CN116109459A - Method and device for determining carbon emission capacity of transportation equipment and electronic equipment - Google Patents
Method and device for determining carbon emission capacity of transportation equipment and electronic equipment Download PDFInfo
- Publication number
- CN116109459A CN116109459A CN202310119806.1A CN202310119806A CN116109459A CN 116109459 A CN116109459 A CN 116109459A CN 202310119806 A CN202310119806 A CN 202310119806A CN 116109459 A CN116109459 A CN 116109459A
- Authority
- CN
- China
- Prior art keywords
- carbon emission
- information
- emission intensity
- target
- time period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 507
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 505
- 238000000034 method Methods 0.000 title claims abstract description 81
- 230000009467 reduction Effects 0.000 claims description 61
- 238000012549 training Methods 0.000 claims description 45
- 238000013499 data model Methods 0.000 claims description 30
- 230000008569 process Effects 0.000 description 20
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 19
- 238000004364 calculation method Methods 0.000 description 17
- 238000005516 engineering process Methods 0.000 description 15
- 238000010586 diagram Methods 0.000 description 13
- 238000012545 processing Methods 0.000 description 12
- 238000003860 storage Methods 0.000 description 12
- 239000000446 fuel Substances 0.000 description 10
- 230000006870 function Effects 0.000 description 10
- 229910002092 carbon dioxide Inorganic materials 0.000 description 9
- 238000011161 development Methods 0.000 description 8
- 238000007726 management method Methods 0.000 description 7
- 238000012795 verification Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 5
- 238000012417 linear regression Methods 0.000 description 5
- 239000007788 liquid Substances 0.000 description 5
- 238000010606 normalization Methods 0.000 description 5
- 230000009471 action Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- 239000003949 liquefied natural gas Substances 0.000 description 4
- 238000010295 mobile communication Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 239000001569 carbon dioxide Substances 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 239000004973 liquid crystal related substance Substances 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000000295 fuel oil Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000004260 weight control Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G06Q50/40—
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Abstract
The embodiment of the application provides a method and a device for determining carbon emission capacity of transportation equipment and electronic equipment, wherein the method comprises the following steps: acquiring target key dimension information of target transportation equipment and first actual carbon emission intensity; acquiring the reference carbon emission intensity corresponding to the key dimension information in the corresponding preset time period according to the corresponding relation between the preset key dimension information and the reference carbon emission intensity in the preset time period; acquiring reference carbon emission intensity in a target time period according to the reference carbon emission intensity; determining at least two carbon emission capability boundary values corresponding to the type parameters based on the reference carbon emission intensity; the first actual carbon emission intensity is compared with a carbon emission capability boundary value to determine a carbon emission capability level of the target transportation device over a target time period. The carbon emission capacity of the transportation equipment can be evaluated more accurately.
Description
Technical Field
The present disclosure relates to the field of transportation, and in particular, to a method and an apparatus for determining carbon emission capability of a transportation device, and an electronic device.
Background
The country sets a 'two carbon' goal, and the traffic field promotes work to promote the development of domestic shipping and the full utilization of new energy technology, wherein the assessment of the carbon emission level plays a vital assessment role.
In the related art, the carbon emission amount may be calculated based on the sailing distance of a transport means such as a ship.
However, in the current scheme, the carbon emission capacity is measured only by a method of calculating the carbon emission amount, so that analysis of carbon-related data is too coarse, which results in inaccurate control of carbon emission conditions of ships, inaccurate control of resources, and excessive consumption of resources.
Disclosure of Invention
The embodiment of the application provides a method for determining carbon emission capacity of transportation equipment, which aims to solve the problems that in the related technology, the carbon emission condition of a ship is inaccurate, and further, deviation of decision can be caused, the control of resources is inaccurate, and the consumption of resources is overlarge easily.
Correspondingly, the embodiment of the application also provides a carbon emission capacity determining device of the transportation equipment, electronic equipment and a storage medium, which are used for guaranteeing the implementation and application of the method.
To solve the above-mentioned problems, embodiments of the present application disclose a method for determining carbon emission capability of a transportation device, the method comprising:
Acquiring target key dimension information of target transportation equipment and first actual carbon emission intensity in a target time period;
acquiring the corresponding reference carbon emission intensity of the key dimension information in the corresponding preset time period according to the corresponding relation between the preset key dimension information and the reference carbon emission intensity in the preset time period;
acquiring reference carbon emission intensity corresponding to the key dimension information type information in a target time period according to the reference carbon emission intensity;
determining at least two carbon emission capability boundary values corresponding to the target key dimension information based on the reference carbon emission intensity;
and comparing the first actual carbon emission intensity with the carbon emission capability boundary value to determine the carbon emission capability level of the target transportation equipment in a target time period.
The embodiment of the application discloses a method for acquiring a corresponding relation, which comprises the following steps:
acquiring historical sample data of a plurality of transportation devices within a preset time period; the historical sample data comprises transport-related information of multiple dimensions of the transport equipment and second actual carbon emission intensity of the transport equipment in the preset time period; the transportation related information of multiple dimensions includes the critical dimension information;
Training a preset model based on the historical sample data to obtain a first data model;
determining first input information of a plurality of dimensions according to transportation related information of the plurality of dimensions of the transportation equipment aiming at the key dimension information respectively; wherein the first input information of a target dimension of the plurality of dimensions comprises an average value of transportation related information, the target dimension comprising at least a portion of dimensions other than the critical dimension information;
and carrying first input information of multiple dimensions of the transportation equipment into the first data model, obtaining reference carbon emission intensity corresponding to the key dimension information, and constructing a corresponding relation between the key dimension information and the reference carbon emission intensity.
The embodiment of the application discloses a transportation equipment carbon emission capacity determining device, the device includes:
the first strength acquisition module is used for acquiring target key dimension information of target transportation equipment and first actual carbon emission strength in a target time period;
the second intensity acquisition module is used for acquiring the reference carbon emission intensity corresponding to the target key dimension information in the preset time period according to the corresponding relation between the preset key dimension information and the reference carbon emission intensity in the preset time period; the corresponding relation between the key dimension information and the reference carbon emission intensity in the preset time period is obtained by training historical sample data of a plurality of transport equipment in the preset time period;
The third intensity acquisition module is used for acquiring reference carbon emission intensity corresponding to the target key dimension information in a target time period according to the reference carbon emission intensity;
the boundary determining module is used for determining at least two carbon emission capacity boundary values corresponding to the target key dimension information based on the reference carbon emission intensity;
and the capacity determining module is used for comparing the first actual carbon emission intensity with the carbon emission capacity boundary value and determining the carbon emission capacity grade of the target transportation equipment in a target time period.
The embodiment of the application discloses a device for acquiring a corresponding relation, which comprises:
the sample acquisition module is used for acquiring historical sample data of a plurality of transport equipment in a preset time period; the historical sample data includes transportation related information for a plurality of dimensions of the transportation device; the transportation related information of multiple dimensions includes the critical dimension information;
the model training module is used for training a preset model based on the historical sample data to obtain a first data model;
the input information adjustment module is used for determining first input information of multiple dimensions according to transportation related information of the multiple dimensions of the transportation equipment aiming at the key dimension information respectively; wherein the first input information of a target dimension of the plurality of dimensions comprises an average value of transportation related information, the target dimension comprising at least a portion of dimensions other than the critical dimension information;
And the corresponding relation determining module is used for bringing first input information of multiple dimensions of the transportation equipment into the first data model, obtaining the reference carbon emission intensity corresponding to the key dimension information, and constructing the corresponding relation between the key dimension information and the reference carbon emission intensity.
The embodiment of the application also discloses electronic equipment, which comprises: a processor; and a memory having executable code stored thereon that, when executed, causes the processor to perform a method as one or more of the above-described methods in embodiments of the present application.
One or more machine-readable media having stored thereon executable code that, when executed, causes a processor to perform a method as one or more of the embodiments of the present application are also disclosed.
Compared with the related art, the embodiment of the application comprises the following advantages:
according to the embodiment of the application, the corresponding relation between different key dimension information and the reference carbon emission intensity in the preset time period can be built in advance, then, for the target transportation equipment in the target period to be evaluated, the reference carbon emission intensity corresponding to the target key dimension information of the target transportation equipment is obtained by utilizing the corresponding relation, and then the reference carbon emission intensity in the target period is obtained based on the reference carbon emission intensity, so that the carbon emission capacity boundary value of the corresponding type of transportation equipment in the target time period can be determined according to the reference carbon emission intensity, the actual carbon emission intensity of the transportation equipment can be compared with the carbon emission capacity boundary value of the type of transportation equipment, and finally, the carbon emission capacity grade of the transportation equipment in the target time period is determined. According to the method, the carbon emission levels of various transportation equipment in a target time period can be evaluated more accurately, the relatively uniform reference carbon emission intensity can be used as a calculation reference, the more standardized carbon emission level can be obtained, enterprises or units can make deeper decision deployment based on the more accurate carbon emission intensity result, differential management and service can be carried out on ships with different carbon intensity levels, the technical blank that the carbon emission capacity of the transportation equipment is evaluated too singly and simply in the related technology is enriched, and the energy development decision based on the carbon emission capacity is facilitated to develop towards a more accurate and scientific direction.
Drawings
FIG. 1 is a system architecture diagram of a method for determining carbon emission capacity of a transportation device according to an embodiment of the present application;
fig. 2 is a step flowchart of a method for obtaining a correspondence provided in an embodiment of the present application;
FIG. 3 is a flow chart of steps of a method for determining carbon emission capacity of a transportation device according to an embodiment of the present application;
FIG. 4 is a graph illustrating carbon emissions capability boundaries provided by embodiments of the present application;
FIG. 5 is a block diagram of a transport equipment carbon emission capability determination device of an embodiment of the present application;
fig. 6 is a block diagram of a correspondence acquiring apparatus provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of an apparatus according to an embodiment of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
For a better understanding of the present application, the concepts to which the present application relates are described below:
carbon emission intensity: the ratio of the total mass of CO2 (M) emitted by the transport means to the total work of transport (W) assumed during a certain period of time.
