Regional carbon emission reduction monitoring method, device and medium

Abstract

The invention discloses a regional carbon emission reduction monitoring method, equipment and a medium. The method comprises the following steps: acquiring low-carbon travel data on each segmented road section in a monitoring region; determining travel parameters of each low-carbon travel object according to the low-carbon travel data; and determining travel carbon emission data in the monitoring area according to the set calculation model and the travel parameters, wherein the travel carbon emission data comprises carbon emission and carbon emission reduction. According to the method, the regional travel carbon emission and carbon emission reduction are obtained based on the road section space-time monitoring unit division result of the monitored regional road network and travel parameter statistics and mining of travel data of different travel modes, so that real-time and accurate monitoring of regional traffic carbon emission reduction is realized; the regional monitoring problem that large-scale traffic carbon emission detection mostly depends on macroscopic statistical data is solved, a manager is helped to carry out regional traffic carbon emission evaluation analysis on the basis, and construction of a novel green low-carbon urban traffic system is assisted.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technology, and in particular to a method, equipment and medium for monitoring regional carbon emission reduction. Background Art

[0002] At present, the total carbon emissions calculation methods mainly adopt two methods: "top-down" and "bottom-up". In recent years, with the improvement of information infrastructure construction, high-precision multi-source heterogeneous traffic data of multiple travel modes have been generated through macro-statistical data, which has made it possible to monitor transportation carbon emissions and carbon reduction.

[0003] However, due to the high requirements for collecting and processing multi-source heterogeneous travel data, current large-scale transportation carbon emissions detection mostly relies on macro-statistical data, which makes it difficult to support real-time and accurate monitoring of regional transportation carbon emissions reduction. Summary of the invention

[0004] The purpose of the present invention is to provide a regional carbon emission reduction monitoring method, device and medium to monitor regional transportation carbon emission reduction in real time and accurately.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] In a first aspect, the present invention provides a method for monitoring regional carbon emission reduction, the method comprising:

[0007] Based on the monitoring period, low-carbon travel data on each segmented road section in the monitoring area is obtained, wherein the low-carbon travel data represents traffic data of each low-carbon travel object on each segmented road section in the monitoring area;

[0008] Determine travel parameters of each low-carbon travel object according to the low-carbon travel data;

[0009] The regional travel carbon emission data within the monitoring area is determined according to the travel parameters, and the regional travel carbon emission data includes regional carbon emissions and regional carbon emission reductions.

[0010] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, determining the regional travel carbon emission data within the monitoring area according to the set travel parameters comprises:

[0011] Determine the road segment travel carbon emission data on each segmented road segment according to the travel parameters;

[0012] The road section travel carbon emission data of each segmented road section are accumulated to obtain the regional travel carbon emission data within the monitoring area.

[0013] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, the determining of the road segment travel carbon emission data on each segmented road segment according to the travel parameters comprises:

[0014] Determine the travel carbon emission data of each low-carbon travel object on the same split road section according to the travel parameters;

[0015] The travel carbon emission data of each low-carbon travel object on the same split road section are accumulated to obtain the section travel carbon emission data on the corresponding split road section.

[0016] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, the travel carbon emission data of any low-carbon travel object includes the carbon emission amount and carbon emission reduction amount of the low-carbon travel object;

[0017] The method of determining the travel carbon emission data of each low-carbon travel object on the same split road section according to the travel parameters includes:

[0018] Determine the baseline scenario carbon emissions and carbon emissions of each low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of each low-carbon travel object;

[0019] The difference between the baseline scenario carbon emissions and the carbon emissions is determined as the carbon emission reduction for the corresponding low-carbon travel object.

[0020] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, the low-carbon travel object includes a natural person, the carbon emissions of the natural person are walking carbon emissions, the carbon emission reduction of the natural person is walking carbon emission reduction, and the travel parameters of the natural person include walking distance; the baseline scenario carbon emissions of the natural person are walking baseline scenario carbon emissions;

[0021] Then, the determination of the baseline scenario carbon emissions and carbon emissions of each low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of each low-carbon travel object includes:

[0022] Determine the product of the walking distance and the baseline carbon emission factor as the walking baseline scenario carbon emissions;

[0023] Determine the product of the walking distance and the walking carbon emission factor as the walking carbon emission;

[0024] The difference between the walking baseline scenario carbon emissions and the walking carbon emissions is the walking carbon emissions reduction.

[0025] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, the low-carbon travel object includes a bicycle, the carbon emissions of the bicycle are bicycle carbon emissions, the carbon emission reduction of the bicycle is bicycle carbon emission reduction, and the travel parameters of the bicycle include bicycle travel distance; the baseline scenario carbon emissions of the bicycle include bicycle baseline scenario carbon emissions;

[0026] Then, the determination of the baseline scenario carbon emissions and carbon emissions of the low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of the low-carbon travel object includes:

[0027] Determine the product of the bicycle travel distance and the baseline carbon emission factor as the bicycle baseline scenario carbon emissions;

[0028] Determine the product of the bicycle travel distance and the bicycle carbon emission factor as the bicycle carbon emission;

[0029] The difference between the bicycle baseline scenario carbon emissions and the bicycle carbon emissions is the bicycle carbon emission reduction.

