A logistics carbon footprint tracking method and device based on passive internet of things

Abstract

The application discloses a logistics carbon footprint tracking method and device based on a passive Internet of Things, wherein the method comprises the following steps: deploying a passive Internet of Things label on a logistics carrier or a cargo package, collecting positioning information and basic information of the passive Internet of Things label through a base station network; analyzing an actual transportation track of the cargo according to the positioning information, determining a transportation energy consumption parameter according to the actual transportation track and real-time road condition data; determining a bearing allocation parameter according to actual bearing information and an actual transportation distance of the cargo; and accounting for a logistics carbon footprint of the cargo according to the transportation energy consumption parameter, a fuel emission factor and the bearing allocation parameter. The application realizes full-scene and high-precision dynamic accounting of the carbon footprint of logistics transportation, and solves the problems of low scene restoration degree of traditional logistics carbon accounting, not considering road condition abnormalities, inaccurate apportioning of multi-cargo loading, and large accounting errors.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things (IoT) and green logistics technology, and in particular to a method and apparatus for tracking the carbon footprint of logistics based on passive IoT. Background Technology

[0002] Current logistics carbon footprint accounting mainly relies on the estimation model of "distance × vehicle type emission factor". This model cannot obtain the real operating conditions during transportation, such as whether the vehicle is running empty, whether it encounters congestion (high emissions from idling), whether it takes detours, and whether the goods have undergone unexpected transshipment during transportation.

[0003] In addition, traditional RFID or active GPS devices are expensive and difficult to maintain, making it difficult to widely adopt them on logistics carriers such as pallets and turnover boxes. This results in data gaps in the logistics chain and makes it impossible to achieve precise carbon accounting "door to door". Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] The main objective of this invention is to provide a logistics carbon footprint tracking method based on passive Internet of Things (IoT) to achieve low-cost, end-to-end carbon footprint tracking.

[0006] Another objective of this invention is to propose a method and apparatus for tracking the carbon footprint of logistics based on passive Internet of Things.

[0007] To achieve the above objectives, a first aspect of the present invention proposes a method for tracking the carbon footprint of logistics based on passive Internet of Things, comprising:

[0008] To achieve the above objectives, a second aspect of the present invention provides a logistics carbon footprint tracking device based on passive Internet of Things, comprising: To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory to implement the method described in the first aspect.

[0009] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0010] The embodiments of the present invention have the following beneficial effects: 1. Data is collected using passive IoT tags. The device has low power consumption and low deployment cost, and can work stably for a long time. It can realize automatic data collection throughout the entire logistics and transportation process, avoiding the problems of lag and distortion in manual statistical data, and ensuring the accuracy of the data source for accounting.

[0011] 2. By combining real-time road conditions with dynamic calculation of transportation energy consumption, it can accurately identify additional carbon emissions in scenarios such as congestion, detours, and traffic delays, which closely matches real transportation conditions and solves the shortcomings of traditional static accounting in quantifying the additional energy consumption under abnormal conditions.

[0012] 3. Based on cargo weight and actual transportation mileage, load allocation parameters are calculated to achieve refined carbon emission allocation in multi-cargo co-loading scenarios, abandoning the traditional average allocation method and significantly reducing carbon footprint accounting errors.

[0013] 4. It can adaptively match the carbon emission factors of different transportation fuels, adapt to various logistics and transportation scenarios, has strong versatility, and effectively improves the overall accuracy and scientific nature of logistics carbon footprint accounting. Attached Figure Description

[0014] The above-described and additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a logistics carbon footprint tracking method based on passive Internet of Things provided in an embodiment of the present invention. Detailed Implementation

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] The following description, with reference to the accompanying drawings, describes a method and apparatus for tracking the carbon footprint of logistics based on a passive Internet of Things (IoT) according to an embodiment of the present invention.

[0018] Example 1 This embodiment provides a logistics carbon footprint tracking method based on passive Internet of Things, such as... Figure 1 As shown, the method includes the following steps: S1, deploy passive IoT tags on logistics vehicles or cargo packaging, and collect the location information and basic information of the passive IoT tags through the base station network.