Reduction coefficient: the amount of attenuation in carbon emission intensity between different time periods.
The method for determining the carbon emission capacity of the transportation equipment can be applied to the scene of carbon emission capacity assessment of ships, so that the carbon emission level of enterprises is fully known according to the carbon emission capacity, accurate decisions are made for the utilization of new energy sources according to the carbon emission level, the situation that the utilization of the new energy source technology is behind due to insufficient carbon emission level mastering is avoided, and the whole energy consumption of the enterprises can be reduced; in addition, the method for determining the carbon emission capacity of the transportation equipment can also be applied to other aspects of resource utilization decisions, such as full popularization and implementation of carbon reduction decisions of certain units, utilization of new energy technologies by enterprises, standardized management of transportation industries such as ship enterprises by certain units and the like, so that the overall energy consumption of the transportation equipment is reduced. Of course, other scenarios may be used, such as aircraft carbon emission capability, automobile carbon emission capability, etc., which are not limited by embodiments of the present application.
Referring to fig. 1, a system architecture diagram of a method for determining carbon emission capability of a transport apparatus, such as a ship, according to an embodiment of the present application is shown. The determination of the carbon emission capability of the transport device may be performed in the server a, and after the determination of the carbon emission capability of the transport device, the determination may be sent to the server B, and the server B may perform subsequent processing. Of course, the determination process for the carbon emission capability and the subsequent processing process after the determination may be performed in the same server, which is not limited by the embodiment of the present application. As in the server a in fig. 1, the transportation device carbon emission capability determination process may include the following processes:
Step S11, training transportation related information of each ship in the reference year to obtain a first data model;
in the embodiment of the present application, one year may be selected as the reference year, and then historical sample data of each ship of the reference year is acquired from the cooperating ships or enterprises.
Taking a ship as an example, historical sample data of various transportation devices in a preset time period can be collected, wherein the historical sample data can comprise transportation related information such as the type, the size, the tonnage, the maximum speed, the navigation time, the berth-keeping time and the like of the ship of the transportation devices in the preset time period, and historical data such as carbon emission intensity and the like. And carrying out regression processing on the historical sample data to obtain a first data model with the dependent variable of carbon emission intensity and the independent variable of a plurality of pieces of transportation related information such as ship types, wherein the first data model can be a regression function, and the regression function obtains the mapping function relation between different pieces of transportation related information and the ship carbon emission intensity through regression processing. The transportation related information is used for reflecting information related to carbon emission of the transportation equipment in the transportation process, and comprises attribute information of the transportation equipment and driving data in the transportation process. The attribute information of the transportation equipment is exemplified by the inherent attributes of the ship, such as the type, size, tonnage, maximum speed, etc. The traveling data during the transportation is exemplified by a ship, such as a ship's travel time, a time when the ship stays at a berth, and the like.
Step S12, obtaining reference carbon emission intensity corresponding to various kinds of information based on the first data model;
in the embodiment of the application, on the basis of obtaining the mapping function relation between different transportation related information and the ship carbon emission intensity, the average carbon emission intensity of the target ship in the preset time period, namely the reference carbon emission intensity, can be determined by obtaining the average value of the different transportation related information and the type information of the target ship, and the overall carbon emission capacity of specific types of transportation equipment in a complex environment can be reflected more accurately.
The type information may be type information of the ship, or the type information may be a combination of type information of the ship and tonnage. Such as bulk cargo vessels, liquid cargo vessels, container vessels, gas transport vessels, LNG (liquefied natural gas) vessels, roll-on and roll-off vessels, grocery vessels, refrigerated cargo vessels, and utility vessels, among others. Of course, other restrictions may be added to the type information, such as a bulk carrier of a certain type, a liquid carrier of a certain type, etc., which are not limited in the embodiments of the present application.
Step S13, acquiring at least two carbon emission capacity boundary values of the ship of the type of the ship A under the target year based on the reference carbon emission intensity corresponding to each type of information;
For the ship a to be evaluated in a certain target year, the carbon emission capability boundary value of the ship of the type to which the ship a belongs in the target year may be obtained based on the reference carbon emission intensity.
In the embodiment of the application, for different kinds of ships, the carbon emission capacity boundary value of the kind is determined according to the reference carbon emission intensity of the corresponding kind, and further, the carbon emission capacity of the transportation equipment is accurately divided from multiple dimensions, so that the transportation equipment can accurately acquire the carbon emission capacity relative to the transportation equipment under complex environmental factors through comparison with the carbon emission capacity boundary value, and the technical blank that the carbon emission capacity of the transportation equipment is estimated to be too single and simple in the related technology is enriched.
Step S14, obtaining a first actual carbon emission intensity of the vessel a.
In the embodiment of the application, for the ship a to be evaluated in a certain target year, the first actual carbon emission intensity of the ship a in the target year may be calculated based on the transportation related information of the ship in the target year.
In practical application, the carbon dioxide discharge amount data and the power consumption data of the ship a in the target year may be acquired, and then the first actual carbon discharge intensity of the target transportation device may be calculated from the carbon dioxide discharge amount and the power consumption. Wherein the carbon dioxide emissions may refer to the total mass of the total amount of CO2 (carbon dioxide) emissions produced by all types of fuel consumed on the target transportation device during the target time period, determined by the conversion coefficient between the mass of fuel and the mass of CO2 and the total mass of fuel consumed; the power consumption is determined by the loading capacity of the ship and the sailing distance of the target transportation device in the target time period. The target time period may be in units of years, and of course may be in units of other time, such as months, quarters, half-years, etc., which are not limited by the embodiments of the present application.
Specifically, taking the calculation of the actual carbon emission intensity of the ship as an example, it can be calculated according to the following formula:
attained CII＝M/W，(1)
W＝C×D t ，(3)
wherein, the attained CII represents the actual carbon emission intensity of the target vessel in a time period; m represents the total mass of CO2 emissions of the target vessel for a period of time. The time period is, for example, one year, and the present application is not limited to a specific time of the target time period.
W represents the total power consumption of the navigation of the target ship in a time period; n represents that N fuel oils are consumed by a target ship in a time period; j represents the j-th fuel consumed by the target vessel during a period of time, and correspondingly M j Representing the mass of CO2 emitted by the target ship consuming the jth fuel during a period of time, C j Indicating j-th fuel and CO2 emissionConversion coefficient of quantity, FC j Indicating the total mass of j-th fuel consumed by the target vessel during a time period.
D t Representing the total distance travelled corresponding to fuel consumption over a period of time; c represents the carrying capacity of a ship, and in particular, for bulk cargo ships, liquid cargo ships, container ships, gas transport ships, LNG ships, roll-on/roll-off ships, grocery ships, refrigerated cargo ships, and utility ships, etc., a weight-on-ton (DWT) can be used as a measure of the carrying capacity; for luxury mail wheels, roll-on and roll-off vessels (vehicle transport vessels) and roll-on and roll-off passenger vessels, total tons (GT) can be used as a measure of carrying capacity.
The conversion coefficients of different types of fuel and CO2 emission are shown in the following table I:
list one
Alternatively, taking a ship as an example, in calculating the first actual carbon emission intensity for different types of ships, the calculation may be performed according to different carrying capacity measurement standards, as follows:
wherein AER represents the actual carbon emission intensity of the carrying capacity in terms of the load tons, DWT represents the carrying capacity of the ship in terms of the unit of measurement of the load tons, and can be calculated for cargo ships of types other than those of the luxury mail wheels, roll-on cargo ships, roll-on guest ships and the like; cgDIST represents the actual carbon emission intensity in terms of carrying capacity, GT represents the ship carrying capacity in total tons, and can be calculated for types of ships such as luxury mail wheels, roll-on-roll ships (vehicle transport ships), roll-on-roll passenger ships, and the like.
And S15, comparing the first actual carbon emission intensity with the carbon emission energy boundary value to obtain the carbon emission capacity grade of the ship A.
After the carbon emission capability boundary value is confirmed, the first actual carbon emission intensity is compared with the carbon emission capability boundary value, and the carbon emission capability level of the target transportation equipment in the target time period is determined. By comparing the first actual carbon emission intensity of the ship A with the carbon emission capacity boundary value of the ship of the type to which the ship A belongs, the actual carbon emission capacity grade of the target transportation equipment under the influence of various complex factors can be accurately reflected, the clear contrast with the transportation equipment of the same type under various factors is further formed, the grasp of the carbon emission capacity of the transportation equipment enters a higher-dimensional level, and the accuracy of evaluating the carbon emission capacity of the transportation equipment is greatly improved.
The steps S11 to S15 may be performed in the server a, however, the server architecture for performing the steps is not limited in this application, and the steps may not be performed in one server, for example, the steps S11 to S12 may be performed in one server C, the steps S13 to S15 may be performed in another server D, and after the server C obtains the reference carbon emission intensity corresponding to each type of information, the reference carbon emission intensity corresponding to each type of information may be sent to the server D to perform the steps S13 to S15 to determine the carbon emission capability of the ship a.
Step S16, instructing the ship to rectify or issue a corresponding certificate.
After the server a determines the carbon emission capability level of a certain ship, the carbon emission capability level may be sent to the server B, where the server B may uniformly manage the service end of the ship, or may be the service end of the company to which the ship belongs. Then, the server B can perform subsequent operations based on the carbon emission capability level, for example, sending a rectification instruction to the ship under the condition that the carbon emission capability level is low, and sending a certificate of the corresponding capability to the ship under the condition that the carbon emission capability level reaches the standard; or in case the carbon emission capability level of the vessel is excellent, the identity of the vessel is given a certain authority. The present embodiment is not limited to this subsequent operation.
