CN115146077A - Individual vehicle trip emission knowledge map construction method - Google Patents
Individual vehicle trip emission knowledge map construction method Download PDFInfo
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Abstract
The invention provides a construction method of an individual vehicle trip emission knowledge map, and relates to the technical field of urban traffic system data mining. The method comprises the following steps: s1, obtaining basic data of vehicle traveling in an area; s2, performing data association to obtain a multi-dimensional trip emission associated data set; s3, constructing a knowledge graph structure associated with the 'vehicle-road-trip-emission' entity; and S4, generating a vehicle travel emission knowledge graph in the region by using the knowledge graph structure. The method is based on a multidimensional travel emission association data set and a 'vehicle-road-travel-emission' map structure, and an individual vehicle travel emission knowledge map is generated to realize knowledge expression of individual vehicle travel emission; the method can accurately express the potential correlation among the multi-dimensional traffic travel emission data and the dynamic evolution and knowledge expression of the individual vehicle travel emission rule, and obtain higher traffic emission evaluation precision.
Description
Technical Field
The invention relates to the technical field of data mining of urban traffic systems, in particular to a construction method of an individual vehicle trip emission knowledge map.
Background
While bringing convenience to people for traveling, the motor vehicles discharge a large amount of harmful substances in tail gas into the atmospheric environment, and become a main source of urban air pollution. In dense population areas, high-concentration exhaust gas directly threatens the health of residents, and the incidence of diseases such as allergy, bronchitis and pneumonia is increased. The high-precision traffic emission assessment can realize the cognition of the dynamic change characteristics of the individual vehicle emission during the trip, provides support for the accurate management and control of the traffic emission, and is indispensable technical research for treating atmospheric pollution.
However, in past research, the emission state of a region or a road is mostly evaluated by establishing a vehicle emission relational database in a macroscopic scale and a mesoscopic scale. Due to the fact that vehicle traveling has individual difference and strong time variation, the relation type database is often difficult to accurately express potential relations among the multi-dimensional traveling emission data and the dynamic evolution process of the individual vehicle traveling emission law, and the emission evaluation fineness is low. In the face of large volume and multiple categories of multi-dimensional trip emission data, a method is needed, which can deeply mine the coupling correlation between vehicle-road-trip-emission data and realize the quick retrieval and cognition of individual trip emission
The prior art document relates to a motor vehicle exhaust emission data fusion system, which comprises a roadside air pollutant concentration estimation module, a roadside air pollutant concentration forecasting module, an urban global atmospheric environment prediction module, a motor vehicle exhaust emission factor estimation module and a motor vehicle exhaust emission characteristic analysis module; the five modules respectively realize different data analysis functions, and different functions can be realized by selecting different modules; the system can be used independently, or two or more than two of the systems can be combined to realize the storage, analysis and fusion of the motor vehicle tail gas telemetering data, the motor vehicle attribute, the driving condition, the detection time and the meteorological condition data, and the analysis and the processing of the motor vehicle tail gas telemetering data are carried out by combining a vehicle-mounted diagnosis system database, a portable emission test system database, a vehicle inspection station off-line database, a traffic information database and a geographic information database to obtain the key indexes and statistical data with the most identification power; but uses a vehicle emissions relational database for regional or road emissions status assessment. Due to the fact that vehicle traveling has individual differences and strong time variation, the relation type database is often difficult to accurately express the potential relation among the multidimensional traveling emission data and the dynamic evolution process of the individual vehicle traveling emission rule, and the emission evaluation fineness is low.
Disclosure of Invention
The invention provides a construction method of an individual vehicle travel emission knowledge map with high emission evaluation fineness for overcoming the technical problems.
The technical scheme of the invention is as follows:
a construction method of an individual vehicle trip emission knowledge graph comprises the following steps:
s1, obtaining basic data of vehicle traveling in a preset area;
s2, carrying out data association on the vehicle trip basic data to obtain a multi-dimensional trip emission associated data set;
s3, constructing a knowledge graph structure;
and S4, generating a vehicle travel emission knowledge graph in the region by using the vehicle travel basic data, the multi-dimensional travel emission association data set and the knowledge graph structure.
The technical scheme provides an individual vehicle travel emission knowledge map construction method, which comprises the steps of determining knowledge map basic data according to traffic emission assessment requirements, and then completing construction of a multi-dimensional travel emission associated data set by using the obtained data; secondly, designing a knowledge graph structure of entity association of 'vehicle-road-travel-discharge' by taking discharge information representing the individual vehicle travel process as a main line, effectively supporting the vehicle travel discharge relation cognition and accurate control from fine to individual level, and accurately and finely expressing the potential association between the vehicle discharge and the vehicle technical performance and travel; finally, generating an individual vehicle trip emission knowledge map on the basis of the multi-dimensional trip emission associated data set and the map structure of 'vehicle-road-trip-emission', and realizing the knowledge expression of individual vehicle trip emission; the method can accurately express the potential correlation among the multi-dimensional traffic travel emission data and the dynamic evolution and knowledge expression of the individual vehicle travel emission rule, and obtain higher traffic emission evaluation precision.