[0030] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, the low-carbon travel object includes ground public transportation, the carbon emissions of the ground public transportation are ground public transportation carbon emissions, the carbon emission reduction of the ground public transportation is ground public transportation carbon emission reduction, and the travel parameters of the ground public transportation include ground public transportation travel distance and the number of bus passengers; the baseline scenario carbon emissions of the ground public transportation include ground public transportation baseline scenario carbon emissions;

[0031] Then, the determination of the baseline scenario carbon emissions and carbon emissions of the low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of the low-carbon travel object includes:

[0032] Determine the product of the ground bus travel distance, the number of bus passengers and the benchmark carbon emission factor as the ground bus baseline scenario carbon emissions;

[0033] Determine the product of the ground bus travel distance, the number of bus passengers and the ground bus carbon emission factor as the ground bus carbon emission;

[0034] The difference between the carbon emissions of the ground bus baseline scenario and the carbon emissions of the ground bus is the carbon emission reduction of the ground bus.

[0035] Optionally, in the regional carbon emission reduction monitoring method provided by the present invention, the low-carbon travel object includes rail transit, the carbon emissions of the rail transit are rail transit carbon emissions, the carbon emission reduction of the rail transit is rail transit carbon emission reduction, and the travel parameters of the rail transit include rail transit travel distance and the number of rail transit passengers; the baseline scenario carbon emissions of the rail transit include rail transit baseline scenario carbon emissions;

[0036] Then, the determination of the baseline scenario carbon emissions and carbon emissions of the low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of the low-carbon travel object includes:

[0037] Determine the product of the rail transit travel distance, the number of rail transit passengers and the benchmark carbon emission factor as the rail transit baseline scenario carbon emissions;

[0038] Determine the product of the rail transit travel distance, the number of rail transit passengers and the rail transit carbon emission factor as the rail transit carbon emissions;

[0039] The difference between the rail transit baseline scenario carbon emissions and the rail transit carbon emissions is the rail transit carbon emissions reduction.

[0040] In a second aspect, the present invention provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the regional carbon emission reduction monitoring method as described in the first aspect.

[0041] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for monitoring regional carbon emission reduction as described in the first aspect is implemented.

[0042] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0043] The present invention provides a regional carbon emission reduction monitoring method, equipment and medium, which is based on the results of the spatiotemporal monitoring unit division of the road sections of the road network in the monitoring area, and the travel parameter statistics and mining based on the travel data of different travel modes, and then uses a pre-set calculation method to finally obtain the travel carbon emission data including travel carbon emissions and carbon emission reductions in the monitoring area, thereby realizing real-time and accurate monitoring of regional transportation carbon emission reduction. That is, since the present invention is based on the traffic data of each low-carbon travel in each divided section in the monitoring area, its granularity is much smaller than macro-statistical data, thereby overcoming the regional monitoring problem that large-scale transportation carbon emission detection mostly relies on macro-statistical data, and can use this as a basis to help managers conduct regional transportation carbon emission evaluation and analysis, and help build a new green and low-carbon urban transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic flow chart of a method for monitoring regional carbon emission reduction according to some embodiments of the invention;

[0046] Figure 2 Schematic diagram of the flow chart of the regional carbon emission reduction monitoring method according to some other embodiments of the present invention;

[0047] Figure 3 A schematic flow chart of a method for monitoring regional carbon emission reduction according to some further embodiments of the present invention;

[0048] Figure 4 Schematic diagram of the flow chart of the regional carbon emission reduction monitoring method according to other embodiments of the present invention;

[0049] Figure 5 A schematic flow chart of a method for monitoring regional carbon emission reduction according to some further embodiments of the present invention;

[0050] Figure 6 A schematic diagram of the structure of a regional carbon emission reduction monitoring device according to some embodiments of the present invention;

[0051] Figure 7 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] It can be understood that in the present invention, in order to solve the problem of scientifically monitoring regional travel carbon emission reduction based on multi-source heterogeneous traffic travel data, the present invention proposes a regional carbon emission reduction monitoring method based on big data. The method is based on the results of the spatiotemporal monitoring unit division of the road network in the monitoring area, and is based on the travel distance mining and travel number statistics of travel data of different travel modes. By establishing a calculation model for carbon emissions of different travel modes, baseline scenario carbon emissions and carbon emission reduction, the carbon emissions and carbon emission reduction of different travel modes are accumulated by road sections according to different travel modes, and finally the regional travel carbon emissions and carbon emission reduction are obtained. This is used as a basis to help managers conduct regional traffic carbon emission evaluation and analysis, and help build a new green and low-carbon urban transportation system.