According to the embodiment of the application, the corresponding relation between different key dimension information and the reference carbon emission intensity in the preset time period can be built in advance, then, for the target transportation equipment in the target period to be evaluated, the reference carbon emission intensity corresponding to the target key dimension information of the target transportation equipment is obtained by utilizing the corresponding relation, and then the reference carbon emission intensity in the target period is obtained based on the reference carbon emission intensity, so that the carbon emission capacity boundary value of the corresponding type of transportation equipment in the target time period can be determined according to the reference carbon emission intensity, the actual carbon emission intensity of the transportation equipment can be compared with the carbon emission capacity boundary value of the type of transportation equipment, and finally, the carbon emission capacity grade of the transportation equipment in the target time period is determined. According to the method, the carbon emission levels of various transportation equipment in a target time period can be evaluated more accurately, the relatively uniform reference carbon emission intensity can be used as a calculation reference, the more standardized carbon emission level can be obtained, enterprises or units can make deeper decision deployment based on the more accurate carbon emission intensity result, differential management and service can be carried out on ships with different carbon intensity levels, the technical blank that the carbon emission capacity of the transportation equipment is evaluated too singly and simply in the related technology is enriched, and the energy development decision based on the carbon emission capacity is facilitated to develop towards a more accurate and scientific direction.
It should be noted that, in the embodiment of the present application, the process of obtaining the type information, the historical sample data and other information, signals or data used is performed under the premise of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Referring to fig. 2, a step flowchart of a method for obtaining a correspondence provided in an embodiment of the present application is shown, including:
Wherein the historical sample data further includes a second actual carbon emission intensity of the transport device over the preset time period.
The second actual carbon emission intensity refers to actual carbon emission intensity of a plurality of transportation devices in a preset time period, the calculation mode can refer to the calculation mode of the first actual carbon emission intensity, and the two calculation modes differ in data only in time periods.
The historical sample data may be obtained from published transport equipment data sets or voyage records of the transport equipment. Alternatively, the target transportation device may be a ship.
Optionally, in an embodiment of the present application, the critical dimension information includes: in the case of the type information of the ship and the tonnage information of the ship, the transportation related information of the plurality of dimensions of the target dimension further includes: at least one of a size of the vessel, a maximum speed of the vessel, a voyage time of the vessel, and a time for the vessel to stay at the berth. Of course, the key dimension information includes: in the case of ship type information, the transportation related information of the plurality of dimensions of the target dimension further includes: at least one of a size of the vessel, a tonnage of the vessel, a maximum speed of the vessel, a sailing time of the vessel, and a time for which the vessel is at berth.
Of course, in the case where the critical dimension information is type information, the transportation-related information of a plurality of dimensions may further include: at least one of a size of the vessel, a tonnage of the vessel, a maximum speed of the vessel, a sailing time of the vessel, and a time for which the vessel is at berth.
It should be noted that, the transportation device may be another device, for example, the transportation device may be an automobile, the key dimension information may include an automobile model, and the transportation related information may include: the vehicle displacement, the vehicle driving speed, the driving mileage of the vehicle on various roads, and the like, which are not limited in the embodiments of the present application.
And training a predetermined regression model, namely a preset model, by taking the second actual carbon emission intensity in the historical sample data as a dependent variable and taking transport-related information data independent variables of multiple dimensions, so as to obtain a first data model. In the embodiment of the present application, the preset model may specifically include a ridge regression model, a lasso (lasso) regression model, a linear regression model, or other regression models, which is not limited in this application.
Optionally, in another embodiment of the present application, training the preset model based on the historical sample data in step 202 to obtain the first data model may include:
step 2021, substituting the historical sample data into the preset model to obtain the dimension parameter of the dimension.
Step 2022, determining a sum of a least square term and a penalty term corresponding to the preset model based on the dimension parameter.
And step 2023, taking the dimension parameter as the dimension parameter of the preset model to obtain the first data model when the sum of the least square term and the penalty term meets a preset condition.
The preset condition is that the sum is minimum or the sum meets a preset value.
In the method described in sub-steps 2031-2033, the preset model is a ridge regression model, and the parameters of the information variables associated with different dimensions, i.e., different transportation, are obtained by training the preset model using the historical sample data, and a regression function of the actual carbon emission intensity is obtained as follows:
wherein y represents the second actual carbon emission intensity, p represents p-dimensional transportation related information, and p is a positive integer; beta j Parameters representing transport-related information for the j-th dimension, beta 0 Is constant; x is x j Is the firstj pieces of transportation related information.
Meanwhile, in order to reduce the error of the regression function in the formula (6), the historical sample data is divided into a training set and a verification set, the following minimum component term (7) and penalty term (8) are added in the training process of the preset model, the sum of the formula (7) and the formula (8) is calculated based on the verification set to serve as a standard for measuring the error of the formula (6), and when the sum of the minimum square component term and the penalty term is minimum or meets a preset value, the current dimension number is determined to be the dimension parameter of the preset model:
wherein N represents the number of verification samples, N is a positive integer, and i represents the ith verification sample used for verifying the preset model in the training process; lambda is a preset weight for balancing the weight relationship between the least squares term and the penalty term.
Of course, in embodiments of the present application, historical sample data may also be trained using a lasso (lasso) regression model and a linear regression model that are different from sub-steps 2021-2023.
Optionally, in order to verify errors between different regression modes, in this embodiment, the historical sample data may be further divided into a training set and a verification set, and the difference between different regression modes may be calculated by using the verification set to compare the errors of different regression methods, specifically, the errors of different regression modes may be calculated by calculating the square of the difference between the predicted value and the true value of each verification sample, and then summing and taking the average value, that is, the Mean Square Error (MSE).
In contrast, the ridge regression method adds the least square term and the penalty term as regression comparison in the training process, and performs weight control between the least square term and the penalty by the parameter lambda, so that the error direction of the parameter can be adjusted more on the basis of smaller error, and compared with a lasso (lasso) regression model and a linear regression model, the accuracy is higher, the convergence direction can be controlled in the participation of multidimensional parameters, the error of the first data model is smaller, and the controllability in the training process is stronger.
In the examples of the present application, beta 0 May be set to 0. After regression training is performed by obtaining the above formulas (6), (7) and (8), the first data model:
y＝β 1 x 1 +β 2 x 2 +…β k (9-0)
wherein x is i As an argument, as transport equipment-related information of the ith dimension, beta i And y is a dependent variable which is a reference carbon emission intensity, and is a parameter of the ith dimension.
It should be noted that, taking 1000 ships as training data, training with the ridge regression model, lasso (lasso) regression model and linear regression model, and performing error evaluation by using mean square error after training is to calculate the square of the difference between the predicted value and the true value of each sample, and then summing and averaging. The formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
mean square error, y i Represents the i-th true value, f (x i ) Representing input x i The obtained predicted value, m, represents the number of samples. The training effect of using the data of 1000 ships as training data is as follows:
| selecting and usingMethod | Linear regression | Ridge regression | Lasso regression |
| MSE | 2.234 | 0.234 | 0.678 |
The comparison shows that the mean square error of the collar regression is minimum, and the prediction is more accurate.
Taking a ship as an example, the key dimension information may be type information of the ship and tonnage information of the ship, and the corresponding target dimension may include at least one of dimensions of a size of the ship, a maximum speed of the ship, a sailing time of the ship, a time when the ship stays at a berth, and the like.
As an example, time information of a ship a having a tonnage of 5000 tons staying at a berth for 5 years, time information of a ship's voyage, and time information of a ship B having a tonnage of 10000 tons staying at a berth for 5 years, voyage time information of a ship, an average time of a ship a and a ship B staying at a berth and an average voyage time of a ship are calculated, and the average time of a ship staying at a berth and the average voyage time of a ship are determined as first input information.
Among these, the type information of the ship such as bulk cargo ship, liquid cargo ship, container ship, gas transport ship, LNG ship, roll cargo ship, grocery ship, refrigerated cargo ship, and dual-purpose ship, etc.
Of course, the key dimension information may also be type information of the ship, and the corresponding target dimension may include at least one of dimensions of a size of the ship, a maximum speed of the ship, tonnage information of the ship, navigation time of the ship, a time when the ship stays at a berth, and the like.
104, substituting first input information of multiple dimensions of the transportation equipment into the first data model, obtaining reference carbon emission intensity corresponding to the key dimension information, and constructing a corresponding relation between the key dimension information and the reference carbon emission intensity; the target dimension is at least part of dimensions except the key dimension information;
for example, in the case that the critical dimension information is type information, for other dimensions than the dimension of the type information in the preset time period, an average value of data in a certain dimension of each ship belonging to the type information may be taken, and then the average value of the type information x and the other dimensions is substituted into formula (9-0), to obtain the following formula (9-1):
wherein y represents a type x 1 Reference carbon emission intensity, beta of transport equipment 1 -β k Regression coefficients corresponding to different transportation related information are represented,
representing a plurality of x 1 And the average value of each transportation related information parameter of the type of transportation equipment except the type information in a preset time period is respectively calculated. Of course->
The parameters of the plurality of transport devices may be directly averaged, respectively, or may be obtained by other operation methods such as a weighting operation, which is not limited in this application.
Thus, the reference carbon emission intensity of the ship with the type information can be calculated by the formula (9-1), so that the correspondence between the key dimension information and the reference carbon emission intensity can be constructed.
The reference carbon emission intensity of the target transportation equipment, which is obtained based on the historical sample data of the plurality of transportation equipment and according to the key dimension information of the target transportation equipment, can accurately reflect the total carbon emission capacity of the specific type of transportation equipment in a certain time under the multi-dimensional factors, and the reference carbon emission intensity can be used as a reference to more deeply and accurately reflect the carbon emission capacity of the transportation equipment.