[0019] Specifically, passive IoT tags are deployed on cargo packaging, pallets, turnover boxes, containers, or transport vehicles. These passive IoT tags can be 5G RedCap tags, passive cellular IoT tags, or other low-power wide-area communication tags. The passive IoT tags obtain their operating power from ambient radio frequency energy or are powered by a weak battery for periodic operation, thereby reducing overall deployment costs and improving long-term operational capability.

[0020] In some implementations, the passive IoT tag periodically reports basic cargo information and location information. The basic information includes at least one of cargo number, cargo type, cargo weight, cargo volume, carrier number, and transport batch number. The location information includes location coordinates obtained based on base station triangulation or GNSS-assisted positioning.

[0021] After receiving the data sent by the passive IoT tag, the base station network uploads the corresponding data to the logistics carbon footprint accounting platform. The logistics carbon footprint accounting platform sorts the location information by time series and establishes a corresponding transportation trajectory data set, providing basic data for subsequent transportation energy consumption analysis.

[0022] S2, parse the actual transportation trajectory of the goods based on the positioning information, and determine the transportation energy consumption parameters based on the actual transportation trajectory and real-time road condition data.

[0023] Specifically, the logistics carbon footprint accounting platform analyzes the actual transportation route of goods based on location information within a continuous time period to obtain the actual driving route, actual stopping location, actual stopping time, and corresponding transportation time period of the vehicle during the transportation process.

[0024] Furthermore, in this embodiment of the application, the parsed actual transportation trajectory is correlated with real-time traffic data. The real-time traffic data includes at least one of the following: road congestion index, traffic light waiting time, road restriction information, traffic control information, and instantaneous vehicle speed.

[0025] In some implementations, when the present application identifies situations such as road congestion, temporary detours, traffic control delays, or long-term parking, the corresponding time period is identified as an abnormal transportation time period, and the additional transportation energy consumption during that time period is calculated.

[0026] The transportation energy consumption parameters Calculated using the following formula:

[0027] in, Idle emission factor, The congestion coefficient, For driving emission factors, This refers to instantaneous velocity.

[0028] The congestion coefficient is used to characterize the impact of road congestion on vehicle idling emissions. During prolonged periods of low-speed driving or stationary waiting, this embodiment of the application increases the weighting of idling emissions for the corresponding time period, thereby enabling transportation energy consumption parameters to reflect the additional carbon emissions caused by traffic jams, detours, and traffic control during actual transportation.

[0029] S3 determines the load allocation parameters based on the actual load information and actual transportation distance of the goods.

[0030] Specifically, in scenarios involving shared transport, less-than-container-load (LCL) transport, or multi-cargo transport, the same transport vehicle corresponds to multiple cargo objects. This application's embodiments allocate the overall transport energy consumption based on the actual load information of each cargo within the vehicle.

[0031] The actual carrying information includes at least one of the following: cargo weight, cargo volume percentage, cargo loading space percentage, and cargo transit time.

[0032] In some implementations, the embodiments of this application calculate the actual transportation distance for each cargo in segments based on the trajectory analysis results, thereby obtaining the actual transportation mileage for each cargo.

[0033] The load allocation parameters Calculated using the following formula:

[0034] in, The target cargo weight. The actual transportation distance of the target goods. The first in the same vehicle Weight of each item For the first The actual transportation distance of the goods.

[0035] The above method allows for a reasonable allocation of overall carbon emissions during transportation based on the actual contribution of different goods, avoiding the accounting errors caused by the traditional method of allocating by average proportion.

[0036] S4. Calculate the logistics carbon footprint of the goods based on the transportation energy consumption parameters, fuel emission factors, and load allocation parameters.

[0037] Specifically, this application's embodiments calculate the logistics carbon footprint of the target cargo based on transportation energy consumption parameters, fuel emission factors, and load allocation parameters. The final logistics carbon footprint... Calculated using the following formula:

[0038] in, The carbon emission factor for the corresponding fuel type of the transport vehicle, wherein the fuel type includes at least one of diesel, gasoline, natural gas, electricity or hydrogen.

[0039] In some implementations, embodiments of this application automatically match the corresponding fuel emission factor based on the actual type of fuel used by the transport vehicle, thereby improving the accuracy of carbon footprint calculation.

