service mode, reasonably distributes regional resources by combining the characteristics of data visualization, and fully schedules the regional resources.
The embodiment of the invention adopts a WeChat applet development mode, and the WeChat applet has the advantages of light weight, short development period, good cross-platform compatibility, low maintenance cost and the like, is suitable for platform popularization in combination with public numbers by relying on the huge flow of WeChat platforms.
The embodiment of the invention adopts a mode of school cooperation and enterprise association, and adopts a grey model and BP neural network to predict and visualize the related data of the express package for the collected data of the express package.
The embodiment of the invention comprises a login inlet facing a student end, a collector end, an institution end and an enterprise end; in terms of the light weight and privacy of the recycling bar applet, the enterprise end and the institution end do not access data through the recycling bar applet, but submit a resident application through a login official website, and provide special background to access data in the authority after the application. The specific access rights are assigned as in table 4-1.
| User terminal | Database usage rights |
| Student terminal | Without database usage rights |
| Collecting member terminal | Without database usage rights |
| Enterprise terminal | Can check the recycling amount and the recycling proportion of garbage such as cartons, plastic bags, foam and the like |
| University end | Can check the information such as student name, academy, school number, garbage collection amount and the like |
After entering the program, students register the product by filling in names, school information (at schools and colleges), numbers, mobile phone numbers and mailboxes, become a recycling bar member after registration is completed, and the recycling bar member initiates three orders of sending express, collecting express and recycling express garbage through the recycling bar applet.
The shipping and collection of the express is opened to the customer as a payment service, and after the order is completed, the amount of the fee is determined by the customer's parcel volume and the service path. The express garbage collection service is freely opened to users, and the service provides two execution modes: the system issues a certain amount of 'points' to the user according to the weight of the logistics garbage and the different recycling modes after the staff determines that the order is completed (the points obtained by the mode of going to the website by self are slightly higher than the points obtained by recycling at the upper gate).
The user can apply for the order receiving service to be a recycling bar sorting master while using various order services of the applet. After sorting and recycling the related express garbage, the user needs to register as a recycling bar sorting master by combining the prior order recycling record and the point situation of the user, and can accept orders issued by other users through the small program and obtain certain compensation and rewards by completing the orders.
1. Description of applet function
(1) Registration page and home page function page
The applet provides a functional home interface that can intuitively present basic functions and can quickly guide a user to use corresponding services. The interface also provides viewing functionality for user subscription information and bonus points. See fig. 2-4.
(2) User reservation interface
The user fills in the information through our reservation interface. In which the user needs to fill in order related information. See fig. 5-10.
(3) Realize the recovery function
After the user initiates the reservation, the user arrives at the collection point for collection and submits the recycle in the appointed reservation time. The user id can be obtained by scanning the two-dimensional code of the user by the receiver, and then the request for obtaining the unfinished reservation list of the user is sent to the background.
The recycling agent obtains the weight of the recycled objects by weighing and fills in the reservation list of the user, and clicks 'confirm recycling' to complete recycling. The corresponding applet will send a timing task to the background requesting to update the reservation order and end the reservation, calculating the user contribution and the points. See fig. 11.
The background records the total weight of the articles recovered by the collector every day, stores the data in a database, and the front end visually displays the data on a chart through canvas 2D drawing technology, so that the recovery amount curves within one week, one month and one year can be switched and checked, and the statistical display function of the recovery data is realized.
(4) Integration presentation
The user can obtain corresponding points after recycling each time, the number of the points corresponds to corresponding electronic money, and the user points can be converted into the electronic money and presented to a payment bank or a bank account when accumulating to a certain amount. See fig. 12-14.
(5) Statistical information
The student user can obtain integral and contribution value in waste recycling process, integral and contribution value all are decided according to waste weight, and this platform records student end user integral and contribution value size with the help of visual page. The platform can also record and count the recovery amount of waste received by the collector in a period of time. See fig. 15-17.
2. Web site function introduction
(1) Homepage of website
To serve businesses and departments that have business and business needs, a web site (www.boxibility.cn) is presented to the portion of users on which the desired information services are provided to the business and business needs. See fig. 18.
(2) Database visualization
The website provides a visual presentation of the data to the user in need thereof. Users can view the data chart through the visual database of the website, thereby obtaining certain reference data and helping them to make more reliable business decisions.
The statistics of the production amount of the express package waste released regularly become a reference for a user in determining the input amount of the express package. See fig. 19.
The regularly released package recycling proportion statistics become references for users in determining package types of express. See fig. 20.