For example, when the key dimension information is type information and tonnage information, taking an average value of data of each ship in a certain dimension of the type information and tonnage information for other dimensions except the type information and tonnage information dimension in the preset time period, and substituting the average value of the type information x and the other dimensions into a formula (9-0) to obtain the following formula (9-2);
wherein y represents a type x 1 Tonnage is x 2 Reference carbon emission intensity, beta of transport equipment 1 -β k Regression coefficients corresponding to different transportation related information are represented, 
Represented as x 1 Of type and x 2 Multiple transport facilities of tonnage divided by x 1 And x 2 The other transportation related information parameters are respectively the average value in the preset time period. Of course->
The parameters of the plurality of transportation devices can be directly averaged respectively, or can be obtained through other operation modes such as weighting operation, which is not limited in the application
Thus, the reference carbon emission intensity of the ship corresponding to the type information and tonnage together can be calculated through the formula (9-2), so that the corresponding relation between the key dimension information and the reference carbon emission intensity can be constructed.
Optionally, in order to reduce the calculation amount in the correspondence acquiring process described in the steps 101-104 and improve the model training and data processing efficiency, before step 202, the following processing may be performed on the transportation related information of the target dimension and the type information of the plurality of transportation devices:
step S21, carrying out one-bit effective coding on the type information to obtain type coding information;
step S22, normalizing the transportation association information in the first dimension to obtain normalized association information; the first dimension includes at least a portion of the dimensions other than the type information;
For example, the plurality of dimensions may include at least some or all of ship type information, ship tonnage information, ship size, ship maximum speed, ship travel time, ship berthing time, etc., and the first dimension may be at least some of the dimensions including ship tonnage information, ship size, ship maximum speed, ship travel time, ship berthing time, etc., in addition to the ship type information.
Correspondingly, in step 202, training the preset model based on the historical sample data to obtain a first data model may include:
training a preset model based on the type association information and the normalized association information of the transportation equipment to obtain a first data model.
As an example, there are 4 types, for example, 1000 for a container ship, 0100 for a particular model of container ship, and 0010 and 0001 for the other two types, respectively.
The normalization refers to performing dimension normalization processing on the same transportation related information, for example, unifying the loading capacities of different units into the same unit.
The normalization ratio is calculated by normalizing the transportation related information of each dimension into a 0-1 interval.
Mode one:
(x-mean(x)/std(x)
where x is transportation related information of a certain dimension of a certain ship, mean (x) is an average value of transportation related information of the dimension x of each ship of the key dimension information, and std (x) is a variance of transportation related information of the dimension x of each ship.
Mode two:
(max(x)-x)/(max(x)-min(x))
wherein x is transportation related information of a certain dimension of a certain ship, max (x) is the maximum value of the transportation related information of the dimension x of each ship of the key dimension information, and min (x) is the minimum value of the transportation related information of the dimension x of each ship.
Of course, the embodiments of the present application are not limited to the normalization manner.
Then, after one-bit effective coding is carried out on the type information and other information except the type information is normalized, training is carried out again based on the processed information, and the calculated amount is reduced.
In the related art, the carbon emission capacity level of a ship may be evaluated by the carbon emission amount. However, the method is undoubtedly single and extensive, so that the carbon emission capability of the outermost layer of the transportation equipment can be mastered, the determination of the carbon emission capability is not accurate enough, further decision or evaluation can not be made based on the carbon emission capability determined under the condition, and the application value of the carbon emission capability evaluation is greatly reduced.
In the actual resource utilization process, the carbon emission of the transportation equipment is an important index for measuring the carbon emission capacity of one transportation equipment, but for the carbon emission capacity of the transportation equipment under complex environments such as different transportation demands, different types of transportation equipment, different transportation environments and the like, the carbon emission capacity is measured only by the carbon emission, and the influence of different factors of the transportation equipment on the carbon emission intensity under the complex environments cannot be accurately mastered, so that the reasonable distribution of the transportation resources under the complex environments cannot be realized. For example, in the course of sailing a ship, the type of the ship, the size of the ship, the tonnage, the sailing speed, the sailing distance, the residence time of the ship, the weather, and other transportation-related information are all important factors that affect the carbon emission intensity of the transportation device. The server of the embodiment of the present application may obtain, based on the foregoing, reference carbon emission intensity of a specific type of transportation device by training historical sample data of a plurality of transportation devices, and determine, according to the reference carbon emission intensity, the reference carbon emission intensity corresponding to the transportation device, thereby further determining a transportation device carbon emission capability standard that measures specific type information, that is, a carbon emission capability boundary value, and determining, according to comparing the actual carbon emission intensity of the transportation device with the carbon emission capability boundary value, a carbon emission capability level of a target transportation device, where the historical sample data may include transportation related information such as a model number, a size, tonnage, a sailing speed, a sailing distance, a residence time, weather, and the like of the transportation device, so as to implement accurate assessment of the carbon emission capability of the transportation device under a complex environmental condition.
In the embodiment of the application, a more accurate corresponding relation between different key dimension information and the reference carbon emission intensity in the preset time period can be provided, so that the corresponding relation is utilized for the target transportation equipment in the target period to be evaluated later, the reference carbon emission intensity corresponding to the target key dimension information of the target transportation equipment is obtained, then the reference carbon emission intensity in the target period is obtained based on the reference carbon emission intensity, the carbon emission capacity boundary value of the corresponding type of transportation equipment in the target time period can be determined according to the reference carbon emission intensity, the actual carbon emission intensity of the transportation equipment can be compared with the carbon emission capacity boundary value of the type of transportation equipment, and finally the carbon emission capacity grade of the transportation equipment in the target time period is determined. According to the method, the carbon emission levels of various transportation equipment in a target time period can be evaluated more accurately, the relatively uniform reference carbon emission intensity can be used as a calculation reference, the more standardized carbon emission level can be obtained, enterprises or units can make deeper decision deployment based on the more accurate carbon emission intensity result, differential management and service can be carried out on ships with different carbon intensity levels, the technical blank that the carbon emission capacity of the transportation equipment is evaluated too singly and simply in the related technology is enriched, and the energy development decision based on the carbon emission capacity is facilitated to develop towards a more accurate and scientific direction. And by training historical sample data of a plurality of transport equipment under the multi-dimension, the corresponding relation between the transport equipment with different preset key dimension information and the reference carbon emission intensity in the preset time period is obtained, so that the corresponding relation is utilized in the subsequent process of evaluating the transport equipment, the target transport equipment can obtain the reference carbon emission intensity of the transport equipment in the preset period according to the key dimension information of the transport equipment, the carbon emission capacity boundary value of the target transport equipment is determined according to the reference carbon emission intensity, the carbon emission capacity grade of the target transport equipment is further determined, the information of a plurality of dimensions related to the carbon emission capacity of the transport equipment can be comprehensively considered, the reference carbon emission intensity of the transport equipment of a corresponding type is obtained through training and calculation, the total carbon emission capacity of the transport equipment of a specific type under the complex factor can be accurately reflected, the carbon emission capacity boundary value determined by the reference carbon emission intensity is accurately divided into the accurate boundaries of the transport equipment in the multi-dimension, the corresponding carbon emission capacity of the transport equipment can be accurately evaluated under the complex environment when the complex transport equipment is in a certain time period by the corresponding relation is used later.
Referring to fig. 3, a flowchart illustrating steps of a method for determining carbon emission capability of a transportation device according to an embodiment of the present application includes:
In the embodiment of the present application, the target transportation device refers to a transportation device for which determination of the carbon emission capability level is required. In the embodiment of the application, the transportation equipment can be divided according to the key dimension information. The critical dimension information may be information classifying the transportation device at a preset level, and the critical dimension information may include information of at least one dimension, such as information of 1 dimension, information of 2 dimensions, or information of more dimensions. In the case of classifying transportation apparatuses with information of a plurality of dimensions as key dimension information, division of transportation apparatuses can be finer and more reasonable. For vessels there may be a classification from major to minor, such as major classes that may be divided into fishing vessels, cargo vessels, pleasure boats etc., under one major class of fishing vessels, 500 ton fishing vessels such as type a, 300 ton fishing vessels of type a, 1000 ton fishing vessels of type B etc. may be further subdivided.
For example, the transportation device may be a ship, for example, the key dimension information may include type information, tonnage, and the like. The type information may not only refer to a simple functional classification of transportation equipment like bulk cargo ships, liquid cargo ships, container ships, etc., but also may include specific models, such as container ship capacity 23992TEU, divided according to different ship attributes. The type information of the target transportation device can be obtained according to the attribute query of the device. As an example, if key dimension information of a container ship is to be obtained, specific model queries can be performed on the disclosed ship data sources through the attributes of the name, load, architecture, draft and the like of the container ship. Of course, the critical dimension information may also include information such as type information, and may also include more dimension information, which is not limited by the embodiments of the present application.
The first actual carbon emission intensity can be calculated according to the formulas (1), (2) and (3), wherein the fuel information, the sailing distance information and the like of the transportation equipment can be obtained through the use record of the transportation equipment. For example, the fuel oil information of the ship and the sailing distance information in the target time period can be obtained through the ship energy consumption report.
The target time period may be divided according to actual needs, for example, divided according to years, where 1 year is a time period, and of course, other time periods may also be used, for example, half a year or two years, which is not limited by the embodiments of the present application.
Alternatively, taking a ship as an example, in calculating the first actual carbon emission intensity for different types of ships, the calculation may be performed according to different carrying capacity measurement standards, as in the foregoing formulas (4) and (5). Step 202, obtaining the reference carbon emission intensity corresponding to the key dimension information in the corresponding preset time period according to the corresponding relation between the preset key dimension information and the reference carbon emission intensity in the preset time period.
The correspondence between the key dimension information and the reference carbon emission intensity in the preset time period can be obtained after training the preset model by utilizing historical sample data of a plurality of transportation devices in the preset time period. This process may be described with reference to the previous steps 101-104 and will not be described in detail here.