[0056] In order to better understand the regional carbon emission reduction monitoring method provided by the present invention, it is described in detail below with reference to the accompanying drawings.

[0057] Figure 1 FIG. 1 is a flow chart of a method for monitoring regional carbon emission reduction according to some embodiments of the present invention. The method may be executed by a computer device, such as Figure 1 As shown, the method specifically comprises the following steps:

[0058] S110, based on the monitoring period, obtaining low-carbon travel data on each segmented road in the monitoring area, wherein the low-carbon travel data represents traffic data of each low-carbon travel object on each segmented road in the monitoring area.

[0059] S120: Determine travel parameters according to the low-carbon travel data.

[0060] S130, determining regional travel carbon emission data within the monitoring area according to the travel parameters, where the regional travel carbon emission data includes regional carbon emissions and regional carbon emission reductions.

[0061] Specifically, in an embodiment of the present invention, in order to achieve real-time and accurate monitoring of carbon emission reduction in a determined monitoring area, low-carbon travel data on each segmented road section in the monitoring area can first be acquired in real time according to a set monitoring period.

[0062] The monitoring period, that is, the monitoring unit can be set according to the actual situation, such as 15 minutes as a monitoring period.

[0063] The segmented road segments, that is, in order to realize the statistics of carbon emissions in the monitoring area, each network segment in the monitoring area can be divided into multiple segmented road segments, and then the low-carbon travel data in each segmented road segment can be counted according to the monitoring period.

[0064] For example, all segmented road segments may be divided based on the road segment display unit in the map.

[0065] It can be understood that before the implementation of the embodiment of the present invention, after the monitoring area is determined, the monitoring sub-areas, that is, the road sections, can be divided into the monitoring area according to the map that has been made in the monitoring area. That is, each road section displayed in the map is regarded as a divided road section to perform data statistics and carbon emission reduction monitoring.

[0066] The low-carbon travel data may represent traffic data of objects that have traffic behaviors on each segmented road section within the monitoring area during the current monitoring period.

[0067] For example, the traffic data of each low-carbon travel object whose destination falls on each segmented road section within the monitoring area may be represented.

[0068] Among them, Figure 5 As shown, the low-carbon travel objects that have travel behaviors may be natural persons, bicycles, non-motor vehicles, public transportation vehicles, etc. The public transportation vehicles may be ground buses and rail transportation, etc.

[0069] It is understood that the specific low-carbon travel mode of the travel object involves a means of transportation, which is not specifically limited in the present invention and can be flexibly adjusted according to the actual monitoring area or the development of the means of transportation, such as new energy vehicles.

[0070] Correspondingly, the low-carbon travel data may include natural person data, walking data, bicycle data, ground bus data, rail transit data, etc.

[0071] In practice, the low-carbon travel data, such as walking data, bicycle data, ground bus data, rail transit data, etc., refers to the traffic data of all low-carbon travel objects whose destination falls on the divided road.

[0072] For example, a natural person's walking data and bicycle data may specifically include location trajectory data; ground bus data may include bus location trajectory data and the number of passengers; and rail transit data may include rail transit train schedules and the number of passengers.

[0073] Furthermore, after the computer device obtains the low-carbon travel data of the above-mentioned objects, it can determine the travel parameters of the corresponding low-carbon travel objects according to the obtained low-carbon travel data.

[0074] For example, the corresponding travel distance and number of travelers are determined based on the trajectory data of walking data and bicycle data; the corresponding travel distance and number of travelers are determined based on the location trajectory data and number of passengers of ground buses; the corresponding travel distance and number of travelers are determined based on the location trajectory data and number of passengers of rail transit trains.

[0075] Finally, the regional travel carbon emission data including the regional carbon emissions and regional carbon emission reductions in the monitoring area during the current monitoring period can be determined based on the travel parameters corresponding to each travel object on each divided road section in the determined monitoring area, as well as the constructed calculation model, such as mathematical expressions, to achieve refined regional carbon emission reduction monitoring.

[0076] That is, in the embodiment of the present invention, based on the results of the spatiotemporal monitoring unit division of the road sections of the road network in the monitoring area, and based on the travel parameter statistics and mining of travel data of different travel modes, and then using a pre-set calculation method, the travel carbon emission data including travel carbon emissions and carbon emission reductions in the monitoring area are finally obtained, thereby realizing real-time and accurate monitoring of regional transportation carbon emission reductions. That is, since the present invention is based on the traffic data of each low-carbon travel in each divided road section in the monitoring area, its granularity is much smaller than macro statistical data, thereby overcoming the regional monitoring problem that large-scale transportation carbon emission detection mostly relies on macro statistical data, and can use this as a basis to help managers conduct regional transportation carbon emission evaluation and analysis, and help build a new green and low-carbon urban transportation system.