Further, the vehicle travel basic data in step S1 includes vehicle technical information data and individual vehicle travel data, the vehicle technical information data is obtained by collecting vehicle registration information data and public network vehicle technical parameter data, and the vehicle technical information data includes: go hidden private license plate number, license plate kind, registration date, vehicle type, emission standard, fuel type, total mass, vehicle age, brand and discharge capacity, individual vehicle trip data obtains through city traffic trip monitoring data source, and individual vehicle trip data includes: go to the number of the hidden private license plate, the kind of the license plate, the trip road section, the trip starting point, the starting point moment, the trip end point, the end point moment, the trip duration, the vehicle speed and the driving direction;
and S2, the multi-dimensional travel emission associated data set comprises a travel-vehicle data associated data set, and the vehicle technical information data and the individual vehicle travel data are associated to obtain the travel-vehicle data associated data set.
Further, the method for acquiring the trip data of the individual vehicle comprises the following steps: according to the urban traffic trip monitoring data source, the gate vehicle-passing record data of all vehicles in the area are obtained, the gate vehicle-passing record data of the individual vehicles are obtained by screening with the individual vehicles as objects, the gate vehicle-passing record data of the individual vehicles are sequenced according to time to obtain the trip travel of the individual vehicles, and the travel time length, the travel mileage and the vehicle speed of the trip travel are calculated; and constructing travel routes of all the individual vehicles in the area as individual vehicle travel data according to the steps.
Further, the association method for associating the vehicle technical information data with the individual vehicle travel data is as follows: and matching the vehicle technical information data to vehicle travel track data of all individual vehicles in the area by using the vehicle technical information data and the number plate number and the number plate type of the individual vehicle in the individual vehicle travel data to obtain a travel-vehicle data association data set.
Further, the vehicle travel basic data in the step S1 further includes individual vehicle emission data, and the individual vehicle emission data is calculated by using vehicle technical information data of the individual vehicle and the individual vehicle travel data;
and S2, the multi-dimensional travel emission associated data set further comprises a travel-vehicle-emission data associated data set, and vehicle technical information data, individual vehicle travel data of all individual vehicles in the area and individual vehicle emission data of all individual vehicles in the area in a travel track by travel track are associated to obtain the travel-vehicle-emission data associated data set.
Further, the individual vehicle emission data includes an emission amount and an emission intensity, and the emission amount and the emission intensity are calculated by:
wherein Q r,a,t,n For the emission of pollutants during a time period t for a vehicle w travelling on a route section r, I r,w,t,n In order to obtain the emission intensity of pollutants for the vehicle w traveling on the section r during the time period t,
for scene-based average velocity, v w,t,n For the average travel speed, EF, of a vehicle w travelling on a route section r over a time period t (base) Based on emission factor, EF (Bin) For operating condition correction factors, EF (Age) For deterioration correction factor, EF (Fuel) As a fuel quality correction factor, L r For the length of the section r, i is the vehicle type, j is the emission standard, k is the fuel type, and y is the age of the vehicle.
Further, the vehicle trip basic data in the step S1 further includes road information data, and the road information data is extracted by using city road network geographic information data;
and S2, the multi-dimensional travel emission associated data set also comprises a road-travel data associated data set, road section IDs are used as unique identifications of individual vehicle travel processes, the road information data are matched with the individual vehicle travel data, and the road information data and the individual vehicle travel data are associated to obtain the road-travel data associated data set.