The correspondence between the preset critical dimension information and the reference carbon emission intensity in the preset time period can reflect the correspondence between the ships with different critical dimension information and the reference carbon emission intensity in the preset time period, for example, the reference carbon emission intensity in a certain year corresponding to the ship with the tonnage of A and the ship type of B.
Therefore, after the target critical dimension information of the target transportation equipment and the first actual carbon emission intensity in the target time period are acquired, the reference carbon emission intensity in the preset time period corresponding to the target critical dimension information can be searched from the corresponding relation between the preset critical dimension information and the reference carbon emission intensity in the preset time period according to the target critical dimension information.
Alternatively, the target transportation apparatus may include a ship, and the transportation related information of the plurality of dimensions may include at least one of type information of the ship, a size of the ship, tonnage of the ship, maximum speed of the ship, sailing time of the ship, and time when the ship stays at the berth. The key dimension information includes: type information of the ship and tonnage information of the ship, wherein the transportation related information of the target dimension comprises the following steps: at least one of a size of the vessel, a maximum speed of the vessel, a voyage time of the vessel, and a time for the vessel to stay at the berth.
And 203, acquiring reference carbon emission intensity corresponding to the target key dimension information in a target time period according to the reference carbon emission intensity.
Considering that the preset time period may be different in time length from the target time period for calculating the first actual carbon emission intensity of the target transportation apparatus, it is necessary to uniformly measure the length of the reference carbon emission intensity with the length of the first actual carbon emission intensity, the reference carbon emission intensity may be converted into the carbon emission intensity of which the time period is the target time period, that is, the reference carbon emission intensity, on the basis of the target time period, and the evaluation may be made more standard by using the reference carbon emission intensity of the same time period as a reference.
In one implementation, the brief duration may be unified by the time length, for example, the target time period is 1 year, and the preset time period is 2 years, so that half of the reference carbon emission intensity needs to be reserved as the reference carbon emission intensity, so that the first actual carbon emission intensity and the reference carbon emission intensity are both one year period, and comparison is convenient.
In another implementation, considering that the carbon emission intensity between different years may be attenuated due to various factors such as society, and comparison is performed in different social environments, as if the modern carbon emission intensity is compared with the past carbon emission intensity, the obtained carbon emission capacity is not time-efficient, so that the reduction coefficient of the carbon emission intensity between different years is considered, for example, the reference carbon emission intensity is calculated based on the data of the sample in 2019, and the first actual carbon emission intensity is calculated based on the data in 2021, and then the reduction coefficient of the carbon emission intensity between 2019 and 2021 needs to be considered, so that the reference carbon emission intensity in the same time dimension as the first actual carbon emission intensity is obtained, the time-efficient of the carbon emission intensity for comparison is ensured, and the finally obtained carbon emission capacity is consistent with the current era, and has a full practical utilization value.
And 204, determining at least two carbon emission capability boundary values corresponding to the target key dimension information based on the reference carbon emission intensity.
The reference carbon emission intensity reflects an average carbon emission level of the transport equipment of the target transport equipment type in the first actual carbon emission intensity time dimension, and thus the reference carbon emission intensity may be taken as an intermediate boundary for measuring the carbon emission capability, the carbon emission capability level of the target transport equipment may be determined to be poor if the first actual carbon emission intensity is greater than the reference carbon emission intensity, and the carbon emission capability level of the target transport equipment may be determined to be good if the first actual carbon emission intensity is lower than the reference carbon emission intensity, and the above is merely an example to explain the role of the reference carbon emission intensity in determining the boundary value, not the limitation of the carbon emission capability level boundary value and the carbon emission capability level division.
Specifically, the carbon emission capability boundary value may be obtained by comprehensively considering factors such as reduction factors, industry development, and the like based on the reference carbon emission strength, and weighting the reference carbon emission strength in different levels based on the factors. In consideration of timeliness of data, therefore, a dynamic adjustment parameter can be designed to weight the reference carbon emission intensity according to the change of the weighting parameter caused by time change, so that the carbon emission capacity boundary value is updated in real time, timeliness of the carbon emission intensity boundary value is ensured, and the carbon emission capacity grade determined by the boundary value has enough practical reference value.
As an example, taking a ship as an example, at least two carbon emission capability boundaries of a specific type of ship are determined according to the method described in step 204, for example, referring to fig. 4, the carbon emission capability boundaries may include an excellent boundary, a lower boundary, a higher boundary, and a disqualified boundary, and the carbon emission capability boundaries may be rated a if the first actual carbon emission strength of the ship is less than or equal to the excellent boundary, B if the first actual carbon emission strength of the ship is greater than the excellent boundary and less than or equal to the better boundary, C if the first actual carbon emission capability boundary is greater than the better boundary and less than or equal to the worse boundary, D if the carbon emission capability boundary is greater than the worse boundary, and E if the carbon emission capability boundary is greater than the disqualified boundary. Wherein the carbon emission capability class A is greater than B, B is greater than C, C is greater than D, and D is greater than E. As shown in fig. 4, the attained CII of a certain vessel is below the poor boundary and above the poor boundary, belonging to class C carbon discharge capacity. Of course, in the embodiment of the present application, the number of carbon emission capability boundary lines may be divided according to actual requirements, and the embodiment of the present application does not limit the number of carbon emission capability boundary lines.
It should be noted that after determining the carbon emission capability level of the target transportation apparatus, the target transportation apparatus may be subjected to subsequent processing such as issuing a modification opinion as described above, issuing a carbon emission capability level certificate, or the like.
Optionally, in another embodiment of the present application, step 203 includes:
a substep 2031 of obtaining a carbon emission reduction coefficient of the target time period relative to the preset time period;
sub-step 2032, determining a reference carbon emission intensity corresponding to the type information according to the carbon emission reduction coefficient and the reference carbon emission intensity.
In this embodiment of the present application, since the preset time period may be used as a reference, the application calculates the reference carbon emission intensity of the target time period, so that in order to gradually increase the carbon emission requirement, the overall carbon emission of the transportation device is gradually reduced, so that the application sets a concept of a carbon emission reduction coefficient, the application may obtain the carbon emission reduction coefficient of the target time period relative to the preset time period according to a first preset rule, and then determine the reference carbon emission intensity corresponding to the type information according to the carbon emission reduction coefficient and the reference carbon emission intensity.
Optionally, in another embodiment of the present application, the substep 2031 comprises:
substep 20311, obtaining a unit reduction coefficient for the unit time period.
The unit reduction coefficient of the unit time period may refer to an attenuation amount of the carbon emission intensity of the unit time period, and may be obtained according to published carbon emission related data or international traffic criteria. Of course, the device can be set according to actual requirements. For example, the unit reduction coefficient may be set to 1, which is not limited in the embodiments of the present application.
Substep 20312, obtaining the carbon emission reduction coefficient based on the difference between the target time periods and the preset time periods, multiplied by the unit reduction coefficient.
For example, the preset time period is 1999, the target time period is 2000, the difference between the target time period and the preset time period is 2000-1999=1, then the carbon emission reduction coefficient is obtained by multiplying 1 by the unit reduction coefficient, for example, the unit reduction coefficient is 1, and then the carbon emission reduction coefficient Z is 1*1 =1.
For example, the preset time period is 1999, the target time period is 2001, the difference between the target time periods and the preset time period is 2001-1999=2, and then the carbon emission reduction coefficient is obtained by multiplying 1 by the unit reduction coefficient, for example, the unit reduction coefficient is 1, and then the carbon emission reduction coefficient Z is 2*1 =2.
Of course, the attenuation coefficients may be different for different time periods, so the application is not limited to the unit reduction coefficient, and the carbon emission reduction coefficient may be calculated according to different reduction coefficients corresponding to consecutive unit time periods.
Optionally, in another embodiment of the present application, step 2032 includes:
sub-step 20311 is to subtract the ratio of the carbon emission reduction coefficient to the preset bottom value from 1 and multiply the reference carbon emission intensity to obtain the reference carbon emission intensity corresponding to the type information.
The preset bottom value may be set according to actual requirements, and in this embodiment of the present application, for example, may be set to 100.
Specifically, the reference carbon emission intensity may be calculated according to the following formula:
required CII＝(1–Z/100)×CII ref ，(10)
wherein, CII ref Representing a reference carbon emission intensity of the target transportation device; z represents a carbon emission reduction coefficient.
Then, for example, the aforementioned preset time period is 1999, the target time period is 2000, its required CII= (1-1/100) ×CII ref 。
Then, for example, the aforementioned preset time period is 1999, the target time period is 2001, its required CII= (1-2/100) ×CII ref 。
Thus, the later the year, the higher the carbon emission capability requirement is, the more the year 1999 is, and the carbon emission of the whole transportation equipment can be reduced year by year.
Optionally, in another embodiment of the present application, step 204 includes:
a substep 2041 of obtaining a carbon emission reduction coefficient of the target time period relative to the preset time period;
the carbon emission reduction coefficient is described with reference to the foregoing step 2031, and will not be described in detail herein.
And a substep 2042 of obtaining at least one dynamic boundary adjustment parameter based on the carbon emission reduction factor.
Optionally, in the case that the dynamic boundary adjustment parameters include at least two, a first dynamic adjustment parameter of the at least two dynamic boundary adjustment parameters is: subtracting a first dynamic value from a first preset value, wherein the first dynamic value is the product of one percent of the carbon emission reduction coefficient and the first preset value; the remaining second dynamic adjustment parameters of the at least two dynamic boundary adjustment parameters are: and adding a second dynamic value to the second preset value and taking the logarithm, wherein the second dynamic value is the product of the carbon emission reduction coefficient and a third preset value, wherein the second preset values of different second dynamic adjustment parameters are different, and the third preset values of different second dynamic adjustment parameters are different.
