CN110175868B - Green travel incentive system and method based on carbon transaction - Google Patents
Green travel incentive system and method based on carbon transaction Download PDFInfo
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- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 88
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 87
- 238000000034 method Methods 0.000 title claims abstract description 30
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0208—Trade or exchange of goods or services in exchange for incentives or rewards
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0226—Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
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- G06Q50/40—
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
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Abstract
The invention provides a green travel incentive system and a method based on carbon transaction, and the system comprises: the system comprises a mobile terminal, a Wi-Fi probe, a vehicle-mounted GPS module, a vehicle-mounted speed sensor, a vehicle-mounted time module, a vehicle-mounted Wi-Fi module, a first wireless communication module, a machine learning server, a second wireless communication module and a user excitation server. The mobile terminal collects user data and transmits the data to the machine learning server; the vehicle-mounted GPS module and the Wi-Fi probe record vehicle travel data and a terminal MAC address; the vehicle-mounted Wi-Fi module uploads the data to a machine learning server; the machine learning server processes and matches the data, predicts a user travel mode by using BP neural network training data, constructs incentive data and transmits the incentive data to the user incentive server; the user motivation server calculates and stores subsidy prices; the mobile terminal is communicated with the user incentive server through the third wireless communication module and subsidies according to the carbon trading price. And the mobile terminal periodically synchronizes the block chain account book.
Description
Technical Field
The invention relates to the technical field of carbon emission management, in particular to a green travel incentive system and a green travel incentive method based on carbon trading.
Background
Since the book of the kyoto protocol, more and more countries adopt market mechanisms to control the emission of greenhouse gases, and the carbon dioxide emission right is used as a commodity in the unit of carbon dioxide equivalent per ton, so that the carbon transaction, which is the transaction of the carbon dioxide emission right, is formed. At present, domestic carbon transactions can be divided into two types, one type is that enterprises of a certain magnitude achieve emission reduction targets by purchasing emission rights, and the other type is that individual investors invest in market to obtain benefits. The two modes promote the prosperity of the carbon trading market, promote the huge energy-saving and emission-reducing behaviors of large enterprises and the like macroscopically, but do not go deep into the daily life of residents, and cannot fully exploit the huge potential of energy conservation and emission reduction contained in daily trips of the residents. The carbon transaction is used for exciting the green trip of the passenger, the environmental protection consciousness of the resident can be improved, the sustainable development idea is deep, the carbon emission limit is donated to the welfare mechanism, the overall happiness of the resident can be effectively improved, the strategic planning of the national modernization construction is met, the greenhouse gas generated in the trip process can be effectively reduced, and the great contribution is made for relieving the greenhouse effect.
In order to ensure that data for stimulating green travel is real and credible, the method also uses a block chain technology, a plurality of servers, mobile terminals, base stations and the like are regarded as different nodes in a block chain network, and the data of users and vehicles are matched, so that the reliability of the data is ensured; by using the ideas of distributed accounting and data encryption, the transaction process is ensured to be public and transparent and easy to trace, and meanwhile, the whole network is ensured to have stronger robustness, and once the data is uploaded, the data is difficult to be tampered.
In addition, in order to record travel data conveniently, machine learning (such as an artificial neural network, a Bayesian network and the like) is used for training the big data, and when the training precision reaches a threshold value, the travel terminal, the travel time, the travel mode and the like of the passenger can be automatically identified, so that the operation steps of the user are greatly simplified.
Disclosure of Invention
In order to solve the problems, the invention provides a green travel incentive system and a green travel incentive method based on carbon trading.