Further, the knowledge-graph structure in step S3 comprises: the system comprises a vehicle information node knowledge graph, a travel information node knowledge graph, an emission information node knowledge graph and a road information node knowledge graph;
the vehicle information node knowledge graph is constructed by vehicle information nodes, and the vehicle information nodes comprise: vehicle information, vehicle type, emission standard, fuel type, total mass, age, brand, and displacement; respectively connecting the type of a vehicle, the emission standard, the type of fuel, the total mass, the age of the vehicle, the brand and the displacement by taking the vehicle information as a center, thereby constructing and obtaining a knowledge graph of the vehicle information node;
the travel information node knowledge graph is constructed by utilizing travel information nodes, and the travel information nodes comprise: travel information, date, hour, road segment and speed level; taking travel information as a center, and respectively connecting date, hour, road section and speed grade, thereby constructing a knowledge graph of travel information nodes and connecting the travel information with vehicle information;
the discharge information class node knowledge graph is constructed by using discharge information class nodes, and the discharge information class nodes comprise: the method comprises the following steps of (1) discharging information, a trip discharging amount grade, a trip discharging intensity grade and a road discharging intensity grade; respectively connecting a trip emission amount grade, a trip emission intensity grade and a road section emission intensity grade by taking the emission information as a center, thereby constructing a knowledge graph of the emission information class and connecting the emission information with the trip information;
the road information node knowledge graph is constructed by using road information nodes, and the road information nodes comprise: road information and road grade; and connecting the road information with the road grade so as to construct and obtain a road information type knowledge graph and connect the road information with the travel information.
Further, the step S4 of generating the in-region vehicle travel emission knowledge graph is based on the knowledge graph structure constructed in the step S3, and the in-region vehicle travel emission knowledge graph is generated by using the multi-dimensional travel emission associated data set established in the step S2.
Further, the specific method for generating the in-region vehicle travel emission knowledge graph comprises the following steps:
constructing a vehicle information node knowledge graph by using the vehicle technical information data; establishing a travel information node knowledge map by adopting the individual vehicle travel data; establishing an emission information node knowledge graph by adopting the individual vehicle emission data; establishing a road information node knowledge graph by using the road information data; and then storing and performing associated representation on the vehicle information node knowledge graph, the travel information node knowledge graph, the emission information node knowledge graph and the vehicle travel basic data in the road information node knowledge graph in a graph database form according to the travel-vehicle data associated data set, the travel-vehicle-emission data associated data set and the road-travel data associated data set to obtain the vehicle travel emission knowledge graph in the region.
The technical scheme provides a construction method of an individual vehicle trip emission knowledge map, and compared with the prior art, the technical scheme has the beneficial effects that: determining knowledge graph basic data according to traffic emission assessment requirements, and then completing construction of a multi-dimensional travel emission associated data set by using the obtained data; secondly, designing a knowledge graph structure of entity association of 'vehicle-road-travel-discharge' by taking discharge information representing the individual vehicle travel process as a main line, effectively supporting the vehicle travel discharge relation cognition and accurate control from fine to individual level, and accurately and finely expressing the potential association between the vehicle discharge and the vehicle technical performance and travel; finally, generating an individual vehicle trip emission knowledge map on the basis of the multi-dimensional trip emission associated data set and the map structure of 'vehicle-road-trip-emission', and realizing the knowledge expression of individual vehicle trip emission; the method can accurately express the potential correlation among the multi-dimensional traffic travel emission data and the dynamic evolution and knowledge expression of the individual vehicle travel emission rule, and obtain higher traffic emission evaluation precision.
Drawings
FIG. 1 is a schematic diagram of steps of a construction method of an individual vehicle trip emission knowledge map;
FIG. 2 is a schematic view of a knowledge-graph structure;
FIG. 3 is a schematic illustration of the results of an individual vehicle emission characteristic search using a knowledge map;
fig. 4 is a diagram illustrating the results of a vehicle type-age-emission standard association search using a knowledge map.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Example 1
An individual vehicle travel emission knowledge graph construction method is shown in fig. 1, and comprises the following steps:
s1, obtaining basic data of vehicle traveling in a preset area;
s2, carrying out data association on the vehicle trip basic data to obtain a multi-dimensional trip emission associated data set;
s3, constructing a knowledge graph structure;
and S4, generating a vehicle travel emission knowledge graph in the region by utilizing the vehicle travel basic data, the multi-dimensional travel emission associated data set and the knowledge graph structure.
The method comprises the steps of determining knowledge graph basic data according to traffic emission assessment requirements, and then completing construction of a multi-dimensional travel emission associated data set by using the obtained data; then, by taking emission information representing the individual vehicle traveling process as a main line, designing a knowledge graph structure of a vehicle-road-traveling-emission entity association, effectively supporting the vehicle traveling emission relationship cognition and accurate control from the fine level to the individual level, and accurately and finely expressing the potential association between the vehicle emission and the vehicle technical performance and traveling; finally, generating an individual vehicle trip emission knowledge map on the basis of the multi-dimensional trip emission associated data set and the map structure of 'vehicle-road-trip-emission', and realizing the knowledge expression of individual vehicle trip emission; the method can accurately express the potential correlation among the multi-dimensional traffic travel emission data and the dynamic evolution and knowledge expression of the individual vehicle travel emission rule, and obtain higher traffic emission evaluation precision.