As an example, the dynamic boundary adjustment parameters may be calculated as follows:
Wherein d 1 Boundary adjustment parameter, d, which can be expressed as an excellent boundary 2 Boundary adjustment parameter, d, which can be expressed as a good boundary 3 Boundary adjustment parameter, d, which may be expressed as a poor boundary 4 Boundary adjustment parameters, which may be denoted as unacceptable boundaries. The logarithm log can be based on e or 10, and the base of the logarithm can be set according to actual needs.
And a sub-step 2043 of determining at least two carbon emission capability boundary values corresponding to the type parameter based on the dynamic boundary adjustment parameter and the reference carbon emission parameter.
In this embodiment of the present application, in order to reduce the carbon emission of the whole transportation device, the carbon emission requirement on the transportation device is higher, so the present application may obtain the dynamic boundary adjustment parameter based on the carbon emission reduction coefficient, and then may dynamically adjust the carbon emission capacity boundary value in the target time period based on the second preset rule.
Specifically, the carbon emission capability boundary value corresponding to the type parameter may be confirmed according to the following formula:
carbon emission capability boundary=exp (dynamic adjustment parameter) reference carbon emission intensity.
Such as:
excellent boundary=exp (d) 1 )*required CII
Better boundary =exp(d 2 )*required CII
Poor boundary = exp (d) 3 )*required CII
Reject boundary=exp (d) 4 )*required CII
As shown in fig. 4, carbon emission capability boundaries of a certain type of ship are determined, including an excellent boundary, a good boundary, a bad boundary, and if the first actual carbon emission intensity of the ship is less than or equal to the excellent boundary, the ship is rated a, and if the first actual carbon emission intensity of the ship is greater than the excellent boundary and less than or equal to the good boundary, the ship is rated B, and if the ship is greater than the bad boundary and less than or equal to the good boundary, the ship is rated C, and if the ship is greater than the bad boundary and less than or equal to the bad boundary, the ship is rated D, and if the ship is greater than or equal to the bad boundary, the ship is rated E.
According to the embodiment of the application, the corresponding relation between different key dimension information and the reference carbon emission intensity in the preset time period can be built in advance, then, for the target transportation equipment in the target period to be evaluated, the reference carbon emission intensity corresponding to the target key dimension information of the target transportation equipment is obtained by utilizing the corresponding relation, and then the reference carbon emission intensity in the target period is obtained based on the reference carbon emission intensity, so that the carbon emission capacity boundary value of the corresponding type of transportation equipment in the target time period can be determined according to the reference carbon emission intensity, the actual carbon emission intensity of the transportation equipment can be compared with the carbon emission capacity boundary value of the type of transportation equipment, and finally, the carbon emission capacity grade of the transportation equipment in the target time period is determined. According to the method, the carbon emission levels of various transportation equipment in a target time period can be evaluated more accurately, the relatively uniform reference carbon emission intensity can be used as a calculation reference, the more standardized carbon emission level can be obtained, enterprises or units can make deeper decision deployment based on the more accurate carbon emission intensity result, differential management and service can be carried out on ships with different carbon intensity levels, the technical blank that the carbon emission capacity of the transportation equipment is evaluated too singly and simply in the related technology is enriched, and the energy development decision based on the carbon emission capacity is facilitated to develop towards a more accurate and scientific direction.
In addition, by adopting the corresponding relation obtained by training the historical sample data of the transportation equipment, the transportation associated information of multiple dimensions related to the carbon emission capacity of the transportation equipment can be comprehensively considered, the transportation associated information of each dimension of the ship is fully utilized, the reference carbon emission intensity of each transportation equipment divided by key dimension information is obtained, the total carbon emission capacity of a certain type of transportation equipment in a complex environment can be accurately reflected, and therefore, when the transportation equipment in a certain time period is evaluated by using the corresponding relation, the multiple carbon emission capacity boundaries corresponding to the transportation equipment in the complex environment can be accurately determined, and the carbon emission capacity grade of the transportation equipment in the complex environment can be more accurately divided.
Referring to fig. 5, a block diagram of a carbon emission capability determining apparatus of a transportation device according to an embodiment of the present application is shown, including:
a first intensity obtaining module 301, configured to obtain target critical dimension information of a target transportation device, a first actual carbon emission intensity, and a first actual carbon emission intensity within a target time period;
A second intensity obtaining module 302, configured to obtain a reference carbon emission intensity corresponding to the target critical dimension information in a preset time period according to a correspondence between the preset critical dimension information and the reference carbon emission intensity in the preset time period;
a third intensity obtaining module 303, configured to obtain, according to the reference carbon emission intensity, a reference carbon emission intensity corresponding to the target key dimension information in a target time period;
a boundary determining module 304, configured to determine at least two carbon emission capability boundary values corresponding to the target key dimension information based on the reference carbon emission intensity;
a capacity determination module 305 is configured to compare the first actual carbon emission intensity with the carbon emission capacity boundary value and determine a carbon emission capacity level of the target transportation device within a target time period.
Optionally, the third intensity obtaining module may include:
a carbon emission reduction coefficient obtaining sub-module, configured to obtain a carbon emission reduction coefficient of the target time period relative to the preset time period;
and the reference carbon emission intensity determination submodule is used for determining the reference carbon emission intensity corresponding to the type information according to the carbon emission reduction coefficient and the reference carbon emission intensity.
Optionally, the carbon emission reduction coefficient obtaining sub-module may include:
the unit reduction coefficient acquisition sub-module is used for acquiring the unit reduction coefficient of the unit time period;
and the carbon emission reduction coefficient calculation sub-module is used for multiplying the unit reduction coefficient based on the difference between the target time period and the preset time period to obtain the carbon emission reduction coefficient.
Optionally, the reference carbon emission intensity determination submodule may include:
and the reference carbon emission intensity calculation sub-module is used for subtracting the ratio of the carbon emission reduction coefficient to a preset bottom value from 1 and multiplying the reference carbon emission intensity to obtain the reference carbon emission intensity corresponding to the type information.
Optionally, the boundary determining module may include:
a carbon emission reduction coefficient obtaining sub-module, configured to obtain a carbon emission reduction coefficient of the target time period relative to the preset time period;
the dynamic boundary adjustment parameter acquisition sub-module is used for acquiring at least one dynamic boundary adjustment parameter based on the carbon emission reduction coefficient;
and the carbon emission capacity boundary value determining submodule is used for determining at least two carbon emission capacity boundary values corresponding to the type parameter based on the dynamic boundary adjustment parameter and the reference carbon emission parameter.
Optionally, in the case that the dynamic boundary adjustment parameters include at least two, a first dynamic adjustment parameter of the at least two dynamic boundary adjustment parameters is: subtracting a first dynamic value from a first preset value, wherein the first dynamic value is the product of one percent of the carbon emission reduction coefficient and the first preset value; the remaining second dynamic adjustment parameters of the at least two dynamic boundary adjustment parameters are: and adding a second dynamic value to the second preset value and taking the logarithm, wherein the second dynamic value is the product of the carbon emission reduction coefficient and a third preset value, wherein the second preset values of different second dynamic adjustment parameters are different, and the third preset values of different second dynamic adjustment parameters are different.
Optionally, the target transportation device is a ship, and the key dimension information includes: type information of the ship and tonnage information of the ship, wherein the transportation related information of the target dimension comprises the following steps: at least one of a size of the vessel, a maximum speed of the vessel, a voyage time of the vessel, and a time for the vessel to stay at the berth.
According to the embodiment of the application, the corresponding relation between different key dimension information and the reference carbon emission intensity in the preset time period can be built in advance, then, for the target transportation equipment in the target period to be evaluated, the reference carbon emission intensity corresponding to the target key dimension information of the target transportation equipment is obtained by utilizing the corresponding relation, and then the reference carbon emission intensity in the target period is obtained based on the reference carbon emission intensity, so that the carbon emission capacity boundary value of the corresponding type of transportation equipment in the target time period can be determined according to the reference carbon emission intensity, the actual carbon emission intensity of the transportation equipment can be compared with the carbon emission capacity boundary value of the type of transportation equipment, and finally, the carbon emission capacity grade of the transportation equipment in the target time period is determined. According to the method, the carbon emission levels of various transportation equipment in a target time period can be evaluated more accurately, the relatively uniform reference carbon emission intensity can be used as a calculation reference, the more standardized carbon emission level can be obtained, enterprises or units can make deeper decision deployment based on the more accurate carbon emission intensity result, differential management and service can be carried out on ships with different carbon intensity levels, the technical blank that the carbon emission capacity of the transportation equipment is evaluated too singly and simply in the related technology is enriched, and the energy development decision based on the carbon emission capacity is facilitated to develop towards a more accurate and scientific direction.
In addition, by adopting the corresponding relation obtained by training the historical sample data of the transportation equipment, the transportation associated information of multiple dimensions related to the carbon emission capacity of the transportation equipment can be comprehensively considered, the transportation associated information of each dimension of the ship is fully utilized, the reference carbon emission intensity of each transportation equipment divided by key dimension information is obtained, the total carbon emission capacity of a certain type of transportation equipment in a complex environment can be accurately reflected, and therefore, when the transportation equipment in a certain time period is evaluated by using the corresponding relation, the multiple carbon emission capacity boundaries corresponding to the transportation equipment in the complex environment can be accurately determined, and the carbon emission capacity grade of the transportation equipment in the complex environment can be more accurately divided.
Referring to FIG. 6, a block diagram of a correspondence acquiring apparatus according to an embodiment of the present application is shown, including
A sample acquiring module 401, configured to acquire historical sample data of a plurality of transport devices within a preset time period; the historical sample data comprises transport-related information of multiple dimensions of the transport equipment and second actual carbon emission intensity of the transport equipment in the preset time period; the transportation related information of multiple dimensions includes the critical dimension information;
The model training module 402 is configured to train a preset model based on the historical sample data to obtain a first data model;
an input information adjustment module 403, configured to determine first input information of multiple dimensions according to transportation related information of multiple dimensions of multiple transportation devices, for the key dimension information respectively; wherein the first input information of a target dimension of the plurality of dimensions comprises an average value of transportation related information, the target dimension comprising at least a portion of dimensions other than the critical dimension information;
the correspondence determining module 404 is configured to bring first input information of multiple dimensions of the transportation device into the first data model, obtain a reference carbon emission intensity corresponding to the key dimension information, and construct a correspondence between the key dimension information and the reference carbon emission intensity.
