CN117114209B - Method, device and equipment for predicting carbon emission of automobile in full life cycle - Google Patents

Method, device and equipment for predicting carbon emission of automobile in full life cycle Download PDF

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CN117114209B
CN117114209B CN202311374711.0A CN202311374711A CN117114209B CN 117114209 B CN117114209 B CN 117114209B CN 202311374711 A CN202311374711 A CN 202311374711A CN 117114209 B CN117114209 B CN 117114209B
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方振锋
马宏伟
王文浚
肖曲
沈冰
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Hubei Gimbol Environmental Technology Co ltd
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Abstract

The application relates to a method, a device and equipment for predicting carbon emission in a full life cycle of an automobile, wherein the method comprises the following steps: predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task; the corresponding preliminary oil consumption is adjusted through driving behavior data of a driver corresponding to each travel task, a first oil consumption range of the vehicle head for completing the corresponding travel task is obtained, and a second oil consumption range of the refrigerator for completing the corresponding travel task is predicted; and combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in a preset period, then predicting a second total oil consumption range of the automobile in a full life period, and finally predicting the carbon emission of the automobile in the full life period. The technical effect that this application had is: the method is used for accurately predicting the carbon emission of the automobile.

Description

Method, device and equipment for predicting carbon emission of automobile in full life cycle
Technical Field
The invention relates to the technical field of carbon emission prediction, in particular to a method, a device and equipment for predicting carbon emission in a full life cycle of an automobile.
Background
Along with the improvement of environmental protection consciousness and the increasing prominence of carbon emission problems, the accurate prediction of the carbon emission of automobiles has important significance for optimizing energy utilization and reducing environmental pollution. By accurately predicting the carbon emission of the automobile, enterprises can be helped to evaluate and improve the transportation scheme, and a transportation mode with lower carbon emission is selected, so that the influence on the environment is reduced.
However, there is currently a lack of specific techniques to accurately predict the carbon emissions of automobiles. The existing carbon emission prediction method is mainly based on parameters such as vehicle types, vehicle years, driving mileage and the like for estimation, and the estimation result lacks accuracy.
Therefore, there is a need for a method for predicting carbon emissions of an automobile in a full life cycle for accurately predicting the carbon emissions of the automobile.
Disclosure of Invention
The application provides a carbon emission prediction method, device and equipment for a full life cycle of an automobile, which are used for accurately predicting the carbon emission of the automobile.
In a first aspect, the present application provides a method for predicting carbon emission in a full life cycle of an automobile, which adopts the following technical scheme: acquiring the number of travel tasks of the automobile in a preset period and driving behavior data of a driver executing each travel task; predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of the goods to be transported of each travel task; the corresponding preliminary oil consumption is adjusted according to the driving behavior data of the driver corresponding to each travel task, and a first oil consumption range of the vehicle head for completing the corresponding travel task is obtained; predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to the basic information of the goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when the travel task is executed and the driving behavior data of the personnel corresponding to each travel task; combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in the preset period, and predicting a second total oil consumption range of the automobile in a full life period according to the first total oil consumption range, wherein the full life period comprises N preset periods, and N is greater than or equal to 1; and predicting the carbon emission of the automobile in the full life cycle according to the second total oil consumption range.
By adopting the technical scheme, the oil consumption range of the head of the automobile and the oil consumption range of the refrigerator of the automobile in the preset period are calculated respectively, the first total oil consumption range of the automobile in the preset period is obtained by combining the oil consumption ranges of the head and the refrigerator, then the second total oil consumption range of the automobile in the full life period is predicted according to the first total oil consumption range, and then the carbon emission of the automobile in the full life period is predicted. The method effectively combines the running characteristics and the energy consumption data of the automobile, can calculate the oil consumption range of the automobile more accurately, and adjusts the final oil consumption according to the driving behavior data of the driver, so that the final predicted oil consumption and the carbon emission in the whole life cycle calculated according to the oil consumption are more accurate and more convincing.