Example 2
In this embodiment, on the basis of embodiment 1, in this embodiment, the vehicle travel basic data in step S1 includes vehicle technical information data and individual vehicle travel data, the vehicle technical information data is obtained by collecting motor vehicle registration information data and public network vehicle technical parameter data, and the vehicle technical information data includes: go hidden private license plate number, license plate kind, registration date, vehicle type, emission standard, fuel type, total mass, vehicle age, brand and discharge capacity, individual vehicle trip data obtains through city traffic trip monitoring data source, and individual vehicle trip data includes: go to the number of the hidden private license plate, the kind of the license plate, the trip road section, the trip starting point, the starting point moment, the trip end point, the end point moment, the trip duration, the vehicle speed and the driving direction;
and S2, the multi-dimensional travel emission associated data set comprises a travel-vehicle data associated data set, and the vehicle technical information data and the individual vehicle travel data are associated to obtain the travel-vehicle data associated data set.
The method for acquiring the trip data of the individual vehicle comprises the following steps: according to a city traffic travel monitoring data source, obtaining the passing vehicle record data of all vehicles in the area, screening by taking the individual vehicle as an object to obtain the passing vehicle record data of the individual vehicle, sequencing the passing vehicle record data of the individual vehicle according to time to obtain the travel of the individual vehicle, and calculating the travel time, the travel mileage and the vehicle speed of the travel; and constructing vehicle travel tracks of all the individual vehicles in the vehicle technical information data as individual vehicle travel data according to the steps. The individual vehicle travel data includes: go to the number of the hidden private license plate, the kind of the license plate, the trip road section, the trip starting point, the starting point moment, the trip end point, the end point moment, the trip duration, the vehicle speed and the driving direction;
the correlation method for correlating the vehicle technical information data and the individual vehicle travel data comprises the following steps: and matching the vehicle technical information data to vehicle travel track data of all individual vehicles in the area by using the vehicle technical information data and the number plate number and the number plate type of the individual vehicle in the individual vehicle travel data to obtain a travel-vehicle data association data set.
The vehicle travel basic data in the step S1 further comprise individual vehicle emission data, and the individual vehicle emission data are obtained by calculation by utilizing vehicle technical information data of individual vehicles and individual vehicle travel data;
the individual vehicle emission data comprises emission amount and emission intensity, and the emission amount and the emission intensity are calculated by the following method:
wherein Q is r,a,t,n For the emission of pollutants during a time period t for a vehicle w travelling on a route section r, I r,w,t,n In order to obtain the emission intensity of pollutants for the vehicle w traveling on the section r during the time period t,
for scene-based average velocity, v w,t,n For the average travel speed, EF, of a vehicle w travelling on a route section r over a time period t (base) Based on emission factor, EF (Bin) For operating condition correction factors, EF (Age) For deterioration correction factor, EF (Fuel) As a fuel quality correction factor, L r For the length of the section r, i is the vehicle type, j is the emission standard, k is the fuel type, and y is the age of the vehicle.
The pollutant emission data and the pollutant emission intensity data are classified into emission grade intervals according to 33% and 66% quantile points; the method for carrying out emission grade interval division on pollutant emission data comprises the following steps: acquiring pollutant discharge amount grade parameter data; the numerical value of the quantile less than 33 percent is defined as a low emission grade interval, the numerical value of the quantile more than 66 percent is defined as a high emission grade interval, and the numerical value between the two is defined as a medium emission grade interval;
the method for dividing the emission intensity grade interval of the pollutant emission intensity data comprises the following steps: acquiring pollutant emission intensity grade parameter data; values below 33% quantile are defined as low emission intensity class intervals, values above 66% quantile are defined as high emission intensity class intervals, and values in between are defined as medium emission intensity class intervals.
And S2, the multi-dimensional travel emission associated data set further comprises a travel-vehicle-emission data associated data set, and vehicle technical information data, individual vehicle travel data of all individual vehicles in the area and individual vehicle emission data of all individual vehicles in the area in a travel track by travel track are associated to obtain the travel-vehicle-emission data associated data set.
The method for obtaining the travel-vehicle-emission data association data set comprises the following steps: calculating the gaseous pollutant emission amount and emission intensity of the motor vehicle trip track by vehicle trip track data and vehicle technical information data of the individual vehicles so as to obtain the trip track by trip track emission data of all the individual vehicles in the area; associating the vehicle technical information data, the vehicle travel track data of all individual vehicles in the area and the trip track emission data of all individual vehicles in the area one by one to obtain a travel-vehicle-emission data association data set;
the vehicle travel basic data in the step S1 further comprise road information data, and the road information data are obtained by utilizing urban road network geographic information data in an extraction mode; the road information data includes: road name, road type, link ID, location, and link length.