The regularly issued express package specification proportion statistics become references for users in determining express package specifications. See fig. 21.
3. Applet architecture
(one) region gridding technique
And combining various network map navigation tools existing in the market, selecting a map open source API technology for secondary development and deployment, and performing secondary development and deployment according to the self selection according to the self-created map instance to realize the dot division in the target area. The partial codes are as follows:
importing a map
<！DOCTYPE html>
<html>
<head>
<meta name＝"viewport"content＝"initial-scale＝1.0,user-scalable＝no"/>
<meta http-equiv＝"Content-Type"content＝"text/html；charset＝utf-8"/>
<title>Hello,World</title>
<style type＝"text/css">
html{height:100％}
body{height:100％；margin:0px；padding:0px}
#container{height:100％}
</style>
< script type= "text/javascript" src= "https:// api. Map. Bai. Com/apiv = 3.0& ak = your key" >/script >
</head>
<body>
<div id＝"container"></div>
<script type＝"text/javascript">
Creation of map instances
var map＝new BMap.Map("container")；
Creation point coordinates (coordinates of a place)
var point＝new BMap.Point(116.404,39.915)；
Initializing map, setting central point coordinates and map level
map.centerAndZoom(point,15)；
</script>
</body>
</html
Lead-in assembly
map.addControl(new BMap.NavigationControl())；
map.addControl(new BMap.ScaleControl())；
map.addControl(new BMap.OverviewMapControl())；
map.addControl(new BMap.MapTypeControl())；
map.setCurrentCity；
Implementation of (II) applet technology
Referring to fig. 22, a student user clicks "one-click reservation" in the applet home page to reserve the nearest waste recovery point, and enters a reservation interface to fill in the name, cell phone, address and reservation time, selects the type of waste recovered, such as corrugated cardboard box, plastic bag, etc., and selects the nearest recovery point to submit the reservation, and the system finds the nearest site and collector through map and navigation services.
The recycling agent obtains the weight of the recycled objects by weighing and fills in the reservation list of the user, and clicks 'confirm recycling' to complete recycling. The corresponding applet will send a timing task to the background requesting to update the reservation order and end the reservation, calculate the user contribution and points and can be presented. The background records the total weight of the articles recovered by the collector every day, the data are stored in a database, the front end visually displays the data on a chart through canvas 2D drawing technology, and the recovery amount curve in one week, one month and one year can be switched and checked.
The enterprise terminal can display a cooperation school list, and the detail page entering the school can check all the generation collection points and corresponding generation collector information of the school, and can audit the cooperation application submitted by the school or directly add the cooperation school. The data visualization function is realized through the front-end open source framework Echart.js, and the distribution and recovery data of the cooperation schools of each province and city of China are displayed. The method manages the collector, checks the daily recovery amount of the collector, and can perform the basic functions of adding, deleting and checking the collector.
(III) prediction and visualization implementation of express package related data based on gray model and BP neural network
(1) Selecting data
In the data acquisition stage, the data are screened or processed in order to ensure data continuity. According to official document data of postal authorities of certain city, the number of express packages and the quality of express packages of certain city are combined for analysis:
table 1: express delivery business volume data (piece) of certain city from 2016, 1 month, 2022, 9 months
| Month/year | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
| 1 | 4406.01 | 4071.88 | 6547.78 | 9151.28 | 5526.25 | 13429.6 | 15769.13 |
| 2 | 2621.97 | 4584.82 | 3587.1 | 5526.25 | 1249.79 | 7436.64 | 11231.76 |
| 3 | 4303.6 | 5620.04 | 7024.72 | 9183.67 | 5872.14 | 12764.75 | 13916.04 |
| 4 | 4062.84 | 5243.61 | 6509.17 | 8494.96 | 8151.4 | 11976.64 | 13328.39 |
| 5 | 4205.99 | 5273.3 | 7415.76 | 9125.86 | 9011.59 | 12676.46 | 16022.76 |
| 6 | 4461.05 | 6024.49 | 7871.36 | 9889.19 | 10617.72 | 15277.88 | 16589.81 |
| 7 | 4243.85 | 5566.74 | 7627.15 | 8802.86 | 10093.77 | 13291.43 | 15410.76 |
| 8 | 4320.34 | 5653.47 | 7544.44 | 8606.43 | 10293.03 | 13268.59 | 15618.94 |
| 9 | 4923.42 | 6292.21 | 8577.17 | 9731.57 | 11960.28 | 13933.26 | 15745.55 |
| 10 | 4916.92 | 6060.63 | 8549.28 | 9746.9 | 11644.23 | 13585.24 | |
| 11 | 6421.98 | 9597.18 | 11420.44 | 13367.53 | 15131.55 | 17657.18 | |
| 12 | 5870.69 | 5870.59 | 9961.91 | 11364.91 | 12998.18 | 15115.11 |
(2) Predictive model
And establishing a gray prediction and BP neural network combined simulation for the annual and monthly time corresponding to the express traffic.
I Gray prediction
The main feature of gray prediction is that the model uses not the original data sequence but the generated data sequence. The core system is a gray Model (GM for short), namely a method for modeling after accumulating and generating (or generating by other methods) the original data to obtain an approximate exponential law. The method has the advantages that a lot of data are not needed, and generally only 4 data are needed, so that the problems of less historical data and low sequence integrity and reliability can be solved; the differential equation can be utilized to fully mine the essence of the system, and the precision is high. The irregular original data can be generated to obtain a generation sequence with stronger regularity, the operation is simple and convenient, the detection is easy, the distribution rule is not considered, and the change trend is not considered.
Known reference data column x (0) ＝(x (0) (1)，x (0) (2)，…x (0) (n)) and 1 time of accumulation to generate a sequence (1-AGO),
x (1) ＝(x (1) (1)，x (1) (2)，…x (1) (n))＝x (0) ＝(x (0) (1)，x (0) (1)+x (0) (2)，x (0) (1)+…+x (0) (n))
x (1) mean generation column z of (1) (1) ＝(z (1) (2)，z (1) (3)，…，z (1) (n))
z (1) (k)＝0.5x (1) (k)+0.5x (1) (k-1)，k＝2，3…n
Establishing an ash differential equation:
x (0) (k)+az (1) (k)＝b，k＝2，3，…n，
the corresponding whitening differential equation is:
model prediction step
1. Verification and processing of data
First, in order to ensure the feasibility of the modeling method, the necessary verification process for the known data columns is required. Let the reference number series be x (0) ＝(x (0) (1)，x (0) (2)，…x (0) (n)) calculating the step ratio of the sequences:
if all the pole ratios lambda (k) fall within the acceptable coverageIn, then sequence x (0) The gray prediction can be performed as data of the model GM (1, 1). Otherwise, it is necessary to pair the sequence x (0) Make necessary ratio conversionAnd the cover is covered by the sun.
I.e. taking proper mature c, making translation transformation
y (0) (k)＝x (0) (k)+c，k＝1，2…，n
Sequencing y (0) ＝(y (0) (1)，y (0) (2)，…y (0) Polar ratio of (n))
2. Modeling
The GM (1, 1) model is built from the whitened differential equation, representing a gray model with 1 st order differential equation and only 1 variable, the prediction formula is as follows:
k=0, 1, …, n-1, … 3. Check predictions
1) And (5) checking relative errors. Calculating relative error
Here, theIf delta (k) < 0.2, then the general requirement can be considered to be met; if delta (k) < 0.1, it is considered that higher requirements can be met.
2) Verification of stage ratio deviation values
First has a reference number series x (0) (k-1)，x (0) (k) Calculating the step ratio lambda (k) and then calculating the step ratio deviation of the response by using the development coefficient alpha
If |ρ (k) | < 0.2, then the general requirement is considered to be met; if |ρ (k) | < 0.1, then the higher requirement is considered to be met.
3) Prediction forecast
And obtaining a predicted value in a specified time zone by using a GM (1, 1) model, and giving a predicted forecast of response according to the requirements of actual problems.
In the embodiment, after the calculation, the relative error check and the level ratio deviation value check are used for judgment, and the express delivery business volume data function expression of 11 and 12 months in 2022 is obtained.
Predictive data expression for 11 months:
x(t)＝51503.3t+763752e -0.059t -757330
predicted data expression for 12 months:
x(t)＝40994e 0.15829t -618.239t-35123.3
model inspection
| Relative error | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
| 11 months of | 0 | 0.195 | 0.104 | 0.0573 | 0.0199 | 0.041 |
| 12 months of | 0 | 0.0923 | 0.235 | 0.205 | 0.178 | 0.165 |
The relative errors are less than 5%, and the prediction effect is good.
The partial codes are as follows:
/>
II BP neural network
The BP neural network is a multi-layer neural network with three or more layers, each layer is composed of a plurality of neurons, as shown in fig. 23, each neuron between the left layer and the right layer is fully connected, i.e. each neuron of the left layer is connected with each neuron of the right layer, and no connection exists between the upper neuron and the lower neuron. The BP neural network is trained in a learning mode of a teacher, and when a pair of learning modes are provided for the network, the activation values of neurons of the BP neural network are transmitted from an input layer to an output layer through hidden layers, and each neuron of the output layer outputs a network response corresponding to the input mode. Then, according to the principle of reducing the error between the expected output and the actual output, correcting each connection right layer by layer from the output layer through each hidden layer and finally returning to the input layer; since this correction process is performed layer by layer from the output to the input, it is called an "error back propagation algorithm". With the continuous correction of the error back propagation training, the accuracy of the network response to the input mode is also continuously improved.
(II) website and database technology implementation
Website construction is one of the requisite channels for enterprises to display images, products and services. Besides displaying enterprise images, the official network of the website is also a man-machine interaction interface for visually expressing information in the database. See fig. 24.
In order to solve the problem that the data manipulation language is boring and strict in the database field, a visual database system based on a graphical user interface forms a new direction for database development. With the rapid development of internet technology and big data, database-driven visualization degree has been rapidly developed and widely used. At present, more and more units and institutions acquire specific data information provided by a user interaction interface through a basic architecture, a database and N WEB interaction webpages facing user services and then page background scripts; through visualization, not only is the overall view shown, the understanding is enhanced, the dialogue, the exploration and the communication are facilitated, but also the complexity is simplified, and the examination is enhanced. Thus, the visualization of the database is a viable option and best choice that the user can accept. The development of the database is based on the statistical data that no express packaging garbage exists at present. The database is a visual database established by using a shiny package (web page and database preparation) of R language on the basis of combining research, model and data analysis. Compared with the internet+ data display form of the traditional statistics annual-image data mode, the method has the advantages that the information quantity is more abundant, the display form is more visual, the method is truly easy to read and understand, and the method is the integration of the internet+ and the statistics data information. See fig. 25.
The above embodiments are merely for illustrating the design concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, the scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications according to the principles and design ideas of the present invention are within the scope of the present invention.
Claims (9)
1. The visual database system of express packaging waste is characterized in that: the system comprises a user side, a database, an applet and a website;
the user end comprises a student end, a collector end, an university end and an enterprise end;
the student end and the collector end input data into the database through the applet of the mobile device; the data input by the student end are registration information comprising names, university information, academic numbers, mobile phone numbers and mailboxes, and order information of express delivery service, express pickup service and express garbage recovery service are reserved for each time; the data input by the collector terminal is order confirmation information comprising user information and recovered article information;
the enterprise end and the institution end access the data in the authority through the background of the website; the enterprise end is used for checking the recycling amount and proportion information of the garbage such as the cartons, the plastic bags, the foam and the like; the college end is used for checking information including student names, colleges, school numbers and garbage collection amount;
the applet comprises a registration page, a home page function page, a user reservation interface, a recovery function module, an integral presentation module and a statistical information module;
the registration page is used for a user to input registration information and login information, and jumps to the home page function page when the login information is consistent with the registration information;
the home page function page is used for a user to check reservation information and point rewards and guides the user to use corresponding services;
the user reservation interface is used for a user to input reservation information;
the recycling function module is used for inputting order related information comprising user information and recycling article information;
the point presenting module and the statistical information module are used for counting and managing user rewards;
the statistical information module is also used for counting the recovery amount of the waste received by the collector in a period of time;
the website comprises a homepage and a database visualization interface; the database visual interface is used for regularly displaying the statistics of the production quantity of the express package waste, the statistics of the recovery ratio of the express package and the statistics of the specification ratio of the express package.
2. The visual database system of packaging waste for express delivery of claim 1, wherein: the charge amount of the express delivery service and the express pickup service is determined by the user package quantity and the service route; the express garbage collection service is freely opened to users, and comprises off-line website service and on-line processing service.
3. The visual database system of packaging waste for express delivery of claim 1, wherein: the applet adopts a regional gridding technology to create a map instance and perform secondary development and deployment to realize the dot division in a target region; the applet is also used for assessment and compensation management of the contractors.
4. The visual database system of packaging waste for express delivery of claim 1, wherein: the database is based on the statistical data of the express packaging garbage which is not available at present, and based on the combination of research, model and data analysis, a visualized database is built by using a shiny package of R language.
5. A gridding management method based on the visual database system of express packaging waste according to any one of claims 1 to 4, characterized in that: the method comprises the following specific steps:
s1: the student end reserves the latest waste recovery point through the applet and enters a reservation interface to fill in the name, the mobile phone, the address and the reservation time; selecting the type of the recycled waste and the nearest recycling point, submitting a reservation, and finding the nearest site and the nearest collector by the system through a map and navigation service;
s2: the recycling agent side obtains the weight of the recycled objects through weighing and fills in the reservation list of the user to complete recycling; the applet sends a request for updating the reservation list and ending the reserved timing task to the background of the website, calculates the contribution value and the point of the user and presents the contribution value and the point;
s3: the background of the website records the total weight of the articles recovered by the receiver every day, and stores the data in a database; the front end of the website realizes a data visualization function through an open source frame Echart.js and canvas 2D drawing technology, and the data is visually displayed on a chart in the form of a recovery rate curve within one week, one month and one year;
s4: the enterprise terminal displays the distribution and the list of the cooperative schools and provides options for auditing the cooperative application of the schools or adding the cooperative schools; displaying all the collection points of the school and corresponding collector information, managing the collectors, and performing addition, deletion and correction on the information including daily collection amount of the collectors.
6. The meshing management method according to claim 5, characterized in that: in the step S1, the small program predicts the related data of the express package based on the gray model and the BP neural network, and comprises the following steps:
s11: selecting data; in order to ensure data continuity, screening or processing is carried out on the data in a data acquisition stage;
s12: a predictive model; establishing a gray prediction and BP neural network combined simulation for the annual month time corresponding to the express traffic;
known reference data column x (0) ＝(x (0) (1)，x (0) (2)，…x (0) (n)) and 1 time of accumulation to generate a sequence (1-AGO),
x (1) ＝(x (1) (1)，x (1) (2)，…x (1) (n))＝x (0) ＝(x (0) (1)，x (0) (1)+x (0) (2)，x (0) (1)+…+x (0) (n))
x (1) mean generation column z of (1) (1) ＝(z (1) (2)，z (1) (3)，…，z (1) (n))，
z (1) (k)＝0.5x (1) (k)+0.5x (1) (k-1)，k＝2，3…n
Establishing an ash differential equation:
x (0) (k)+az (1) (k)＝b，k＝2，3，…n，
the corresponding whitening differential equation is:
s13: checking and processing data; performing checking treatment on the known data columns to ensure the feasibility of the modeling method;
according to the reference number series x (0) ＝(x (0) (1)，x (0) (2)，…x (0) (n)) calculating the step ratio of the sequences:
if all the level ratios lambda (k) fall within the acceptable coverageIn, then sequence x (0) Gray prediction can be performed as data of the model GM (1, 1); otherwise, it is necessary to pair the sequence x (0) Performing comparison conversion treatment to make the ratio conversion treatment fall in the capacity coverage, namely taking a proper constant c to perform translation conversion:
y (0) (k)＝x (0) (k)+c，k＝1，2…，n
let sequence y (0) ＝(y (0) (1)，y (0) (2)，…y (0) The stage ratio of (n)) is:
s14: establishing a model; the GM (1, 1) model is built from the whitened differential equation, representing a gray model with 1 st order differential equation and only 1 variable, the prediction formula is as follows:
k＝0，1，…，n-1，…
s15: and (5) checking the predicted value.
7. The meshing management method according to claim 6, characterized in that: in the step S15, the inspection method includes:
s151: checking relative errors; is provided withCalculating relative error:
if delta (k) < 0.2, then the general requirement is considered to be met; if delta (k) < 0.1, then higher requirements are deemed to be met;
s152: step D, checking a level ratio deviation value; first by reference number series x (0) (k-1)、x (0) (k) Calculating a step ratio lambda (k), and then solving a step ratio deviation of response by using a development coefficient alpha:
if |ρ (k) | < 0.2, then the general requirement is considered to be met; if |ρ (k) | < 0.1, then higher requirements are considered to be met;
s153: predicting and forecasting; and obtaining a predicted value in a specified time zone by using a GM (1, 1) model, and giving a predicted forecast of response according to the requirements of actual problems.
8. The meshing management method according to claim 6, characterized in that: the BP neural network is trained according to a study mode of a teacher, and the specific steps are as follows:
when a pair of learning modes are provided for a network, the activation values of neurons of the learning modes are transmitted from an input layer to an output layer through each hidden layer, and each neuron in the output layer outputs a network response corresponding to the input mode;
adopting an error back propagation algorithm, and correcting each connection weight layer by layer from an output layer through each hidden layer and finally returning to an input layer according to the principle of reducing the error between expected output and actual output;
with the continuous correction of the error back propagation training, the accuracy of the network response to the input mode is continuously improved.
9. A computer storage medium, characterized by: a computer program executable by a computer processor, the computer program executing the gridding management method according to any one of claims 5 to 8.
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