The technical scheme of the system is a green travel incentive system based on carbon transaction, which is characterized by comprising the following steps: the system comprises a mobile terminal, a Wi-Fi probe, a vehicle-mounted GPS module, a vehicle-mounted speed sensor, a vehicle-mounted time module, a vehicle-mounted Wi-Fi module, a first wireless communication module, a machine learning server, a second wireless communication module and a user excitation server;
the mobile terminal is connected with the Wi-Fi probe in a wireless communication mode; the vehicle-mounted Wi-Fi module is respectively connected with the Wi-Fi probe, the vehicle-mounted GPS module, the vehicle-mounted speed sensor and the vehicle-mounted time module in sequence through leads; the mobile terminal is connected with the first wireless communication module in a wireless communication mode; the vehicle-mounted Wi-Fi module is connected with the first wireless communication module in a wireless communication mode; the first wireless communication module is connected with the machine learning server through a wire; the first wireless communication module is connected with the second wireless communication module in a wireless communication mode; the second wireless communication module is connected with the user excitation server through a wire; the second wireless communication module is connected with the mobile terminal in a wireless communication mode. The technical scheme of the invention is a green trip incentive method based on carbon transaction, which is characterized by comprising the following steps:
step 1: the mobile terminal collects a user data set consisting of a user MAC address and daily activity track data and transmits the user data set to the machine learning server in a wireless mode;
and 2, step: the vehicle-mounted GPS module records a vehicle data set, namely vehicle travel time, vehicle position, vehicle speed and vehicle information, and the Wi-Fi probe acquires a mobile phone MAC address of a user;
and 3, step 3: the vehicle-mounted Wi-Fi module uploads data to a machine learning server;
and 4, step 4: the machine learning server calculates the row distance by using the collected user data;
and 5: the machine learning server sequentially matches the user data set, the vehicle data set and the user travel distance to screen qualified data for data storage;
and 6: the machine learning server carries out BP neural network model training on training set data, the training set is input into a neural network for repeated training until the deviation and variance of the data meet requirements, and the data for each training is all trip data in days of a certain week;
and 7: the machine learning server puts the trained neural network model into application, predicts a trip mode and a trip mode of a user, constructs user incentive data and transmits the user incentive data to the user incentive server;
and 8: the user incentive server calculates and stores carbon emission generated by travel and corresponding carbon transaction price or point according to user incentive data;
and step 9: the mobile terminal is communicated with the user incentive server periodically through the third wireless communication module, part of the carbon transaction price is subsidized to be the fare for the next trip, or a certain amount of points are deducted to give rewards, and the user can also donate the rewards to a welfare agency; and the mobile terminals are communicated with each other regularly, and the block chain account book is synchronized.
Preferably, in step 1, the mobile terminal queries device information to obtain an MAC address, and obtains daily activity track data through a terminal built-in positioning module;
in the step 1, the data set at the time t in the ith trip of the user is as follows:
wherein i is e {1,2,3, …, N }
ID is the MAC address of the user;
T i the travel time of the ith trip for the user;
preferably, the vehicle-mounted GPS module records vehicle information, vehicle speed and vehicle position in the step 2; acquiring the current moment through a vehicle-mounted time module; the Wi-Fi probe scans the mobile terminal equipment in the vehicle at a certain frequency, and the MAC addresses of all the terminal equipment in the vehicle are obtained at regular time;
in the step 2, the data set at the time t in the jth trip of the vehicle is as follows:
wherein j is from {1,2,3, …, N * }
I c The vehicle information, namely the type, the number of the nuclear people and the like of the vehicle C;
preferably, in step 3, the vehicle-mounted Wi-Fi module uploads a vehicle data set C to the machine learning server through the first wireless communication module in a wireless communication mode;
preferably, in step 4, the machine learning server calculates the row distance by using the collected user data as follows:
the ith travel distance L of the user i The calculation method is as follows:
k is the total time number in one trip;
preferably, the matching conditions in step 5 are:
will be provided with
Intermediate ID and
in
Carrying out MAC address matching;
distance L for user to travel i Performing distance matching, wherein H is a distance threshold, and judging whether L is i H m;
counting the data meeting the 3 conditions, giving a quantity threshold value beta, and if the quantity of the data meeting the conditions is not less than beta, considering the data set
And (5) according with the requirements, keeping the training set, and otherwise, discarding.