And S2, the multi-dimensional travel emission associated data set further comprises a road-travel data associated data set, and the road information data and the individual vehicle travel data are associated to obtain the road-travel data associated data set.
The method for obtaining the road-trip data associated data set comprises the following steps: matching the road information data with individual vehicle travel data by taking a road section ID as a unique identifier of an individual vehicle travel process to obtain a road-travel data association dataset;
step S3, the knowledge-graph structure comprises: the system comprises a vehicle information node knowledge graph, a travel information node knowledge graph, an emission information node knowledge graph and a road information node knowledge graph;
the vehicle information node knowledge graph is constructed by vehicle information nodes, and the vehicle information nodes comprise: vehicle information, vehicle type, emission standard, fuel type, total mass, age, brand, and displacement; respectively connecting the type of a vehicle, the emission standard, the type of fuel, the total mass, the age of the vehicle, the brand and the displacement by taking the vehicle information as a center, thereby constructing and obtaining a knowledge graph of the vehicle information node;
the travel information node knowledge graph is constructed by utilizing travel information nodes, and the travel information nodes comprise: travel information, date, hour, road segment and speed level; taking travel information as a center, and respectively connecting date, hour, road section and speed grade, thereby constructing a knowledge graph of travel information nodes and connecting the travel information with vehicle information;
the discharge information node knowledge graph is constructed by using discharge information nodes, and the discharge information nodes comprise: the method comprises the following steps of (1) emission information, a trip emission quantity grade, a trip emission intensity grade and a road section emission intensity grade; respectively connecting a trip emission amount grade, a trip emission intensity grade and a road section emission intensity grade by taking the emission information as a center, thereby constructing a knowledge graph of the emission information class and connecting the emission information with the trip information;
the road information node knowledge graph is constructed by using road information nodes, and the road information nodes comprise: road information and road grade; and the road information is connected with the road grade, so that a road information type knowledge graph is constructed and obtained, and the road information is connected with the travel information.
S4, generating a regional vehicle trip emission knowledge graph by adopting the multi-dimensional trip emission associated data set established in the S2 on the basis of the knowledge graph structure established in the S3;
the method specifically comprises the following steps: constructing a vehicle information node knowledge graph by using the vehicle technical information data in the step S22; establishing a travel information node knowledge graph by using the vehicle travel track data in the step S22; establishing a discharge information node knowledge graph by using the vehicle discharge track data in the step S23; establishing a road information node knowledge graph by using the road information data in the step S21; meanwhile, the basic data of the vehicle travel is stored and associated and represented in a graph database form, and a vehicle travel emission knowledge graph in the region is obtained.
Example 3
The embodiment provides a method for constructing an individual vehicle trip emission knowledge graph by combining a specific data example, which comprises the following steps:
s1: basic data required by the knowledge graph are determined and obtained, and the basic data comprise vehicle technical information data, road information data, individual vehicle travel data and individual vehicle emission data.
More specifically, in step S1, the specific process of acquiring the vehicle travel basic data is as follows:
s11: the vehicle technical information data includes: number of go-to-conceal private license plate, license plate type, registration date, vehicle type, emission standard, fuel type, total mass, age of vehicle, brand and displacement. And directly acquiring vehicle technical information data by using the multi-source heterogeneous data acquisition capability of the motor vehicle registration information. The obtained vehicle technical information data is used for removing the number of the private license plate, the type of the license plate, the registration date, the type of the vehicle, the emission standard, the type of fuel, the total mass, the age, the brand and the displacement. Referring to a national standard document, a standard classification system of vehicle types, emission standards and fuel types is determined, wherein the vehicle types are divided into: k1 large-scale passenger cars, K2 medium-scale passenger cars, K3 small-scale passenger cars, K1 heavy-duty trucks and the like. The emission standard is divided into: country i before, country i, country ii, country iii, country iv and country v. The fuel types are: gasoline A, diesel oil B, electricity C, natural gas E, solar energy N and the like. The vehicle technical information data is shown in table one.
Table-vehicle technical information data example
The method for acquiring the vehicle age comprises the following steps: and combining the vehicle travel date with the registration date, and calculating the vehicle age by taking the year as a unit. The vehicle age calculation can be performed using equation (1):
y w ＝(D w -T w )/365 (1)
wherein, y w Indicating the age of the vehicle w, D w Indicating the date of vehicle trip, T w Indicating the vehicle registration date.
S12: the individual vehicle travel data includes: go to the number of the hidden private license plate, the kind of the license plate, the trip road section, the trip starting point, the starting point moment, the trip end point, the end point moment, the trip duration, the vehicle speed and the driving direction;
the method for acquiring the trip data of the individual vehicle comprises the following steps: according to the urban traffic trip monitoring data source, on the basis of all bayonet vehicle passing record data in a research area, the bayonet vehicle passing records are sequentially connected according to the ascending order of time, an individual vehicle is taken as an object, any vehicle trip short trip is reconstructed, the trip duration, the trip mileage and the vehicle speed are calculated, the vehicle trip track of the whole area individual is formed, and the required individual vehicle trip data is obtained. The individual vehicle travel data specifically comprises parameter information such as a number of a go-hidden private license plate, a license plate type, a travel road section, a travel starting point, a starting point moment, a travel terminal point, a terminal point moment, a travel duration, a vehicle speed, a travel direction and the like. An example of the individual vehicle travel data is shown in table two.
Table two individual vehicle travel data example
Aiming at the problems that part of bayonet data are concentrated in space-time distribution and the vehicle track is difficult to determine, the straight-line distance between the bayonet coordinates of the vehicle at the first moment and each adjacent monitoring point is calculated, the bayonet point position with the minimum Euclidean distance value is selected as the coordinate information of the vehicle at the next moment, and the travel track of the vehicle is accurately restored.
Wherein, the vehicle speed information is the average travel speed, and the calculation is carried out by adopting an equation (2):
v w,n ＝L a /(t w,n+1 -t w.n ) (2)
wherein v is w,n Average running speed, L, for the vehicle w passing the nth track unit a Is the length of road segment a; t is t w,n ,t w,n+1 Respectively, the starting point and the end point of the vehicle w passing through the nth track unit.
S13: and extracting road information data of the required road section based on the urban road network geographic information data, wherein the road information data comprises road name, road type, road section number, position and road section length parameter information. The road information data is shown in table two, for example.
Example of the Table three road information data
S2: and establishing a travel emission association data set, including association of multi-source heterogeneous data and emission calculation.
The data association process consists of three parts, namely road-trip data association, trip-vehicle data association and trip-vehicle-emission data association, and comprises the following specific processes:
s21: road-trip data correlation. And matching the road information data in the step S13 with the individual vehicle travel data in the step S12 by using the "link ID" parameter information as a unique identifier of the individual vehicle travel process.
S22: travel-vehicle data correlation. The 'license plate number + license plate type' is used as an identity identification label of the vehicle, and the dilemma that a plurality of motor vehicles cannot be uniquely identified due to the same license plate number is solved. And matching the processed vehicle technical information data into vehicle travel track units one by utilizing the vehicle identity, and finishing the time-space association of the individual vehicle travel data and the technical information.
S23: trip-vehicle-emissions data correlation. And taking the single trip track of the individual vehicle in the step S12 as an activity level parameter, and taking the technical parameters of the individual vehicle in the step S11 as calculation indexes, calculating the emission amount and emission intensity of gaseous pollutants of the motor vehicle on each trip track, so as to extract the emission data of the regional total individual vehicle on each trip track and associate the emission data with the trip-vehicle-emission data.
The individual vehicle travel track emission data is calculated by adopting an equation (3) and an equation (4) respectively:
wherein Q is r,a,t,n For the emission of pollutants of a vehicle w travelling on a route section r over a time period t, I r,w,t,n For the intensity of pollutant emissions during the time period t for a vehicle w travelling on a road section r,
for scene-based average velocity, v w,t,n For the average travel speed, EF, of a vehicle w travelling on a route section r over a time period t (base) Based on emission factor, EF (Bin) For operating condition correction factors, EF (Age) For deterioration correction factor, EF (Fuel) As a fuel quality correction factor, L r For the length of the section r, i is the vehicle type, j is the emission standard, k is the fuel type, y is the age of the vehicle; s3: and designing a knowledge graph structure associated with the entity of 'vehicle-road-travel-emission'.
S3: constructing a knowledge graph structure associated with a vehicle-road-trip-emission entity;
the knowledge-graph structure comprises: the system comprises a vehicle information node knowledge graph, a travel information node knowledge graph, an emission information node knowledge graph and a road information node knowledge graph; there are 19 entity nodes and 18 connection relations. The vehicle information node knowledge graph is constructed by utilizing vehicle information nodes, and the vehicle information nodes comprise: vehicle information, vehicle type, emission standard, fuel type, total mass, age, brand, and displacement; respectively connecting the type of a vehicle, the emission standard, the type of fuel, the total mass, the age of the vehicle, the brand and the displacement by taking the vehicle information as a center, thereby constructing and obtaining a knowledge graph of the vehicle information node;
the travel information node knowledge graph is constructed by utilizing travel information nodes, and the travel information nodes comprise: travel information, date, hour, road segment and speed level; taking travel information as a center, and respectively connecting dates, hours, road sections and speed levels, thereby constructing a knowledge graph of travel information nodes;
the discharge information class node knowledge graph is constructed by using discharge information class nodes, and the discharge information class nodes comprise: the method comprises the following steps of (1) emission information, a trip emission quantity grade, a trip emission intensity grade and a road section emission intensity grade; respectively connecting a trip emission amount grade, a trip emission intensity grade and a road section emission intensity grade by taking the emission information as a center, thereby constructing and obtaining a knowledge graph of the emission information class;
the road information node knowledge graph is constructed by using road information nodes, and the road information nodes comprise: road information and road grade; and connecting the road information with the road grade so as to construct and obtain the road information type knowledge graph.