[0077] Alternatively, if Figure 2 As shown, in some embodiments of the present invention, the following steps may be performed to determine the regional carbon emission reduction amount within the monitoring area:

[0078] S131, determining the road segment travel carbon emission data on each segmented road segment according to the travel parameter.

[0079] S132, accumulating the road section travel carbon emission data of each segmented road section to obtain the regional travel carbon emission data in the monitoring area.

[0080] Specifically, in some embodiments of the present invention, for the calculation of travel carbon emission data within the monitoring area, the pre-divided road sections can be first used as units to calculate the section carbon emissions and section carbon emission reductions on each section, and then the section carbon emissions and section carbon emission reductions of each section can be summed up, and finally the regional travel carbon emission data within the monitoring area can be obtained.

[0081] Alternatively, if Figure 3 and Figure 5 As shown, in some embodiments of the present invention, in S131, the calculation of the road segment travel carbon emission data on each segmented road segment may specifically include the following steps:

[0082] S01, determining the travel carbon emission data of each low-carbon travel object on the same split road section according to the travel parameter.

[0083] S02, accumulating the travel carbon emission data of each low-carbon travel object on the same segmented road section to obtain the segment travel carbon emission data on the corresponding segmented road section.

[0084] Specifically, in this embodiment, for different low-carbon travel objects appearing on each segmented road section, such as natural persons, bicycles, ground public transportation vehicles and rail transit, the carbon emissions and carbon emission reductions of each low-carbon travel object on the same segmented road section can be calculated based on the travel parameters of each low-carbon travel object.

[0085] In practice, if Figure 4 and Figure 5 As shown, the calculation of the carbon emission data of each low-carbon travel object, that is, the carbon emission and carbon emission reduction of each low-carbon travel object, can be specifically carried out through the following steps:

[0086] S001, determine the product of travel parameters and baseline carbon emission factor as the baseline scenario carbon emissions.

[0087] S002, determine the product of the travel parameter and the carbon emission factor as the carbon emission amount.

[0088] S003, determine the difference between the baseline scenario carbon emissions and the elephant carbon emissions as the elephant carbon emission reduction.

[0089] Specifically, for the travel parameters of various types of transportation objects, the product of the travel parameters and the corresponding baseline carbon emission factors can be calculated first to serve as the baseline scenario carbon emissions corresponding to each low-carbon travel object. In addition, the product of the travel parameters and the corresponding base emission factors can be calculated to serve as the corresponding carbon emissions of each low-carbon travel object. Further, the difference between the baseline scenario carbon emissions and the carbon emissions can be determined as the carbon emission reduction of the corresponding low-carbon travel object.

[0090] The travel parameters may include the travel distance of each travel object, or in some embodiments, if the travel object is a public transportation vehicle, may also include the number of travelers.

[0091] For example, the travel parameters may include the travel distance of natural persons and bicycles, and may also include the travel distance and number of travelers of ground public transportation vehicles and rail transit vehicles.

[0092] Correspondingly, the benchmark carbon emission factor may be a baseline person-kilometer speed carbon emission factor, which may include a walking person-kilometer carbon emission factor, a bicycle person-kilometer carbon emission factor, a ground bus person-kilometer carbon emission factor, and a rail transit person-kilometer carbon emission factor.

[0093] The carbon emissions may include walking carbon emissions, bicycle carbon emissions, ground public transportation carbon emissions and rail transit carbon emissions, etc.

[0094] Finally, the carbon emissions of the baseline scenario can be subtracted from the carbon emissions, and the difference result can be used as the carbon emission reduction corresponding to the travel object, that is, the carbon emission reduction of walking, cycling, ground public transportation and rail transit can be determined.

[0095] It can be understood that the baseline scenario carbon emissions represent a standard scenario and are the criteria for determining whether a low-carbon travel object's travel has reduced carbon emissions. The carbon emissions reductions of all travel behaviors are based on the baseline scenario.

[0096] It can also be understood that the calculation model in some embodiments of the present invention can be understood as the mathematical calculation formula in the above steps, that is, the product of the travel parameters and the benchmark carbon emission factor, the product of the travel parameters and the carbon emission factor, and the calculation formula of the difference between the baseline scenario carbon emissions and the carbon emissions.

[0097] For example, Figure 5 As shown, for a natural person, that is, if he chooses to travel by walking, his carbon emissions are the carbon emissions from walking, his carbon emission reductions are the carbon emission reductions from walking, and his travel parameters include walking distance; his baseline scenario carbon emissions are the carbon emissions from walking baseline scenario.

[0098] Specifically, it includes:

[0099] S0001, determine the product of the walking distance and the baseline carbon emission factor as the walking baseline scenario carbon emissions.