Wherein the correspondence relationship includes a correspondence relationship between preset critical dimension information and reference carbon emission intensity within a preset time period as in any one of the embodiments shown in fig. 1 and 3.
Optionally, the model training module is specifically configured to, based on the historical sample data, bring the historical sample data into the preset model, and obtain a dimension parameter of the dimension; based on the dimension parameters, determining the sum of a least square term and a penalty term corresponding to the preset model; and under the condition of the minimum value of the sum of the least square and the punishment terms, taking the dimension parameter as the dimension parameter of the preset model to obtain the first data model.
Optionally, the transportation related information of the plurality of dimensions includes type information and transportation related information of a first dimension, and the method may further include:
the coding module is used for carrying out one-bit effective coding on the type information to obtain type coding information;
the normalization module is used for normalizing the transportation association information in the first dimension to obtain normalized association information; the first dimension includes at least a portion of the dimensions other than the type information;
correspondingly, the model training module may include:
the first data model acquisition sub-module is used for training a preset model based on the type association information and the normalized association information of the transportation equipment to obtain a first data model.
In the embodiment of the application, a more accurate corresponding relation between different key dimension information and the reference carbon emission intensity in the preset time period can be provided, so that the corresponding relation is utilized for the target transportation equipment in the target period to be evaluated later, the reference carbon emission intensity corresponding to the target key dimension information of the target transportation equipment is obtained, then the reference carbon emission intensity in the target period is obtained based on the reference carbon emission intensity, the carbon emission capacity boundary value of the corresponding type of transportation equipment in the target time period can be determined according to the reference carbon emission intensity, the actual carbon emission intensity of the transportation equipment can be compared with the carbon emission capacity boundary value of the type of transportation equipment, and finally the carbon emission capacity grade of the transportation equipment in the target time period is determined. According to the method, the carbon emission levels of various transportation equipment in a target time period can be evaluated more accurately, the relatively uniform reference carbon emission intensity can be used as a calculation reference, the more standardized carbon emission level can be obtained, enterprises or units can make deeper decision deployment based on the more accurate carbon emission intensity result, differential management and service can be carried out on ships with different carbon intensity levels, the technical blank that the carbon emission capacity of the transportation equipment is evaluated too singly and simply in the related technology is enriched, and the energy development decision based on the carbon emission capacity is facilitated to develop towards a more accurate and scientific direction. And by training historical sample data of a plurality of transport equipment under the multi-dimension, the corresponding relation between the transport equipment with different preset key dimension information and the reference carbon emission intensity in the preset time period is obtained, so that the corresponding relation is utilized in the subsequent process of evaluating the transport equipment, the target transport equipment can obtain the reference carbon emission intensity of the transport equipment in the preset period according to the key dimension information of the transport equipment, the carbon emission capacity boundary value of the target transport equipment is determined according to the reference carbon emission intensity, the carbon emission capacity grade of the target transport equipment is further determined, the information of a plurality of dimensions related to the carbon emission capacity of the transport equipment can be comprehensively considered, the reference carbon emission intensity of the transport equipment of a corresponding type is obtained through training and calculation, the total carbon emission capacity of the transport equipment of a specific type under the complex factor can be accurately reflected, the carbon emission capacity boundary value determined by the reference carbon emission intensity is accurately divided into the accurate boundaries of the transport equipment in the multi-dimension, the corresponding carbon emission capacity of the transport equipment can be accurately evaluated under the complex environment when the complex transport equipment is in a certain time period by the corresponding relation is used later. The embodiment of the application also provides a non-volatile readable storage medium, where one or more modules (programs) are stored, where the one or more modules are applied to a device, and the device may be caused to execute instructions (instractions) of each method step in the embodiment of the application.
Embodiments of the present application provide one or more machine-readable media having instructions stored thereon that, when executed by one or more processors, cause an electronic device to perform a method as described in one or more of the above embodiments. In this embodiment of the present application, the electronic device includes various types of devices such as a terminal device, a server (a cluster), and the like.
Embodiments of the present disclosure may be implemented as an apparatus for performing a desired configuration using any suitable hardware, firmware, software, or any combination thereof, which may include electronic devices such as terminal devices, servers (clusters), etc. Fig. 6 schematically illustrates an example apparatus 1000 that may be used to implement various embodiments described in embodiments of the present application.
For one embodiment, fig. 7 illustrates an example apparatus 1000 having one or more processors 1002, a control module (chipset) 1004 coupled to at least one of the processor(s) 1002, a memory 1006 coupled to the control module 1004, a non-volatile memory (NVM)/storage 1008 coupled to the control module 1004, one or more input/output devices 1010 coupled to the control module 1004, and a network interface 1012 coupled to the control module 1004.
The processor 1002 may include one or more single-core or multi-core processors, and the processor 1002 may include any combination of general-purpose or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, the apparatus 1000 can be used as a terminal device, a server (cluster), or the like in the embodiments of the present application.
In some embodiments, the apparatus 1000 may include one or more computer-readable media (e.g., memory 1006 or nonvolatile memory (NVM)/storage 1008) having instructions 1014 and one or more processors 1002 in combination with the one or more computer-readable media configured to execute the instructions 1014 to implement the modules to perform the actions described in this disclosure.
For one embodiment, the control module 1004 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 1002 and/or any suitable device or component in communication with the control module 1004.
The control module 1004 may include a memory controller module to provide an interface to the memory 1006. The memory controller modules may be hardware modules, software modules, and/or firmware modules.
Memory 1006 may be used to load and store data and/or instructions 1014 for device 1000, for example. For one embodiment, the memory 1006 may include any suitable volatile memory, such as a suitable DRAM. In some embodiments, the memory 1006 may comprise a double data rate type four synchronous dynamic random access memory (DDR 4 SDRAM).
For one embodiment, the control module 1004 may include one or more input/output controllers to provide an interface to the NVM/storage 1008 and the input/output device(s) 1010.
For example, NVM/storage 1008 may be used to store data and/or instructions 1014. NVM/storage 1008 may include any suitable nonvolatile memory (e.g., flash memory) and/or may include any suitable nonvolatile storage device(s) (e.g., one or more Hard Disk Drives (HDDs), one or more Compact Disc (CD) drives, and/or one or more Digital Versatile Disc (DVD) drives).
NVM/storage 1008 may include storage resources that are physically part of the device on which apparatus 1000 is installed, or may be accessible by the device without necessarily being part of the device. For example, NVM/storage 1008 may be accessed over a network via input/output device(s) 1010.
Input/output device(s) 1010 may provide an interface for apparatus 1000 to communicate with any other suitable device, input/output device 1010 may include communication components, audio components, sensor components, and the like. The network interface 1012 may provide an interface for the device 1000 to communicate over one or more networks, and the device 1000 may communicate wirelessly with one or more components of a wireless network in accordance with any of one or more wireless network standards and/or protocols, such as accessing a wireless network based on a communication standard, such as WiFi (wireless fidelity, wireless local area network), 2G (second generation mobile communication technology), 3G (third generation mobile communication technology), 4G (fourth generation mobile communication technology), 5G (fifth generation mobile communication technology), etc., or a combination thereof.
For one embodiment, at least one of the processor(s) 1002 may be packaged together with logic of one or more controllers (e.g., memory controller modules) of the control module 1004. For one embodiment, at least one of the processor(s) 1002 may be packaged together with logic of one or more controllers of the control module 1004 to form a System In Package (SiP). For one embodiment, at least one of the processor(s) 1002 may be integrated on the same mold as logic of one or more controllers of the control module 1004. For one embodiment, at least one of the processor(s) 1002 may be integrated on the same die with logic of one or more controllers of the control module 1004 to form a system on chip (SoC).
In various embodiments, the apparatus 1000 may be, but is not limited to being: a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.), among other terminal devices. In various embodiments, device 1000 may have more or fewer components and/or different architectures. For example, in some embodiments, the apparatus 1000 includes one or more cameras, a keyboard, a Liquid Crystal Display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an Application Specific Integrated Circuit (ASIC), and a speaker.
The detection device can adopt a main control chip as a processor or a control module, sensor data, position information and the like are stored in a memory or an NVM/storage device, a sensor group can be used as an input/output device, and a communication interface can comprise a network interface.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the present application.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The above detailed description of a method for determining carbon emission capability of a transportation device, a method and a device for obtaining a correspondence relationship, an electronic device, and a machine readable medium provided by the present application, where specific examples are applied to illustrate principles and implementations of the present application, the above description of examples is only used to help understand the method and core ideas of the present application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (14)
1. A method for determining carbon emission capacity of a transportation device, comprising:
acquiring target key dimension information of target transportation equipment and first actual carbon emission intensity in a target time period;
acquiring the reference carbon emission intensity corresponding to the target key dimension information in a preset time period according to the corresponding relation between the preset key dimension information and the reference carbon emission intensity in the preset time period;
acquiring reference carbon emission intensity corresponding to the target key dimension information in a target time period according to the reference carbon emission intensity;
Determining at least two carbon emission capability boundary values corresponding to the target key dimension information based on the reference carbon emission intensity;
and comparing the first actual carbon emission intensity with the carbon emission capability boundary value to determine the carbon emission capability level of the target transportation equipment in a target time period.
2. The method of claim 1, wherein the obtaining the reference carbon emission intensity corresponding to the target critical dimension information for the target time period based on the reference carbon emission intensity comprises:
acquiring a carbon emission reduction coefficient of the target time period relative to the preset time period;
and determining the reference carbon emission intensity corresponding to the type information according to the carbon emission reduction coefficient and the reference carbon emission intensity.