The entity nodes and attributes in the knowledge-graph structure of the "vehicle-road-trip-emission" entity association are shown in table four.
Table four entity node names and attributes
The specific knowledge-graph structure is shown in figure 2.
In the construction of the travel information category map, the speed grade entity needs to grade the vehicle speed in advance. The vehicle speed grade adopts a traffic running condition grade division method based on the road section evaluation travel speed in urban traffic running condition evaluation specifications to divide the vehicle speed into 5 categories of high speed, medium speed, low speed and low speed. The specific division standard is shown in table four.
Meter-five vehicle speed grade division method
Wherein, V j Indicating the average travel speed, V, of the road section f Representing the road segment free stream velocity.
The connection relationship between the entity nodes is shown in table six.
Connection relation between six entity nodes of table
S4: and (4) generating the individual vehicle trip emission knowledge graph by adopting the multidimensional trip emission associated data set established in the step (S2) on the basis of the knowledge graph structure designed in the step (S3). Constructing a vehicle information class chart part by adopting vehicle technical information data; establishing a travel information category map part by adopting vehicle travel track data; establishing a spectrum knot part of an emission information class diagram by adopting vehicle emission track data; and establishing a road information class chart part by adopting the road information data.
More specifically, the data import adopts a Neo4j graph database as a data storage platform, and the import of the multidimensional traffic emission related data is realized through a Neo4j-import method. And the attached figure 3 shows partial contents of the finally established knowledge graph, and realizes graphical knowledge expression of individual-level vehicle trip emission.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A construction method of an individual vehicle trip emission knowledge map is characterized by comprising the following steps:
s1, obtaining vehicle travel basic data in a preset area;
s2, carrying out data association on the vehicle trip basic data to obtain a multi-dimensional trip emission associated data set;
s3, constructing a knowledge graph structure;
and S4, generating a vehicle travel emission knowledge graph in the region by utilizing the vehicle travel basic data, the multi-dimensional travel emission associated data set and the knowledge graph structure.
2. The individual vehicle travel emission knowledge graph construction method according to claim 1, wherein the vehicle travel basic data in step S1 includes vehicle technical information data and individual vehicle travel data, the vehicle technical information data is obtained by collecting motor vehicle registration information data and public network vehicle technical parameter data, and the vehicle technical information data includes: go hidden private license plate number, license plate kind, registration date, vehicle type, emission standard, fuel type, total mass, vehicle age, brand and discharge capacity, individual vehicle trip data obtains through city traffic trip monitoring data source, and individual vehicle trip data includes: go to the number of the hidden private license plate, the kind of the license plate, the trip road section, the trip starting point, the starting point moment, the trip end point, the end point moment, the trip duration, the vehicle speed and the driving direction;
and S2, the multi-dimensional travel emission associated data set comprises a travel-vehicle data associated data set, and the vehicle technical information data and the individual vehicle travel data are associated to obtain a travel-vehicle data associated data set.
3. The individual vehicle travel emission knowledge graph construction method according to claim 2, wherein the individual vehicle travel data acquisition method comprises the following steps: according to a city traffic travel monitoring data source, obtaining the passing vehicle record data of all vehicles in the area, screening by taking the individual vehicle as an object to obtain the passing vehicle record data of the individual vehicle, sequencing the passing vehicle record data of the individual vehicle according to time to obtain the travel of the individual vehicle, and calculating the travel time, the travel mileage and the vehicle speed of the travel; and constructing vehicle travel tracks of all the individual vehicles in the vehicle technical information data as individual vehicle travel data according to the steps.
4. The individual vehicle travel emission knowledge graph construction method according to claim 2, wherein the association method for associating the vehicle technical information data with the individual vehicle travel data is as follows: and matching the vehicle technical information data to the vehicle travel track data of all the individual vehicles in the area by using the vehicle technical information data and the number plate numbers and number plate types of the individual vehicles in the individual vehicle travel data to obtain a travel-vehicle data association data set.