[0100] S0002, determine the product of the walking distance and the walking carbon emission factor as the walking carbon emission, and the difference between the walking baseline scenario carbon emission and the walking carbon emission is the walking carbon emission reduction.

[0101] Specifically, the walking distance is first calculated based on the obtained location trajectory data, and then the walking carbon emissions, walking baseline scenario carbon emissions and walking carbon emission reductions of the walking section are calculated based on the walking distance. The walking carbon emissions, walking baseline scenario carbon emissions and walking carbon emission reductions of multiple natural persons whose end points fall on this section of road are calculated respectively and accumulated.

[0102] The following calculation model can be used to define and calculate walking baseline scenario carbon emissions, walking carbon emissions and walking carbon emission reductions:

[0103] Carbon emissions in walking baseline scenario = walking distance * baseline person-kilometer speed carbon emission factor;

[0104] Walking carbon emissions = walking distance * walking person-kilometer carbon emission factor;

[0105] Walking carbon emission reduction = walking baseline scenario carbon emission - walking carbon emission. For another example, for a low-carbon travel object of bicycle, the carbon emission of the bicycle is bicycle carbon emission, the carbon emission reduction of the bicycle is bicycle carbon emission reduction, the travel parameters of the bicycle include bicycle travel distance; the baseline scenario carbon emission of the bicycle includes bicycle baseline scenario carbon emission.

[0106] That is, through the following steps:

[0107] S0003, determine the product of the bicycle travel distance and the benchmark carbon emission factor as the bicycle baseline scenario carbon emissions.

[0108] S0004, determine the product of the bicycle travel distance and the bicycle carbon emission factor as the bicycle carbon emissions, and the difference between the bicycle baseline scenario carbon emissions and the bicycle carbon emissions is the bicycle carbon emission reduction.

[0109] Specifically, the bicycle carbon emission reduction calculation is to calculate the bicycle travel distance based on the obtained location trajectory data, and then calculate the bicycle carbon emissions, baseline scenario carbon emissions and carbon emission reduction for that section of the trip based on the bicycle travel distance. The bicycle carbon emissions, bicycle baseline scenario carbon emissions and bicycle carbon emission reduction are calculated for multiple bicycle trajectories whose end points fall on this section of road, and the results are accumulated.

[0110] For bicycle travel objects, after their traffic data is brought into the established calculation model, the bicycle baseline scenario carbon emissions, bicycle carbon emissions and bicycle carbon emission reduction are defined:

[0111] Bicycle baseline scenario carbon emissions = bicycle travel distance * baseline person-kilometer speed carbon emission factor;

[0112] Bicycle carbon emissions = bicycle travel distance * bicycle person-kilometer carbon emission factor;

[0113] Bicycle carbon emission reduction = bicycle baseline scenario carbon emissions - bicycle carbon emissions.

[0114] For another example, for the calculation of carbon emission reduction of ground public transportation, that is, the low-carbon travel object includes ground public transportation, the carbon emissions of the ground public transportation are the ground public transportation carbon emissions, the carbon emission reduction of the ground public transportation is the ground public transportation carbon emission reduction, and the travel parameters of the ground public transportation include the ground public transportation travel distance and the number of bus passengers; the baseline scenario carbon emissions of the ground public transportation include the ground public transportation baseline scenario carbon emissions.

[0115] That is, you can do it through the following steps:

[0116] S0005, determine the product of the ground bus travel distance, the number of bus passengers and the benchmark carbon emission factor as the ground bus baseline scenario carbon emissions.

[0117] S0006, determine the product of the ground bus travel distance, the number of bus passengers and the ground bus carbon emission factor as the ground bus carbon emissions, and the difference between the ground bus baseline scenario carbon emissions and the ground bus carbon emissions is the ground bus carbon emission reduction.

[0118] Specifically, the travel distance and number of passengers of ground buses are calculated based on the obtained bus location trajectory data, and the carbon emissions, baseline scenario carbon emissions and carbon emission reductions of the ground bus for that section of travel are calculated based on the ground bus travel distance and the number of passengers. The carbon emissions, baseline scenario carbon emissions and carbon emission reductions of multiple ground buses whose end points fall on this section of road are calculated separately and accumulated.

[0119] That is, for ground bus travel objects, after bringing their traffic data into the established calculation model, the ground bus baseline scenario carbon emissions, ground bus carbon emissions and ground bus carbon emission reduction are defined:

[0120] Carbon emissions of ground bus baseline scenario = ground bus travel distance * number of people on board * baseline person-kilometer speed carbon emission factor;

[0121] Ground bus carbon emissions = ground bus travel distance * number of people on board * ground bus passenger-kilometer carbon emission factor;

[0122] Carbon emission reduction of ground public transportation = carbon emissions of ground public transportation in the baseline scenario - carbon emissions of ground public transportation.