Organized in the order of the week's serial number on the same day of each week for a period of time. For the user on day D every week, D belongs to the time data set of {1,2, …,7}, the ith trip t, t belongs to the time data set of {1,2, …, K }
Repeating matching to finally obtain N effective sets A as training set data;
the stored data in step 5 are as follows:
wherein, ID R A MAC address for the user;
I c R the vehicle information is the type and the number of the persons carrying the vehicle C;
T i R the travel time of the ith trip for the user;
preferably, the BP neural network model in step 6 is:
the input layer neuron number S is 5, i.e. the travel time T i R And travelling speed
Distance of travel
Trip starting point
Vehicle type I c R And the number of samples is D, i trips, and the data of K trips at each time are all collected. The weights are respectively
The output layer neuron number O is 4, namely the trip end point B i Time to get on the bus C i When getting off the vehicleIn a room D i And travel mode E i The weights are respectively
The number of neurons in the hidden layer is 4, and the method is calculated by experience
Obtaining;
the error function model is
Wherein t is n To the desired output u n Is the actual output;
calculating by using a gradient descent method;
feedback is carried out according to output, neuron weights of an input layer, an output layer and a hidden layer are adjusted, and training is repeated until the prediction accuracy rate is larger than delta;
preferably, the step 7 of putting the trained neural network model into application includes:
identifying a trip mode and a trip mode of a user;
the user motivation data in step 7 is as follows:
P i ＝{A i ,B i ,C i ,D i ,E i ,F i }，i∈[1,N]
wherein N is the number of users, A i As coordinates of starting point of trip, B i Is a travel end point coordinate; c i For boarding time, D i For alighting time, E i For the trip mode, F i The travel speed is;
preferably, the formula for calculating the carbon emission generated by the trip in step 8 is as follows:
L i ＝Σ j，k，t [T·V·G j，k，t ·H j，k，t ]
wherein:
t is the running time of the vehicle;
v is the running speed of the vehicle;
G j,k,t l/100km for fuel consumption;
H j,k,t carbon emission factor for different fuels;
j is a vehicle type, such as a car, bus;
k is a fuel type such as raw coal, gasoline, diesel, kerosene, natural gas;
t is road type, such as city, country, default as city road when lacking data;
the transaction price of each ton of carbon dioxide equivalent can refer to the transaction price of the current market, and under the condition that the market is not mature, the carbon emission generated by each trip of a passenger can be converted into points by adopting a point system and then the points are counted into an account, and the cost of one-time riding is saved by using a certain point and the like as rewards;
the formula for calculating the carbon trade price or point is as follows:
wherein:
L car carbon emission generated by the shortest path of the driving starting point and the driving ending point of the average car type car;
L real carbon emission actually generated in the user traveling process;
gamma is a factor converted into a carbon transaction price or point;
h is the carbon emission factor of the motor gasoline;
when the user walks and rides a bicycle, carbon emission is not generated, the maximum value is taken under the condition, and the price subsidy or the point given to the user is the largest;
when the user travels by car, the carbon emission is the most, and the carbon trading price or point is 0.
The invention has the advantages of popularizing carbon transaction, enhancing the low-carbon environmental-protection awareness of resident trip, encouraging the resident to take public transport and relieving traffic jam.
Drawings
FIG. 1: the invention is a system framework;
FIG. 2: is the process of the method;
FIG. 3: the block chain block schematic of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the system technical solution of the present invention is a carbon transaction-based green travel incentive system, which is characterized by comprising: the system comprises a mobile terminal, a Wi-Fi probe, a vehicle-mounted GPS module, a vehicle-mounted speed sensor, a vehicle-mounted time module, a vehicle-mounted Wi-Fi module, a first wireless communication module, a machine learning server, a second wireless communication module and a user excitation server;
the mobile terminal is connected with the Wi-Fi probe in a wireless communication mode; the vehicle-mounted Wi-Fi module is respectively connected with the Wi-Fi probe, the vehicle-mounted GPS module, the vehicle-mounted speed sensor and the vehicle-mounted time module in sequence through leads; the mobile terminal is connected with the first wireless communication module in a wireless communication mode; the vehicle-mounted Wi-Fi module is connected with the first wireless communication module in a wireless communication mode; the first wireless communication module is connected with the machine learning server through a wire; the first wireless communication module is connected with the second wireless communication module in a wireless communication mode; the second wireless communication module is connected with the user excitation server through a wire; the second wireless communication module is connected with the mobile terminal in a wireless communication mode.