5. The individual vehicle travel emission knowledge graph construction method according to claim 2, wherein the vehicle travel basic data of step S1 further includes individual vehicle emission data calculated by using vehicle technical information data of an individual vehicle and the individual vehicle travel data;
and S2, the multi-dimensional travel emission associated data set also comprises a travel-vehicle-emission data associated data set, and the vehicle technical information data, the individual vehicle travel data of all the individual vehicles in the area and the individual vehicle emission data of all the individual vehicles in the area along a travel track are associated to obtain a travel-vehicle-emission data associated data set.
6. The individual vehicle travel emission knowledge graph construction method according to claim 5, wherein the individual vehicle emission data comprises emission amount and emission intensity, and the emission amount and emission intensity are calculated by the following method:
I r,w,t,n ＝Q r,w,t,n /L r
wherein Q r,a,t,n For the emission of pollutants during a time period t for a vehicle w travelling on a route section r, I r,w,t,n In order to obtain the emission intensity of pollutants for the vehicle w traveling on the section r during the time period t,
is a scene-based average velocity, v w,t,n For the average travel speed, EF, of a vehicle w travelling on a route section r over a time period t (base) Based on emission factor, EF (Bin) For operating condition correction factors, EF (Age) For deterioration correction factor, EF (Fuel) As a fuel quality correction factor, L r For the length of the section r, i is the vehicle type, j is the emission standard, k is the fuel type, and y is the age of the vehicle.
7. The individual vehicle travel emission knowledge graph construction method according to claim 5, wherein the vehicle travel basic data of step S1 further comprises road information data extracted by using city road network geographic information data;
and S2, the multi-dimensional travel emission associated data set further comprises a road-travel data associated data set, road section IDs serve as unique identifications of individual vehicle travel processes, the road information data are matched with the individual vehicle travel data, and the road information data and the individual vehicle travel data are associated to obtain the road-travel data associated data set.
8. The individual vehicle travel emission knowledge graph construction method according to claim 7, wherein the knowledge graph structure of step S3 comprises: the system comprises a vehicle information node knowledge graph, a travel information node knowledge graph, an emission information node knowledge graph and a road information node knowledge graph;
the vehicle information node knowledge graph is constructed by vehicle information nodes, and the vehicle information nodes comprise: vehicle information, vehicle type, emission standard, fuel type, total mass, age, brand, and displacement; respectively connecting the type of a vehicle, the emission standard, the type of fuel, the total mass, the age of the vehicle, the brand and the displacement by taking the vehicle information as a center, thereby constructing and obtaining a knowledge graph of the vehicle information node;
the travel information node knowledge graph is constructed by utilizing travel information nodes, and the travel information nodes comprise: travel information, date, hour, road segment and speed level; taking travel information as a center, and respectively connecting date, hour, road section and speed grade, thereby constructing a knowledge graph of travel information nodes and connecting the travel information with vehicle information;
the discharge information class node knowledge graph is constructed by using discharge information class nodes, and the discharge information class nodes comprise: the method comprises the following steps of (1) emission information, a trip emission quantity grade, a trip emission intensity grade and a road section emission intensity grade; respectively connecting a trip emission amount grade, a trip emission intensity grade and a road section emission intensity grade by taking the emission information as a center, thereby constructing a knowledge graph of the emission information class and connecting the emission information with the trip information;
the road information node knowledge graph is constructed by using road information nodes, and the road information nodes comprise: road information and road grade; and the road information is connected with the road grade, so that a road information type knowledge graph is constructed and obtained, and the road information is connected with the travel information.
9. The individual vehicle travel emission knowledge graph construction method according to claim 8, wherein the intra-area vehicle travel emission knowledge graph generated in step S4 is generated by using the multi-dimensional travel emission association data set established in step S2 on the basis of the knowledge graph structure obtained in step S3.
10. The individual vehicle travel emission knowledge graph construction method according to claim 9, wherein a specific method for generating the intra-area vehicle travel emission knowledge graph is as follows:
constructing a vehicle information node knowledge graph by using the vehicle technical information data; establishing a travel information node knowledge map by adopting the individual vehicle travel data; establishing an emission information node knowledge graph by adopting the individual vehicle emission data; establishing a road information node knowledge graph by using the road information data; and then storing and performing associated representation on the vehicle information node knowledge graph, the travel information node knowledge graph, the emission information node knowledge graph and the vehicle travel basic data in the road information node knowledge graph in a graph database form according to the travel-vehicle data associated data set, the travel-vehicle-emission data associated data set and the road-travel data associated data set to obtain the vehicle travel emission knowledge graph in the region.
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