[0123] For another example, for rail transit travel, that is, the low-carbon travel object includes rail transit, the carbon emissions of the rail transit are the rail transit carbon emissions, the carbon emission reduction of the rail transit is the rail transit carbon emission reduction, and the travel parameters of the rail transit include the rail transit travel distance and the number of rail transit passengers; the baseline scenario carbon emissions of the rail transit include the rail transit baseline scenario carbon emissions.

[0124] That is, you can do it through the following steps:

[0125] S0007, determine the product of the rail transit travel distance, the number of rail transit passengers and the benchmark carbon emission factor as the rail transit baseline scenario carbon emissions.

[0126] S0008, determine the product of the rail transit travel distance, the number of rail transit passengers and the rail transit carbon emission factor as the rail transit carbon emissions, and the difference between the rail transit baseline scenario carbon emissions and the rail transit carbon emissions is the rail transit carbon emission reduction.

[0127] Specifically, the carbon emission reduction calculation for rail transit can be carried out by calculating the rail transit travel distance and the number of passengers according to the rail transit train timetable, and then calculating the carbon emissions, baseline scenario carbon emissions and carbon emission reductions for that section of rail transit based on the rail transit travel distance and the number of rail transit passengers. Multiple rail transit vehicles whose end points fall on this section of the road can be calculated separately, and the carbon emissions, baseline scenario carbon emissions and carbon emission reductions can be accumulated.

[0128] For rail transit travel objects, after bringing their traffic data into the established calculation model, the baseline scenario carbon emissions, rail transit carbon emissions and rail transit carbon emission reduction are defined:

[0129] Rail transit baseline scenario carbon emissions = rail transit travel distance * rail transit passengers * baseline person-kilometer speed carbon emission factor;

[0130] Rail transit carbon emissions = rail transit travel distance * number of rail transit passengers * rail transit passenger-kilometer carbon emission factor;

[0131] Carbon emissions reduction of rail transit = carbon emissions of rail transit baseline scenario - carbon emissions of rail transit.

[0132] It can be understood that the baseline person-kilometer speed carbon emission factor in the above calculation models is the baseline carbon emission factor.

[0133] It can also be understood that after determining the carbon emission reduction of each travel object on the same section through the above-mentioned calculation models, the carbon emissions and carbon emission reductions of various low-carbon travel modes of each travel object can be accumulated to obtain the carbon emissions and carbon emission reductions of low-carbon travel on this section.

[0134] Finally, in S132, the carbon emissions and carbon reductions of low-carbon travel in all divided road sections in the monitoring area are accumulated to obtain the carbon emissions and carbon reductions of low-carbon travel in the monitoring area, and finally realize the real-time and accurate monitoring of regional transportation carbon emission reduction, which can then be used as a basis to help managers conduct regional transportation carbon emission evaluation and analysis, and help build a new green and low-carbon urban transportation system.

[0135] It is understandable that Figure 5 The low-carbon travel objects involved in the shown embodiments, and the corresponding low-carbon travel data, are merely illustrative explanations of the regional carbon emission reduction monitoring method of the present invention, that is, the method provided by the present invention, when actually implemented, can flexibly adjust the processed low-carbon travel objects and their corresponding low-carbon travel data according to the location of the monitoring area or the actual development of low-carbon transportation vehicles, so as to achieve accurate monitoring of carbon emission reduction in the monitoring area.

[0136] That is, in the embodiment of the present invention, the following beneficial effects are achieved:

[0137] There is no need to conduct large-scale traffic surveys, saving a lot of manpower and time. As long as various types of travel data are available, regional travel carbon emissions can be monitored; among them, walking data and bicycle data are location trajectory data; ground bus data includes bus location trajectory data and the number of passengers; rail transit data includes rail transit train timetables and the number of passengers.

[0138] With the help of some methods in data mining, travel distance data is extracted from multi-source heterogeneous traffic data, and the regional travel carbon emission indicators are calculated through the baseline scenario carbon emissions, the carbon emissions of each travel mode and the carbon emission reduction calculation model of each travel mode. The model accuracy is higher than the traditional model based on macro-statistical data and simpler than actual measurements, thereby achieving the purpose of scientifically and accurately monitoring travel carbon emissions.

[0139] Establish a regional carbon emission statistics system that is "bottom-up" and highly adaptable. Comprehensively improve the statistical level of regional travel carbon emissions. By setting scientific division standards for monitoring units, statistically calculate regional transportation carbon emissions and carbon reductions by transportation mode, and improve the universality of the model.

[0140] On the other hand, Figure 6 As shown, some embodiments of the present invention further provide a regional carbon emission reduction monitoring device, the device comprising:

[0141] An acquisition module 210 is used to acquire low-carbon travel data on each segmented road section in the monitoring area based on a monitoring period, wherein the low-carbon travel data represents traffic data of each low-carbon travel object on each segmented road section in the monitoring area;

[0142] A first determination module 220, configured to determine travel parameters of each low-carbon travel object according to the low-carbon travel data;

[0143] The second determination module 230 is used to determine the travel carbon emission data in the monitoring area according to the travel parameters, and the travel carbon emission data includes carbon emission amount and carbon emission reduction amount.

[0144] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the second determination module includes:

[0145] A first determining unit 231 is used to determine the travel carbon emission data on each segmented road section according to the travel parameters;

[0146] The second determining unit 132 is used to accumulate the travel carbon emission data of each segmented road section to obtain the travel carbon emission data in the monitoring area.

[0147] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the first determination unit is specifically used to:

[0148] Determine the travel carbon emission data of each low-carbon travel object on the same split road section according to the travel parameters;

[0149] The travel carbon emission data of each low-carbon travel object on the same split road section are accumulated to obtain the travel carbon emission data on the corresponding split road section.

[0150] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the first determination unit is specifically used to:

[0151] Determine the baseline scenario carbon emissions and carbon emissions based on the baseline carbon emission factor, the carbon emission factor and the travel parameters;

[0152] The difference between the baseline scenario carbon emissions and the carbon emissions is determined as the carbon emission reduction.

[0153] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the low-carbon travel object includes a natural person, the travel carbon emission data includes walking carbon emissions and walking carbon emission reduction, and the travel parameter includes walking distance;

[0154] Then the first determining unit is specifically used for:

[0155] Determine the product of the walking distance and the baseline carbon emission factor as the walking baseline scenario carbon emissions;

[0156] The product of the walking distance and the walking carbon emission factor is determined as the walking carbon emission amount, and the difference between the walking baseline scenario carbon emission amount and the walking carbon emission amount is the walking carbon emission reduction amount.

[0157] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the low-carbon travel object includes a bicycle, the travel carbon emission data includes bicycle carbon emissions and bicycle carbon emission reduction, and the travel parameters include bicycle travel distance;

[0158] The first unit is specifically used for:

[0159] Determine the product of the bicycle travel distance and the baseline carbon emission factor as the bicycle baseline scenario carbon emissions;

[0160] The product of the bicycle travel distance and the bicycle carbon emission factor is determined as the bicycle carbon emissions, and the difference between the bicycle baseline scenario carbon emissions and the bicycle carbon emissions is the bicycle carbon emission reduction.

[0161] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the low-carbon travel object includes ground public transportation, the travel carbon emission data includes ground public transportation carbon emissions and ground public transportation carbon emission reduction, and the travel parameters include ground public transportation travel distance and the number of public transportation passengers;

[0162] The first unit is specifically used for:

[0163] Determine the product of the ground bus travel distance, the number of bus passengers and the benchmark carbon emission factor as the ground bus baseline scenario carbon emissions;

[0164] The product of the ground bus travel distance, the number of bus passengers and the ground bus carbon emission factor is determined as the ground bus carbon emissions, and the difference between the ground bus baseline scenario carbon emissions and the ground bus carbon emissions is the ground bus carbon emission reduction.

[0165] Optionally, in the regional carbon emission reduction monitoring device of some embodiments of the present invention, the low-carbon travel object includes rail transit, the travel carbon emission data includes rail transit carbon emissions and rail transit carbon emission reduction, and the travel parameters include rail transit travel distance and the number of rail transit passengers;

[0166] The first unit is specifically used for:

[0167] Determine the product of the rail transit travel distance, the number of rail transit passengers and the benchmark carbon emission factor as the rail transit baseline scenario carbon emissions;

[0168] The product of the rail transit travel distance, the number of rail transit passengers and the rail transit carbon emission factor is determined as the rail transit carbon emissions, and the difference between the rail transit baseline scenario carbon emissions and the rail transit carbon emissions is the rail transit carbon emission reduction.

[0169] In an exemplary embodiment, a computer device, namely a data collection device, is provided. The computer device may be a server or a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store video tag processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a meteorological data transmission method is implemented.

[0170] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0171] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0172] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0173] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0174] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0176] The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0177] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for monitoring regional carbon emission reduction, characterized in that: The method comprises: Based on the monitoring period, low-carbon travel data on each segmented road section in the monitoring area is obtained, wherein the low-carbon travel data represents traffic data of each low-carbon travel object on each segmented road section in the monitoring area; Determine travel parameters of each low-carbon travel object according to the low-carbon travel data; The regional travel carbon emission data within the monitoring area is determined according to the travel parameters, and the regional travel carbon emission data includes regional carbon emissions and regional carbon emission reductions.

2. The regional carbon emission reduction monitoring method according to claim 1, characterized in that: The step of determining the regional travel carbon emission data within the monitoring area according to the set travel parameters comprises: Determine the road segment travel carbon emission data on each segmented road segment according to the travel parameters; The road section travel carbon emission data of each segmented road section are accumulated to obtain the regional travel carbon emission data within the monitoring area.

3. The regional carbon emission reduction monitoring method according to claim 2, characterized in that: Determining the road segment travel carbon emission data on each segmented road segment according to the travel parameters includes: Determine the travel carbon emission data of each low-carbon travel object on the same split road section according to the travel parameters; The travel carbon emission data of each low-carbon travel object on the same split road section are accumulated to obtain the section travel carbon emission data on the corresponding split road section.

4. The regional carbon emission reduction monitoring method according to claim 3 is characterized in that: The travel carbon emission data of any low-carbon travel object includes the carbon emission and carbon emission reduction of the low-carbon travel object; The method of determining the travel carbon emission data of each low-carbon travel object on the same split road section according to the travel parameters includes: Determine the baseline scenario carbon emissions and carbon emissions of each low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of each low-carbon travel object; The difference between the baseline scenario carbon emissions and the carbon emissions of each of the low-carbon travel objects is determined as the carbon emission reduction of the corresponding low-carbon travel object.

5. The regional carbon emission reduction monitoring method according to claim 4 is characterized in that: The low-carbon travel object includes a natural person, the carbon emissions of the natural person are walking carbon emissions, the carbon emission reduction of the natural person is walking carbon emission reduction, and the travel parameters of the natural person include walking distance; the baseline scenario carbon emissions of the natural person are walking baseline scenario carbon emissions; Then, the determination of the baseline scenario carbon emissions and carbon emissions of each low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of each low-carbon travel object includes: Determine the product of the walking distance and the baseline carbon emission factor as the walking baseline scenario carbon emissions; Determine the product of the walking distance and the walking carbon emission factor as the walking carbon emission; The difference between the walking baseline scenario carbon emissions and the walking carbon emissions is the walking carbon emissions reduction.

6. The method for monitoring regional carbon emission reduction according to claim 4, characterized in that: The low-carbon travel object includes a bicycle, the carbon emissions of the bicycle are bicycle carbon emissions, the carbon emission reduction of the bicycle is bicycle carbon emission reduction, the travel parameters of the bicycle include bicycle travel distance; the baseline scenario carbon emissions of the bicycle include bicycle baseline scenario carbon emissions; Then, the determination of the baseline scenario carbon emissions and carbon emissions of the low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of the low-carbon travel object includes: Determine the product of the bicycle travel distance and the baseline carbon emission factor as the bicycle baseline scenario carbon emissions; Determine the product of the bicycle travel distance and the bicycle carbon emission factor as the bicycle carbon emission; The difference between the bicycle baseline scenario carbon emissions and the bicycle carbon emissions is the bicycle carbon emission reduction.

7. The method for monitoring regional carbon emission reduction according to claim 4, characterized in that: The low-carbon travel object includes ground public transportation, the carbon emissions of the ground public transportation are ground public transportation carbon emissions, the carbon emission reduction of the ground public transportation is ground public transportation carbon emission reduction, the travel parameters of the ground public transportation include ground public transportation travel distance and the number of bus passengers; the baseline scenario carbon emissions of the ground public transportation include ground public transportation baseline scenario carbon emissions; Then, the determination of the baseline scenario carbon emissions and carbon emissions of the low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of the low-carbon travel object includes: Determine the product of the ground bus travel distance, the number of bus passengers and the benchmark carbon emission factor as the ground bus baseline scenario carbon emissions; Determine the product of the ground bus travel distance, the number of bus passengers and the ground bus carbon emission factor as the ground bus carbon emission; The difference between the carbon emissions of the ground bus baseline scenario and the carbon emissions of the ground bus is the carbon emission reduction of the ground bus.

8. The method for monitoring regional carbon emission reduction according to claim 4, characterized in that: The low-carbon travel object includes rail transit, the carbon emissions of rail transit are rail transit carbon emissions, the carbon emission reduction of rail transit is rail transit carbon emission reduction, the travel parameters of rail transit include rail transit travel distance and the number of rail transit passengers; the baseline scenario carbon emissions of rail transit include rail transit baseline scenario carbon emissions; Then, the determination of the baseline scenario carbon emissions and carbon emissions of the low-carbon travel object according to the baseline carbon emission factor, carbon emission factor and travel parameters of the low-carbon travel object includes: Determine the product of the rail transit travel distance, the number of rail transit passengers and the benchmark carbon emission factor as the rail transit baseline scenario carbon emissions; Determine the product of the rail transit travel distance, the number of rail transit passengers and the rail transit carbon emission factor as the rail transit carbon emissions; The difference between the rail transit baseline scenario carbon emissions and the rail transit carbon emissions is the rail transit carbon emissions reduction.

9. A computer device, characterized in that: The computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the regional carbon emission reduction monitoring method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for monitoring regional carbon emission reduction according to any one of claims 1 to 8 is implemented.

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