[0040] For example, during the transportation of a batch of electronic products from Shenzhen to Chengdu, this embodiment of the application detected that the transport vehicle was delayed in Guiyang for 10 hours due to traffic control via a passive IoT tag. Based on the vehicle's stationary trajectory, congestion coefficient, and idling emission factor during this period, this embodiment of the application automatically calculates the corresponding idling transport energy consumption and includes this energy consumption in the transport energy consumption parameters. Subsequently, this embodiment of the application, in conjunction with the corresponding cargo carrying allocation parameters, allocates this additional carbon emission, rather than simply calculating it based on the straight-line distance from Shenzhen to Chengdu, thus truly reflecting the carbon cost of the logistics process.

[0041] Example 2 This invention also provides a logistics carbon footprint tracking device based on passive Internet of Things, comprising: The first module is used to deploy passive IoT tags on logistics vehicles or cargo packaging, and collect the location information and basic information of the passive IoT tags through a base station network; The second module is used to analyze the actual transportation trajectory of the goods based on the location information, and to determine the transportation energy consumption parameters based on the actual transportation trajectory and real-time road condition data. The third module is used to determine the load allocation parameters based on the actual load information and actual transportation distance of the goods. The fourth module is used to calculate the logistics carbon footprint of goods based on the transportation energy consumption parameters, fuel emission factors, and load allocation parameters.

[0042] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0044] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0045] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for tracking the carbon footprint of logistics based on passive Internet of Things, characterized in that, Includes the following steps: Deploy passive IoT tags on logistics vehicles or cargo packaging, and collect the location information and basic information of the passive IoT tags through a base station network; The actual transportation trajectory of the goods is analyzed based on the location information, and the transportation energy consumption parameters are determined based on the actual transportation trajectory and real-time road condition data. The load allocation parameters are determined based on the actual load information and actual transportation distance of the goods. The logistics carbon footprint of goods is calculated based on the aforementioned transportation energy consumption parameters, fuel emission factors, and load allocation parameters.

2. The method according to claim 1, characterized in that, The transportation energy consumption parameters Calculated using the following formula: in, Idle emission factor, The congestion coefficient, For driving emission factors, This refers to instantaneous velocity.

3. The method according to claim 2, characterized in that, The load allocation parameters Calculated using the following formula: in, The target cargo weight. The actual transportation distance of the target goods. The first in the same vehicle Weight of each item For the first The actual transportation distance of the goods.

4. The method according to claim 3, characterized in that, Final logistics carbon footprint Calculated using the following formula: in, This refers to fuel emission factors.

5. The method according to claim 1, characterized in that, The passive IoT tag is powered by ambient radio frequency energy or a weak battery and periodically reports basic cargo information and location information.

6. The method according to claim 1, characterized in that, The positioning information is obtained by base station triangulation and / or GNSS-assisted positioning.

7. The method according to claim 1, characterized in that, The real-time traffic data includes at least one of the following: congestion index, traffic light waiting time, and vehicle instantaneous speed.

8. The method according to claim 1, characterized in that, The process of determining transportation energy consumption parameters also includes: Based on the actual transportation trajectory, identify the detours, congestion, or traffic control-related road sections, and include the corresponding driving energy consumption and / or idling energy consumption in the transportation energy consumption parameters.

9. The method according to claim 1, characterized in that, The actual carrying information includes at least one of the following: cargo weight, cargo volume ratio, and cargo transit time in the vehicle.

10. A logistics carbon footprint tracking device based on passive Internet of Things, characterized in that, include: The first module is used to deploy passive IoT tags on logistics vehicles or cargo packaging, and collect the location information and basic information of the passive IoT tags through a base station network; The second module is used to analyze the actual transportation trajectory of the goods based on the location information, and to determine the transportation energy consumption parameters based on the actual transportation trajectory and real-time road condition data. The third module is used to determine the load allocation parameters based on the actual load information and actual transportation distance of the goods. The fourth module is used to calculate the logistics carbon footprint of goods based on the transportation energy consumption parameters, fuel emission factors, and load allocation parameters.

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