The mobile terminal is selected as a smart phone; the Wi-Fi probe is selected to be WP302E; the type of the vehicle-mounted GPS module is TR130; the type of the vehicle-mounted speed sensor is XHF-F168; the vehicle-mounted time module is selected to be RX-8731LC; the type selection of the vehicle-mounted Wi-Fi module is MU960C; the first wireless communication module is selected to be S7-200CN; the machine learning server is selected to be EMC PowerEdge R740; the second wireless communication module is selected to be WM-232SM; the user incentive server is selected as SPARCM12;
the following describes the embodiments of the present invention with reference to fig. 1 to 3: a green travel incentive system and method based on carbon trading are characterized by comprising the following steps:
step 1: the mobile terminal collects a user data set consisting of a user MAC address and daily activity track data and transmits the user data set to the machine learning server in a wireless mode;
in the step 1, the mobile terminal inquires equipment information to obtain an MAC address, and daily activity track data is obtained through a built-in terminal positioning module;
in step 1, the data set at the time t in the ith trip of the user is as follows:
wherein i is e {1,2,3, …, N }
ID is the MAC address of the user;
T i the travel time of the ith trip for the user;
step 2: the vehicle-mounted GPS module records a vehicle data set, namely vehicle travel time, vehicle position, vehicle speed and vehicle information, and the Wi-Fi probe acquires a mobile phone MAC address of a user;
in the step 2, the vehicle-mounted GPS module records vehicle information, vehicle speed and vehicle position; acquiring the current moment through a vehicle-mounted time module; the Wi-Fi probe scans the mobile terminal equipment in the vehicle at a certain frequency and regularly obtains the MAC addresses of all the terminal equipment in the vehicle;
in the step 2, the data set at the time t in the jth trip of the vehicle is as follows:
wherein j is from {1,2,3, …, N * }
I c The vehicle information, namely the type, the number of the nuclear people and the like of the vehicle C;
and step 3: the vehicle-mounted Wi-Fi module uploads data to a machine learning server;
in the step 3, the vehicle-mounted Wi-Fi module uploads a vehicle data set C to the machine learning server through the first wireless communication module in a wireless communication mode;
and 4, step 4: the machine learning server calculates the row distance by using the collected user data;
in step 4, the machine learning server calculates the row distance by using the collected user data as follows:
the ith travel distance L of the user i The calculation method is as follows:
k is the total time number in one trip;
and 5: the machine learning server sequentially matches the user data set, the vehicle data set and the user travel distance to screen qualified data for data storage;
the matching conditions in step 5 are as follows:
will be provided with
Intermediate ID and
in
Carrying out MAC address matching;
distance L for user to travel i Performing distance matching, wherein H is a distance threshold, and judging whether L is i < H =500 m;
counting the data meeting the 3 conditions, giving a quantity threshold value beta, and if the quantity of the data meeting the conditions is not less than beta, considering the data set
And (5) according with the requirements, keeping the training set, and otherwise, discarding.