3. The method of claim 2, wherein the obtaining the carbon emission reduction coefficient of the target time period relative to the preset time period comprises:
obtaining a unit reduction coefficient of a unit time period;
and multiplying the unit reduction coefficient by the difference between the target time period and the preset time period to obtain the carbon emission reduction coefficient.
4. The method according to claim 2, wherein the determining the reference carbon emission intensity corresponding to the type information based on the carbon emission reduction coefficient and the reference carbon emission intensity includes:
and subtracting the ratio of the carbon emission reduction coefficient to a preset bottom value from 1, and multiplying the ratio by the reference carbon emission intensity to obtain the reference carbon emission intensity corresponding to the type information.
5. The method of claim 1, wherein determining at least two carbon emission capability boundary values corresponding to the target critical dimension information based on the reference carbon emission intensity comprises:
acquiring a carbon emission reduction coefficient of the target time period relative to the preset time period;
acquiring at least one dynamic boundary adjustment parameter based on the carbon emission reduction coefficient;
and determining at least two carbon emission capability boundary values corresponding to the type parameter based on the dynamic boundary adjustment parameter and the reference carbon emission parameter.
6. The method of claim 5, wherein, in the case where the dynamic boundary adjustment parameters include at least two, a first dynamic adjustment parameter of the at least two dynamic boundary adjustment parameters is: subtracting a first dynamic value from a first preset value, wherein the first dynamic value is the product of one percent of the carbon emission reduction coefficient and the first preset value; the remaining second dynamic adjustment parameters of the at least two dynamic boundary adjustment parameters are: and adding a second dynamic value to the second preset value and taking the logarithm, wherein the second dynamic value is the product of the carbon emission reduction coefficient and a third preset value, wherein the second preset values of different second dynamic adjustment parameters are different, and the third preset values of different second dynamic adjustment parameters are different.
7. The method of claim 1, wherein the target transportation device is a ship, and the critical dimension information comprises: type information of the ship and tonnage information of the ship, wherein the transportation related information of the target dimension comprises the following steps: at least one of a size of the vessel, a maximum speed of the vessel, a voyage time of the vessel, and a time for the vessel to stay at the berth.
8. The method for acquiring the corresponding relation is characterized by comprising the following steps:
acquiring historical sample data of a plurality of transportation devices within a preset time period; the historical sample data includes transportation related information for a plurality of dimensions of the transportation device; the transportation related information of multiple dimensions includes the critical dimension information;
training a preset model based on the historical sample data to obtain a first data model;
determining first input information of a plurality of dimensions according to transportation related information of the plurality of dimensions of the transportation equipment aiming at the key dimension information respectively; wherein the first input information of a target dimension of the plurality of dimensions comprises an average value of transportation related information, the target dimension comprising at least a portion of dimensions other than the critical dimension information;
And carrying first input information of multiple dimensions of the transportation equipment into the first data model, obtaining reference carbon emission intensity corresponding to the key dimension information, and constructing a corresponding relation between the key dimension information and the reference carbon emission intensity.
9. The method of claim 8, wherein training the pre-set model based on the historical sample data to obtain a first data model comprises:
based on the historical sample data, carrying the historical sample data into the preset model to acquire dimension parameters of the dimension;
based on the dimension parameters, determining the sum of a least square term and a penalty term corresponding to the preset model;
and under the condition that the sum of the least square and punishment terms meets a preset condition, taking the dimension parameter as the dimension parameter of the preset model to obtain the first data model.
10. The method of claim 8, wherein the transport-related information for the plurality of dimensions includes type information and transport-related information for a first dimension, the method further comprising:
carrying out one-bit effective coding on the type information to obtain type coding information;
Normalizing the transportation association information in the first dimension to obtain normalized association information; the first dimension includes at least a portion of the dimensions other than the type information;
training a preset model based on the historical sample data to obtain a first data model, wherein the training comprises the following steps:
training a preset model based on the type association information and the normalized association information of the transportation equipment to obtain a first data model.
11. A transport apparatus carbon emission capacity determination device, characterized by comprising:
the first strength acquisition module is used for acquiring target key dimension information of target transportation equipment and first actual carbon emission strength in a target time period;
the second intensity acquisition module is used for acquiring the reference carbon emission intensity corresponding to the target key dimension information in the preset time period according to the corresponding relation between the preset key dimension information and the reference carbon emission intensity in the preset time period; the corresponding relation between the key dimension information and the reference carbon emission intensity in the preset time period is obtained by training historical sample data of a plurality of transport equipment in the preset time period;
The third intensity acquisition module is used for acquiring reference carbon emission intensity corresponding to the target key dimension information in a target time period according to the reference carbon emission intensity;
the boundary determining module is used for determining at least two carbon emission capacity boundary values corresponding to the target key dimension information based on the reference carbon emission intensity;
and the capacity determining module is used for comparing the first actual carbon emission intensity with the carbon emission capacity boundary value and determining the carbon emission capacity grade of the target transportation equipment in a target time period.
12. An acquisition apparatus of correspondence, characterized by comprising:
the sample acquisition module is used for acquiring historical sample data of a plurality of transport equipment in a preset time period; the historical sample data comprises transport-related information of multiple dimensions of the transport equipment and second actual carbon emission intensity of the transport equipment in the preset time period; the transportation related information of multiple dimensions includes the critical dimension information;
the model training module is used for training a preset model based on the historical sample data to obtain a first data model;
The input information adjustment module is used for determining first input information of multiple dimensions according to transportation related information of the multiple dimensions of the transportation equipment aiming at the key dimension information respectively; wherein the first input information of a target dimension of the plurality of dimensions comprises an average value of transportation related information, the target dimension comprising at least a portion of dimensions other than the critical dimension information;
and the corresponding relation determining module is used for substituting the first input information of the plurality of dimensions of the transportation equipment into the first data model, obtaining the reference carbon emission intensity corresponding to the key dimension information, and constructing the corresponding relation between the key dimension information and the reference carbon emission intensity.
13. An electronic device, comprising:
a processor; and
a memory having executable code stored thereon that, when executed, causes the processor to perform the method of any of claims 1 to 6 or the method of any of claims 1-10.
14. One or more machine readable media having executable code stored thereon that, when executed, causes a processor to perform the method of any of claims 1 to 6 or the method of any of claims 1-10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310119806.1A CN116109459A (en) | 2023-01-18 | 2023-01-18 | Method and device for determining carbon emission capacity of transportation equipment and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310119806.1A CN116109459A (en) | 2023-01-18 | 2023-01-18 | Method and device for determining carbon emission capacity of transportation equipment and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116109459A true CN116109459A (en) | 2023-05-12 |
Family
ID=86265292
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310119806.1A Pending CN116109459A (en) | 2023-01-18 | 2023-01-18 | Method and device for determining carbon emission capacity of transportation equipment and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116109459A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116974772A (en) * | 2023-09-21 | 2023-10-31 | 阿里云计算有限公司 | Resource optimization and carbon emission reduction method and equipment for large language model |
-
2023
- 2023-01-18 CN CN202310119806.1A patent/CN116109459A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116974772A (en) * | 2023-09-21 | 2023-10-31 | 阿里云计算有限公司 | Resource optimization and carbon emission reduction method and equipment for large language model |
| CN116974772B (en) * | 2023-09-21 | 2024-02-27 | 阿里云计算有限公司 | Resource optimization and carbon emission reduction method and equipment for large language model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhou et al. | Development of a transient fuel consumption model | |
| Wu et al. | Sensitive analysis of emission rates in MOVES for developing site-specific emission database | |
| Zhang et al. | Traffic accident prediction based on LSTM-GBRT model | |
| CN107239844A (en) | A kind of petroleum transportation vehicle oil consumption Forecasting Methodology based on Hadoop | |
| CN116109459A (en) | Method and device for determining carbon emission capacity of transportation equipment and electronic equipment | |
| CN114077970B (en) | Method and device for determining carbon emission related factor based on urban morphology | |
| CN111046491A (en) | Method and device for estimating oil consumption of large ship diesel engine | |
| CN115829081A (en) | Urban traffic carbon emission prediction method based on support vector regression model | |
| Yeow et al. | Comparing commercial vehicle fuel consumption models using real-world data under calibration constraints | |
| Ma et al. | Integrated traffic and emission simulation: a model calibration approach using aggregate information | |
| CN116453371B (en) | Method and device for identifying returning of shared vehicle, computer equipment and storage medium | |
| CN115691136A (en) | Urban low-carbon passenger traffic structure evaluation system based on data analysis | |
| Pan et al. | Characteristics analysis and modeling of emissions for bus with liquefied natural gas fuel system in real world driving | |
| CN116664025A (en) | Loading and unloading position point generation method, device and equipment | |
| Oduro et al. | CO 2 vehicular emission statistical analysis with instantaneous speed and acceleration as predictor variables | |
| CN115221218A (en) | Quality evaluation method and device for vehicle data, computer equipment and storage medium | |
| CN114022045A (en) | Regional economic development level determining method and device and terminal equipment | |
| Wang | Vehicle refueling behavior model based on spatio-temporal big data monitoring platform | |
| Xu et al. | Evaluation and Influencing Factors of Transportation Industry Energy Efficiency of Changjiang Economic Zone | |
| Yang et al. | Research on the Location Planning of a Dry Port Based on Fuzzy C-Means Clustering | |
| CN110796417B (en) | Goods source value determination method, device, equipment and storage medium | |
| CN113435653B (en) | Method and system for predicting saturated power consumption based on logistic model | |
| Vaz et al. | Dynamic Analysis of the Sustainable Performance of Electric Mobility in European Countries | |
| CN116562025A (en) | Traffic carbon emission analysis system and method based on traffic model data | |
| CN107610467B (en) | Traffic state discrimination method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |