CN112529245A - Street domestic waste collection and transportation quantity prediction method coupled with multi-source big data - Google Patents
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Abstract
The invention discloses a street domestic waste collection and transportation quantity prediction method coupled with multi-source big data, which comprises the following steps: connecting a street map in the area with a geographical coordinate map of the garbage transfer station, matching the information of the street to which each garbage transfer station belongs, performing superposition analysis on the base area data of various land buildings in the area and the street boundary map, and calculating the building density of each street; obtaining the population density of each street according to the population density data, the room price data and the social consumer goods retail total of each street in the area; carrying out normalization processing on the building area, height data, building density and population density of each street; inputting the normalized data into a pre-constructed and trained BP neural network; the invention realizes the accurate prediction of garbage collection and transportation amount in communities and streets.
Description
Technical Field
The invention relates to the technical field of garbage collection and transportation quantity prediction, in particular to a street household garbage collection and transportation quantity prediction method coupled with multi-source big data.
Background
The methods mainly applied to domestic and foreign research in domestic garbage prediction can be roughly divided into two categories: the first method mainly uses economic and cultural factors as indexes as independent variables, such as per-capita income level, total regional production value, room price, education level and the like, and the applied analysis model mainly comprises a multivariate regression analysis model, a per-capita garbage production prediction method, an artificial neural network model, a system dynamics model and the like; the second method is to obtain historical data of the domestic garbage amount of a research object and deduce the future garbage yield by comparative analysis of the historical data by using a mathematical method, and mainly comprises a grey model, a time series model and the like. The research mainly takes the garbage amount of cities and regions as a prediction object, and utilizes large-scale data such as the total value of national economic production, per capita consumption expenditure, the urbanization rate and the like to predict and analyze the garbage generation amount. The research in a large scale range can reflect the generation condition of macroscopic domestic garbage, and has guiding significance for the capacity design and building planning of large garbage disposal plants and landfill sites. However, due to the difference of the area of each street and community and the difference of the number of residents, the difference of the garbage collection and transportation demands is caused, and the existing large-scale range research cannot reflect the difference, so that the reference is difficult to be provided for the design of garbage collection and transportation and treatment facilities in a small-scale range. With the popularization of classification policy for living and the improvement of collection and transportation policy, compared with the prior mode that all streets are collected and transported in a centralized manner and then transported to a landfill or a treatment plant, the garbage collection and transportation policy at the present stage is more inclined to consider that the garbage amount generated in the streets or the combination of a plurality of streets is collected and transported in a centralized manner to a small garbage treatment center in the streets for disposal, and all places are more actively developing the reform of 'kitchen garbage can not go out of the community', and the kitchen garbage is required to be directly collected, transported and centrally treated in the streets so as to reduce the pressure of the large garbage treatment plant and the landfill. The existing garbage disposal measures put higher requirements on the garbage quantity prediction precision in a small-scale research range, but the existing method for predicting the domestic garbage production quantity in a small-scale range is relatively lack, and the capacity and layout of garbage classification and disposal facilities in communities and streets and relevant garbage disposal policies are difficult to design according to scientific prediction results.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a street domestic garbage collection and transportation quantity prediction method which can accurately predict the actual conditions of garbage collection, transportation and treatment in communities and streets and is coupled with multi-source big data.
The purpose of the invention is realized by the following technical scheme:
a street domestic waste collection and transportation quantity prediction method coupled with multi-source big data comprises the following steps:
s1, connecting the street map in the area with the geographic coordinate map of the garbage transfer station, matching the street information of each garbage transfer station, and performing visualization processing;
s2, performing superposition analysis on the base area data of various land buildings in the area and the street boundary map to obtain the street to which each plot or building belongs, counting the building area and height data of each street, and calculating the building density of each street;
s3, obtaining the population density of each street according to the population density data, the room price data and the retail total amount of the social consumer goods of each street in the area;
s4, carrying out normalization processing on the building area, height data, building density and population density of each street;
s5, inputting the normalized data into a pre-constructed and trained BP neural network;
and S6, the BP neural network outputs the average daily throughput of the garbage transfer station.
Preferably, the formula for calculating the building density of each street is: the building density is the total area of the street building, the occupied area of the street is multiplied by 100 percent, and the building density is the street building density.
Preferably, the normalization process is formulated as:
x＝(x-Min)/(Max-Min)。
preferably, in step S5, 8 prediction variables in the input layer of the BP neural network are input nodes, the output layer nodes are average daily throughput of the garbage transfer station, and there are 2 hidden nodes.
Preferably, if the accuracy of the average daily throughput of the BP neural network output garbage transfer station is lower than the preset threshold, performing accuracy enhancement operation on the existing BP neural network again, specifically: continuously training the existing BP neural network in the neural network analysis in the SPSS Modeler, and selecting the training purpose to improve the accuracy of the model; the target method can continuously generate a plurality of 'component models' to be compared with the existing models, weights the variable according to the residual error of the former model, gives analysis weight according to the principle that whether the variable with larger residual error value is used as the principle, the larger the residual error is, the higher the weight is given, and finally integrates all the component models to form a new model with higher accuracy.
Compared with the prior art, the invention has the following advantages:
according to the method, a small-scale research object is used for coupling multi-source big data in communities and streets, and the multi-source big data is input into a pre-constructed and trained BP neural network; the BP neural network outputs the average daily throughput of the garbage transfer station, and the accurate prediction of garbage collection and transportation in communities and streets is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate exemplary embodiments of the invention and, together with the description, serve to explain the invention and are not intended to limit the invention. In the drawings:
fig. 1 is a flow chart of the street domestic waste collection and transportation quantity prediction method coupled with the multi-source big data according to the embodiment.
Fig. 2 is a distribution diagram of the position of the waste transfer station in the river region according to the embodiment.
Fig. 3 is a diagram illustrating a daily average garbage disposal amount prediction result of the BP neural network of the present embodiment for the garbage transfer station in the river region.
Detailed Description
The invention is further illustrated by the following figures and examples.
The street domestic garbage collection and transportation quantity prediction method coupled with the multi-source big data is taken as an example in the Tian river district of Guangzhou city, the multi-source big data is emphatically utilized to predict the garbage production quantity, and a big data processing and application framework of garbage processing dimension is discussed, so that the prediction result is more comprehensive and specific. Meanwhile, based on small-scale research objects, and prediction research is carried out according to the actual situation of garbage collection, transportation and treatment in communities and streets, referring to fig. 1, the method for predicting the garbage collection and transportation amount in communities and streets by providing theoretical reference to the layout design of garbage transfer stations and treatment facilities in streets and streets specifically comprises the following steps:
(1) in order to obtain the overall situation of regional garbage amount, the position and daily average handling capacity of each garbage transfer station in the sky river region are obtained through a government information public website, a space connection tool (analysis Tools, Overlay, Spatial Join) of Arcgis is used for connecting a street map of the sky river region with a geographical coordinate map of the garbage transfer station, the information of the street to which each garbage transfer station belongs is matched for each garbage transfer station, and visualization processing is performed, and the result is shown in fig. 2 and table 1.
TABLE 1 information table of garbage transfer station in the river area
(2) Considering the influence of the total building height, the building density, the total residential area, the total office and school land area, the total commercial land area, the total weekday population density, the total weekend population density, the average house price and the total social consumer retail sales on the garbage production amount, relevant data of each street are obtained and subjected to data visualization processing, as shown in tables 2 and 3.
TABLE 2 garbage generation and related factor data for each street in the river
TABLE 3 garbage generation and related factor data of each street in the river
(21) Building height data of a river area, AOI data of buildings in the river area and base area data of various land buildings in the river area are subjected to superposition analysis by utilizing space connection Tools (analysis Tools, Overlay, Spatial Join) of Arcgis and a street boundary diagram of the river area to obtain the building area and height data of each street or the street to which the building belongs, and then the building area and height data of each street are counted by utilizing a superposition analysis method (Spatial analysis Tools, Zonal Statistics) of the Arcgis. The following formula is used to calculate the building density of each street.
The results of building density ÷ total area of street building ÷ street floor area × 100% ═ street building density are shown in table 2.
(22) Acquiring 2015 Guangzhou city region thermal grid map from Tengcong position big data in an Tengcong map open platform as statistical data of population density. The room price data of the Guangzhou city sky-river area adopts the room price grid map data of the sky-river area 2015, and the total sale amount of the social consumer goods of each street of the Guangzhou city sky-river area 2015 is obtained by applying for data disclosure on the statistics bureau website of the Guangzhou city sky-river area. The population density of each street was derived using the spatial connectivity tool and overlay analysis described above. The street is used as the research scale in the patent, and because the research scope is less, compared with the data in the government statistical yearbook, the population density of each street in the river area can be reflected more truly by using the regional thermodynamic diagram. In addition, in the study, the thermodynamic data are divided into two different time periods of working days and weekends, statistics of general population of each street is carried out, and the influence of population density on the garbage amount in different degrees in different time periods can be better reflected, as shown in table 3.
(3) The method selects a three-layer BP neural network to predict the garbage amount in the river region, the number of nodes of an input layer is 8 listed above factors influencing the garbage generation amount, and the specific prediction steps are as follows:
data normalization processing: because the data is subjected to predictive analysis by using the multilayer perceptron, the data needs to be standardized before BP neural network analysis, the normalization processing is a way of data standardization processing, the purpose of the processing method is to convert data with different orders of magnitude into numbers between [0,1], and the generation of large errors of network prediction caused by overlarge differences of the orders of magnitude of the data can be prevented. The processing formula is as follows:
x＝(x-Min)/(Max-Min)
x is the acquired data.
And analyzing the normalized data by using an SPSS Modeler. The data are processed by adopting an MLP (multi-layer perceptron) network structure, 8 prediction variables in an input layer are input nodes, nodes in an output layer are average daily throughput of the garbage transfer station, 2 hidden nodes are provided, in order to prevent model overfitting, a stopping rule is set to be maximum training time of 15 minutes, and parameters for preventing overfitting are 25%.
Thirdly, after the result is output, if the accuracy rate of the result is lower than 90%, the accuracy enhancement operation is carried out on the existing model again. The existing model function is continuously trained in the SPSS Modeler internal neural network analysis, and the training purpose is selected to improve the accuracy of the model. The target method can continuously generate a plurality of 'component models' to be compared with the existing models, weights the variable according to the residual error of the former model, gives analysis weight according to the principle that whether the variable with larger residual error value is used as the principle, the larger the residual error is, the higher the weight is given, and finally integrates all the component models to form a new model with higher accuracy. The analysis results are shown in fig. 3, table 4 and table 4.
TABLE 5 model prediction results
TABLE 6 analysis of model accuracy
In summary, the embodiment of the invention provides a method for analyzing the receiving and transporting capacity of a garbage transfer station, which is coupled with multi-source big data and GIS space analysis. The method comprises the following steps: considering the influence of economic factors, building factors and population factors on the garbage collection and transportation quantity; the street is taken as a research scale, the method conforms to the updating process of the garbage collection and transportation mode, and provides reference for the design of garbage collection and transportation facilities in the future; extracting specific data of each factor influencing the garbage collection and transportation quantity by utilizing multisource data such as population thermodynamic diagram data, AOI data, building height data, social consumer goods retail gross data and the like, and adding geographic elements to the data by utilizing a GIS (geographic information System) space analysis method to obtain specific data of each influencing factor of each street; and (4) carrying out weight analysis and garbage collection and transportation quantity prediction on each influence factor by using the BP neural network.
From the analysis result of the BP neural network, the main factors influencing the generation of the garbage amount are population factors and residential land area factors, the generation of the garbage amount is greatly influenced by the population number because the main body generating the urban household garbage is a human, and the residential land area factors also obtain higher weight because the source of the household garbage is mainly a family. In the waste collection and transportation layout of Guangzhou city, the treatment system of the kitchen waste is not overlapped with the household waste, and the kitchen waste generated in the catering facilities and the dining room facilities of the commercial land and the office land does not flow into the household waste transfer station, so that the area of the commercial land and the office land has a weak influence on the waste amount. In the aspect of economic factors, the importance of the house price factor is 0.09, which is obviously higher than the retail total amount of the social consumer goods, because the house price is more closely related to the residential land factor, and the quantity and consumption level of residents in cities have great influence on the generation of the garbage amount.
In addition, the average absolute error of the prediction result is 7.272, the maximum relative error is 46.02%, the prediction accuracy reaches 93%, and the method shows that the relation between nonlinear variables such as influence factors of urban domestic garbage analysis and daily average treatment capacity of the garbage transfer station is more applicable to the neural network model, so that more reasonable prediction data can be obtained, and the method can be used for subsequent policy implementation, facility planning and personnel allocation. In summary, the present study considers that when the urban domestic garbage amount is predicted in a small scale range, variables related to population density and residential area can be selected and analyzed by using a BP neural network analysis method.
The above-mentioned embodiments are preferred embodiments of the present invention, and the present invention is not limited thereto, and any other modifications or equivalent substitutions that do not depart from the technical spirit of the present invention are included in the scope of the present invention.
Claims (5)
1. A street domestic waste collection and transportation quantity prediction method coupled with multi-source big data is characterized by comprising the following steps:
s1, connecting the street map in the area with the geographic coordinate map of the garbage transfer station, matching the street information of each garbage transfer station, and performing visualization processing;
s2, performing superposition analysis on the base area data of various land buildings in the area and the street boundary map to obtain streets to which each land or building belongs, counting building area and height data of each street, and calculating the building density of each street;
s3, obtaining the population density of each street according to the population density data, the room price data and the retail total amount of the social consumer goods of each street in the area;
s4, carrying out normalization processing on the building area, height data, building density and population density of each street;
s5, inputting the normalized data into a pre-constructed and trained BP neural network;
and S6, the BP neural network outputs the average daily throughput of the garbage transfer station.
2. The method for predicting street domestic waste collection and transportation quantity coupled with multi-source big data according to claim 1, wherein the formula for calculating the building density of each street is as follows: the building density is the total area of the street building, the occupied area of the street is multiplied by 100 percent, and the building density is the street building density.
3. The method for predicting street domestic waste collection and transportation quantity coupled with multi-source big data according to claim 1, wherein the formula of the normalization process is as follows:
x＝(x-Min)/(Max-Min)。
4. the method for predicting street garbage collection and transportation amount coupled with multi-source big data according to claim 1, wherein 8 prediction variables in the input layer of the BP neural network in step S5 are input nodes, the output layer nodes are average daily throughput of the garbage transfer station, and there are 2 hidden nodes.
5. The method for predicting the street domestic garbage collection and transportation quantity coupled with the multi-source big data according to claim 1, wherein if the accuracy of the average daily throughput of the BP neural network output garbage transfer station is lower than a preset threshold, the accuracy enhancement operation is performed on the existing BP neural network again, specifically:
continuously training the existing BP neural network in the neural network analysis in the SPSS Modeler, and selecting the training purpose to improve the accuracy of the model; the target method can continuously generate a plurality of 'component models' to be compared with the existing models, weights the variable according to the residual error of the former model, gives analysis weight according to the principle that whether the variable with larger residual error value is used as the principle, the larger the residual error is, the higher the weight is, and finally integrates all the component models to form a new model with higher accuracy.
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| CN117830062A (en) * | 2024-03-05 | 2024-04-05 | 天津市城市规划设计研究总院有限公司 | Household garbage collection and transportation system planning method based on full life cycle carbon emission accounting |
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