Organized in the order of the week's serial number on the same day of each week for a period of time. For the user on day D every week, D belongs to the time data set of {1,2, …,7}, the ith trip t, t belongs to the time data set of {1,2, …, K }
Repeating matching to finally obtain N effective sets A as training set data;
the stored data in step 5 are as follows:
wherein, ID R A MAC address for the user;
I c R the vehicle information is the type and the number of the persons carrying the vehicle C;
T i R the travel time of the ith trip for the user;
step 6: the machine learning server carries out BP neural network model training on training set data, the training set is input into a neural network for repeated training until the deviation and variance of the data meet requirements, and the data of each training is all trip data in days of a certain week;
the BP neural network model in the step 6 is as follows:
the input layer neuron number S is 5, i.e. the travel time T i R Travel speed
Distance of travel
Trip starting point
Vehicle type I c R And the sample number is D, i trips and the totality of K trip data at each time. The weights are respectively
The output layer neuron number O is 4, namely the trip end point B i Time to get on the bus C i Get-off time D i And travel mode E i The weights are respectively
The number of neurons in the hidden layer is 4, and the method is calculated by experience
Obtaining;
the error function model is
Wherein t is n To the desired output u n Is the actual output;
calculating by using a gradient descent method;
feedback is carried out according to the output, neuron weights of an input layer, an output layer and a hidden layer are adjusted, and training is repeated until the prediction accuracy rate is larger than delta;
and 7: the machine learning server puts the trained neural network model into application, predicts a trip mode and a trip mode of a user, constructs user incentive data and transmits the user incentive data to the user incentive server;
the step 7 of putting the trained neural network model into application is as follows:
identifying a trip mode and a trip mode of a user;
the user motivation data in step 7 is as follows:
P i ＝{A i ,B i ,C i ,D i ,E i ,F i }，i∈[1,N]
wherein N is the number of users, A i As coordinates of starting point of trip, B i Is a travel end point coordinate; c i For getting on, D i For alighting time, E i For the trip mode, F i The travel speed is;
and 8: the user incentive server calculates and stores carbon emission generated by travel and corresponding carbon transaction price or point according to user incentive data;
the formula for calculating the carbon emission generated by trip in step 8 is as follows:
L i ＝∑ j，k，t [T·V·G j，k，t ·H j，k，t ]
wherein:
t is the running time of the vehicle, and in the example, 0.5h is taken;
v is the running speed of the vehicle, and in the example, 40km/h is taken;
G j,k,t taking 12L/100km in the example as fuel consumption L/100km;
H j,k,t the carbon emission factor of different fuels is 43070kJ/kg of fuel oil in the example;
j is a vehicle type, such as a car and a bus, in this example, the car is taken;
k is a fuel type, such as raw coal, gasoline, diesel oil, kerosene and natural gas, in this case gasoline is taken;
t is road type, such as city, countryside, default to city road when lacking data, in this example take city;
selecting the vehicle gasoline as a default for the fuel type k, and adopting the value recommended in an IPCC2006 annual report as a default for the carbon emission factors of different fuels;
the transaction price of each ton of carbon dioxide equivalent can refer to the transaction price of the current market, and under the condition that the market is not mature, the carbon emission generated by each trip of a passenger can be converted into points by adopting a point system and then the points are counted into an account, and the cost of one-time riding is saved by using a certain point and the like as rewards;
the formula for calculating the carbon trade price or point is as follows:
wherein:
L car carbon emission generated by the shortest path of the driving starting point and the driving ending point of the average car type car;
L real carbon emission actually generated in the user trip process;
gamma is a factor converted into carbon transaction price or point, and is taken as 0.5;
h is a carbon emission factor of the motor gasoline, and 43070kJ/kg is taken;
the gasoline density is 0.75kg/L,1kg of carbon is completely combusted to release 32645.83kJ heat.
The saved carbon emission amount calculated by the embodiment is 762.77kg, and if the carbon trading price of the current trip is 30 yuan/ton and the carbon trading price factor is 0.5, the carbon trading price subsidized by the current trip is about 11 yuan.
When the user walks or rides a bicycle, carbon emission is not generated, and the maximum value is taken under the condition that the price subsidy or the point given to the user is the most;
when the user travels by taking a car, the generated carbon emission is the largest, and the carbon trading price or point is 0;
when the passenger gets on a vehicle driven by other energy sources, the corresponding carbon emission can be calculated according to the energy source type, for example, the rail transit can be calculated according to thermal power, and the average power consumed by each passenger for travelling each time is converted into the carbon emission generated by the thermal power.
And step 9: the mobile terminal is communicated with the user incentive server periodically through the third wireless communication module, part of the carbon transaction price is subsidized to be the fare for the next trip, or a certain amount of points are deducted to give rewards, and the user can also donate the rewards to a welfare agency; and the mobile terminals are communicated with each other at regular intervals, and block chain accounts are synchronized.
Although the present description makes greater use of terms such as mobile terminal, wi-Fi probe, onboard GPS module, onboard speed sensor, onboard time module, onboard Wi-Fi module, first wireless communication module, machine learning server, second wireless communication module, user motivation server, etc., the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe the nature of the invention and they are to be construed as any additional limitation which is not in accordance with the spirit of the invention.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given in some detail, and not to be taken as limiting the scope of the invention, and that those skilled in the art, on the basis of the teachings of the present invention, may make alterations and modifications without departing from the scope of the invention as defined by the appended claims
Within the scope of the present invention, the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.
Claims (9)
1. A carbon transaction green travel incentive method of a carbon transaction green travel incentive system is characterized by comprising the following steps:
the carbon transaction green travel incentive system comprises: the system comprises a mobile terminal, a Wi-Fi probe, a vehicle-mounted GPS module, a vehicle-mounted speed sensor, a vehicle-mounted time module, a vehicle-mounted Wi-Fi module, a first wireless communication module, a machine learning server, a second wireless communication module and a user excitation server;
the mobile terminal is connected with the Wi-Fi probe in a wireless communication mode; the vehicle-mounted Wi-Fi module is respectively connected with the Wi-Fi probe, the vehicle-mounted GPS module, the vehicle-mounted speed sensor and the vehicle-mounted time module in sequence through leads; the mobile terminal is connected with the first wireless communication module in a wireless communication mode; the vehicle-mounted Wi-Fi module is connected with the first wireless communication module in a wireless communication mode; the first wireless communication module is connected with the machine learning server through a wire; the first wireless communication module is connected with the second wireless communication module in a wireless communication mode; the second wireless communication module is connected with the user excitation server through a wire; the second wireless communication module is connected with the mobile terminal in a wireless communication mode;
the carbon transaction green travel incentive method comprises the following steps:
step 1: the mobile terminal collects a user data set consisting of a user MAC address and daily activity track data and transmits the user data set to the machine learning server in a wireless manner;
step 2: the vehicle-mounted GPS module records a vehicle data set, namely vehicle travel time, vehicle position, vehicle speed and vehicle information, and the Wi-Fi probe acquires a mobile phone MAC address of a user;
and step 3: the vehicle-mounted Wi-Fi module uploads data to a machine learning server;
and 4, step 4: the machine learning server calculates the row distance by using the collected user data;
and 5: the machine learning server sequentially matches the user data set, the vehicle data set and the user travel distance to screen qualified data for data storage;
and 6: the machine learning server carries out BP neural network model training on training set data, the training set is input into a neural network for repeated training until the deviation and variance of the data meet requirements, and the data of each training is all trip data in days of a certain week;
and 7: the machine learning server puts the trained neural network model into application, predicts a trip mode and a trip mode of a user, constructs user incentive data and transmits the user incentive data to the user incentive server;
and 8: the user incentive server calculates and stores carbon emission generated by travel and corresponding carbon transaction price or point according to user incentive data;
and step 9: the mobile terminal is communicated with the user incentive server periodically through the second wireless communication module, part of the carbon transaction price is subsidized to be the fare for the next trip, or a certain amount of points are deducted to provide rewards, and the user can donate the rewards to a welfare agency; and the mobile terminals are communicated with each other regularly, and the block chain account book is synchronized.
2. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1, wherein the mobile terminal queries device information to obtain an MAC address in step 1, and obtains daily activity trajectory data through a terminal built-in positioning module;
in step 1, the data set at the time t in the ith trip of the user is as follows:
wherein i is e {1,2,3, …, N }
ID is the MAC address of the user;
T i the travel time of the ith trip for the user;
3. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1, wherein the vehicle-mounted GPS module records vehicle information, vehicle speed, vehicle location in step 2; acquiring the current moment through a vehicle-mounted time module; the Wi-Fi probe scans the mobile terminal equipment in the vehicle at a certain frequency and regularly obtains the MAC addresses of all the terminal equipment in the vehicle;
in the step 2, the data set at the time t in the jth trip of the vehicle is as follows:
wherein j is epsilon {1,2,3, …, N * }
I c The vehicle information is the type and the number of the persons carrying the vehicle C;
4. A carbon transaction green travel incentive method for a carbon transaction green travel incentive system according to claim 1, wherein in the step 3, the vehicle-mounted Wi-Fi module uploads a vehicle data set C to the machine learning server via the first wireless communication module by a wireless communication manner.
5. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1, wherein the machine learning server calculates a row distance using the collected user data in step 4 as follows:
the ith travel distance L of the user i The calculation method is as follows:
k is the total time number in one trip.
6. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1, wherein the matching conditions in the step 5 are as follows:
will be provided with
Intermediate ID and
in
Performing MAC address matching;
distance L for user to travel i Performing distance matching, wherein H is a distance threshold, and judging whether L is i H m;
counting the data meeting the 3 conditions, giving a quantity threshold value beta, and if the quantity of the data meeting the conditions is not less than beta, considering the data set
If the requirement is met, keeping the training set, otherwise, discarding;
organized in the order of the sequence numbers in the week on the same day every week in a period of time, for the user on day D every week, D belongs to {1,2, …,7}, the ith trip t, t belongs to {1,2, …, K } time data set
Repeating matching to finally obtain N effective sets A as training set data;
the stored data in step 5 are as follows:
wherein, ID R A MAC address for the user;
I c R the vehicle information is the type and the number of the nuclear people of the vehicle C;
T i R the travel time of the ith trip for the user;
7. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1, wherein the BP neural network model in step 6 is:
the input layer neuron number S is 5, i.e. the travel time T i R Travel speed
Distance of travel
Trip starting point
Vehicle type I c R The sample number is D, i trips, the weight of the total of K trip data at each time is respectively
The output layer neuron number O is 4, namely the trip end point B i Time to get on the bus C i Get-off time D i And travel mode E i The weights are respectively
The number of neurons in the hidden layer is 4, and the method is calculated by experience
Obtaining;
the error function model is
Wherein t is n To the desired output u n Is the actual output;
calculating by using a gradient descent method;
and (5) feeding back according to the output, adjusting neuron weights of an input layer, an output layer and a hidden layer, and repeating training until the prediction accuracy is greater than delta.
8. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1, wherein the step 7 of putting the trained neural network model into application comprises:
identifying a trip mode and a trip mode of a user;
the user motivation data in step 7 is as follows:
P i ＝{A i ,B i ,C i ,D i ,E i ,F i }，i∈[1,N]
wherein N is the number of users, A i As coordinates of starting point of trip, B i Is a travel terminal coordinate; c i For getting on, D i For alighting time, E i For the trip mode, F i The travel speed.
9. The carbon transaction green travel incentive method of the carbon transaction green travel incentive system according to claim 1,
the formula for calculating the carbon emission generated by trip in step 8 is as follows:
L i ＝∑ j,k,t [T·V·G j,k,t ·H j,k,t ]
wherein:
t is the running time of the vehicle;
v is the running speed of the vehicle;
G j,k,t l/100km for fuel consumption;
H j,k,t carbon emission factor for different fuels;
j is a vehicle type, such as a car, bus;
k is a fuel type such as raw coal, gasoline, diesel, kerosene, natural gas;
t is road type, such as city, country, default as city road when lacking data;
the transaction price of each ton of carbon dioxide equivalent can refer to the transaction price of the current market, and under the condition that the market is not mature, the carbon emission generated by each trip of a passenger can be converted into points by adopting a point system and then the points are counted into an account, and one-time riding fee is saved as reward by a certain point;
the formula for calculating the carbon trade price or point is as follows:
wherein:
L car carbon emission generated by the shortest path of the driving starting point and the driving ending point of the average car type car;
L real carbon emission actually generated in the user trip process;
gamma is a factor converted into a carbon transaction price or point;
h is the carbon emission factor of the motor gasoline;
when the user walks and rides a bicycle, carbon emission is not generated, the maximum value is taken under the condition, and the price subsidy or the point given to the user is the largest;
when the user travels by car, the carbon emission is the most, and the carbon trading price or point is 0.
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Application publication date: 20190827 Assignee: CLP Tongtu (Beijing) Technology Co.,Ltd. Assignor: WUHAN University OF TECHNOLOGY Contract record no.: X2023420000265 Denomination of invention: A Green Travel Incentive System and Method Based on Carbon Trading Granted publication date: 20230103 License type: Common License Record date: 20230801 |
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