Optionally, the basic information of the goods to be transported of each trip task includes weight information, and predicting, according to the unit oil consumption of a head of the automobile, the distance of each trip task, and the basic information of the goods to be transported of each trip task, a preliminary oil consumption of the head to complete a corresponding trip task and an operation time of a refrigerator in the automobile to complete the corresponding trip task includes: predicting the initial fuel consumption of the locomotive for completing the corresponding travel task according to an initial fuel consumption formula, wherein the initial fuel consumption formula is that The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For preliminary fuel consumption>For the load factor->For the weight of the goods to be transported, +.>For the journey of each trip task +.>The unit oil consumption of the locomotive is; predicting the time spent by the headstock to complete the corresponding travel task according to a running time formula, wherein the time formula is thatThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Time spent for the head to complete the corresponding travel task, < >>For the journey of each trip task +.>For the average speed of the head +.>Is the oil consumption coefficient; and obtaining the running time of the refrigerator for completing the corresponding travel task according to the time spent by the vehicle head for completing the corresponding travel task.
By adopting the technical scheme, the preliminary oil consumption of the locomotive is estimated by combining the multidimensional information such as unit oil consumption, the distance and the weight of the goods to be transported of the locomotive, so that the final estimated preliminary oil consumption is more accurate, and the final estimated oil consumption and carbon emission in the whole life cycle of the automobile are more accurate. The time spent by the head for completing the travel task can be predicted through the information, so that the running time of the refrigerator can be directly estimated through the time spent by the head for completing the travel task, and the labor and material resources are directly reduced.
Optionally, the adjusting the corresponding preliminary fuel consumption according to the driving behavior data of the driver corresponding to each trip task to obtain a first fuel consumption range of the vehicle head for completing the corresponding trip task includes: obtaining driving behavior data of a driver corresponding to each trip task, wherein the driving behavior data comprises average sudden acceleration times, average sudden braking times and average overspeed times; according to the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver, scoring the driving behaviors of each driver to obtain the driving scores of each driver; calculating the extra fuel consumption of the drivers corresponding to each trip task according to the driving scores of the drivers; and adjusting the corresponding preliminary oil consumption by the extra oil consumption of the driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task.
Through adopting above-mentioned technical scheme, through obtaining the average rapid acceleration number of times of driver, average rapid braking number of times and average overspeed number of times etc. can score driver's driving behavior to can estimate the extra oil consumption that the driver consumed when carrying out corresponding task according to driving behavior score, then adjust preliminary oil consumption according to extra oil consumption, make the final predicted first oil consumption within a range's result more accurate, and then make final predicted car total oil consumption and total carbon emission more accurate in full life cycle.
Optionally, the step of scoring the driving behavior of each driver according to the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver to obtain the driving score of each driver includes: the average sudden acceleration times of each driver are distributed to be a first weight, the average sudden braking times are distributed to be a second weight, and the average overspeed times are distributed to be a third weight; arithmetically multiplying the first weight and the average rapid acceleration times of each driver to obtain a first score; arithmetically multiplying the second weight and the average sudden braking times of each driver to obtain a second score; arithmetically multiplying the third weight and the average overspeed times of all drivers to obtain a third score; and adding the first score, the second score and the third score to obtain the driving score of each driver.
By adopting the technical scheme, the dimensions such as the average sudden acceleration times, the average sudden braking times, the average overspeed times and the like of each driver are weighted, and then each driver is scored according to the weighted results. The weight can be set according to actual demands by self, so that the driving score of each finally obtained driver accords with the actual situation, and the preliminary fuel consumption is adjusted through the driving score of the driver, so that the method has flexibility and practicability.
Optionally, predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to the basic information of the goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when executing each travel task and the driving behavior data of the personnel corresponding to each travel task; comprising the following steps: obtaining a refrigerating temperature range suitable for the goods to be transported of each trip task according to the basic information of the goods to be transported of each trip task; combining a refrigerating temperature range adapted to goods to be transported of each trip task, the running time of a refrigerator in each trip task and the environmental temperature change rate when the trip task is executed to obtain a preliminary oil consumption range of the refrigerator; and adjusting the corresponding preliminary oil consumption range according to the driving behavior data of the driver corresponding to each trip task to obtain a second oil consumption range for the refrigerator to finish the corresponding trip task.
Through adopting above-mentioned technical scheme, through obtaining multidimensional information, estimate the preliminary oil consumption of refrigerator, then adjust preliminary oil consumption according to driver's driving behavior data for final second oil consumption scope that obtains is more accurate and more accords with reality, and also be convenient for follow-up calculation car oil consumption and carbon emission are more accurate in whole full life cycle.
Optionally, the predicting, according to the first total fuel consumption range, a second total fuel consumption range of the automobile in a full life cycle includes: predicting the number of travel tasks of the automobile in the full life cycle according to the number of travel tasks of the automobile in the preset period; combining the trip task quantity of the automobile in the full life cycle and the total oil consumption range of the automobile in the full life cycle to obtain the average oil consumption quantity of each trip task; and multiplying the average oil consumption of each trip task by the trip task quantity of the automobile in the full life cycle to obtain a second total oil consumption range of the automobile in the full life cycle.
By adopting the technical scheme, the average oil consumption of each trip task of the automobile in the preset period can be obtained according to the total oil consumption of the automobile in the preset period, and the total oil consumption and the total carbon emission of the automobile in the whole life period can be accurately predicted according to the average oil consumption of each trip task of the automobile in the preset period.
Optionally, the predicting the carbon emission of the automobile in the full life cycle according to the second total fuel consumption range includes: calculating the maximum carbon emission and the minimum carbon emission of the automobile in the full life cycle according to the second total oil consumption range; calculating a difference between the maximum carbon emission and the minimum carbon emission, and if the difference is greater than a preset difference, calculating a historical average carbon emission of the automobile according to the historical carbon emission data; predicting a first carbon emission of the automobile in a full life cycle according to the historical average carbon emission of the automobile; judging whether the first carbon emission is between the minimum carbon emission and the maximum carbon emission, if so, determining that the first carbon emission is the carbon emission of the automobile in a full life cycle; and if not, taking the intermediate quantity of the minimum carbon emission quantity and the maximum carbon emission quantity as the carbon emission quantity of the automobile in the whole life cycle.
By adopting the technical scheme, when the carbon emission of the automobile in the full life cycle is calculated, the carbon emission of the final automobile in the full life cycle can be determined by combining the historical average carbon emission of the automobile, and the specific determination method and parameters of the carbon emission of the automobile in the full life cycle can be adjusted and determined according to actual conditions, so that the finally obtained carbon emission of the automobile in the full life cycle is more accurate and flexible.
In a second aspect, the present application provides a carbon emission prediction apparatus for a full life cycle of a cold dining car, the apparatus comprising: the system comprises an acquisition module, a first prediction module, a second prediction module, a third prediction module, a combination module and a fourth prediction module; the acquisition module is used for acquiring the travel task times of the automobile in a preset period and driving behavior data of a driver executing each travel task; the first prediction module is used for predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of the goods to be transported of each travel task; the second prediction module is used for adjusting the corresponding preliminary oil consumption according to driving behavior data of a driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task; the third prediction module is used for predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to the basic information of the goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when the travel task is executed and the driving behavior data of the personnel corresponding to the travel task; the combination module is used for combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in the preset period, and predicting a second total oil consumption range of the automobile in a full life cycle according to the first total oil consumption range, wherein the full life cycle comprises N preset periods, and N is greater than or equal to 1; and the fourth prediction module is used for predicting the carbon emission of the automobile in the full life cycle according to the second total fuel consumption range.
By adopting the technical scheme, the oil consumption range of the head of the automobile and the oil consumption range of the refrigerator of the automobile in the preset period are calculated respectively, the first total oil consumption range of the automobile in the preset period is obtained by combining the oil consumption ranges of the head and the refrigerator, then the second total oil consumption range of the automobile in the full life period is predicted according to the first total oil consumption range, and then the carbon emission of the automobile in the full life period is predicted. The method effectively combines the running characteristics and the energy consumption data of the automobile, can calculate the oil consumption range of the automobile more accurately, and adjusts the final oil consumption according to the driving behavior data of the driver, so that the final predicted oil consumption and the carbon emission in the whole life cycle calculated according to the oil consumption are more accurate and more convincing.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme: the system comprises a processor, a memory, a user interface and a network interface, wherein the memory is used for storing instructions, the user interface and the network interface are used for communicating with other devices, and the processor is used for executing the instructions stored in the memory so as to enable the electronic device to execute a computer program of the carbon emission prediction method of the full life cycle of the automobile.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical solutions: a computer program capable of being loaded by a processor and executing any one of the above-described carbon emission prediction methods for a full life cycle of an automobile is stored.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method has the advantages that the operation characteristics and the energy consumption data of the automobile are effectively combined, the oil consumption range of the automobile can be calculated more accurately, the final oil consumption is adjusted through the driving behavior data of a driver, and the final predicted oil consumption and the carbon emission in the whole life cycle obtained through calculation according to the oil consumption are more accurate and more convincing;
2. the preliminary oil consumption of the locomotive is estimated by combining multidimensional information such as unit oil consumption, distance and weight of goods to be transported of the locomotive, so that the final estimated preliminary oil consumption is more accurate, and further the final estimated oil consumption and carbon emission in the whole life cycle of the automobile are more accurate. The time spent by the head for completing the travel task can be predicted, and the running time of the refrigerator can be directly estimated through the time spent by the head for completing the travel task, so that the labor and material resources are directly reduced;
3. The average oil consumption of each trip task of the automobile in the preset period can be obtained according to the total oil consumption of the automobile in the preset period, and the total oil consumption and the total carbon emission of the automobile in the whole life period can be accurately predicted according to the average oil consumption of each trip task of the automobile in the preset period.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting carbon emissions in a full life cycle of an automobile according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a carbon emission prediction device for a full life cycle of an automobile according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 1. an acquisition module; 2. a first prediction module; 3. a second prediction module; 4. a third prediction module; 5. a combining module; 6. a fourth prediction module; 1000. an electronic device; 1001. a processor; 1002. a communication bus; 1003. a user interface; 1004. a network interface; 1005. a memory.
Description of the embodiments
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.
In the description of embodiments of the present application, words such as "exemplary," "such as" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "illustrative," "such as" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "illustratively," "such as" or "for example," etc., is intended to present related concepts in a concrete fashion. In addition, unless otherwise indicated, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The vehicle according to the present application is a vehicle having a refrigerator, and may be specifically a refrigerator vehicle, and in the present application, the vehicle is understood to be a refrigerator vehicle unless otherwise specified.
For a refrigerated vehicle, it consists of two parts, the first part being the head part of the refrigerated vehicle, the head being used to control and manipulate the travel of the refrigerated vehicle; the second part is a refrigerator part, the refrigeration equipment in the refrigerator is usually composed of a compressor, an evaporator and a control system, the refrigeration equipment usually drives the compressor to work through an engine of a vehicle, the refrigerant is compressed and absorbs heat and reduces the temperature through the evaporator, so that the refrigeration effect of the refrigerator is realized, and therefore, the refrigeration process of the refrigerator needs the engine of the vehicle to run and consumes fuel such as gasoline or diesel oil; therefore, the oil consumption of the whole refrigerator car in the whole life cycle can be accurately predicted by predicting the oil consumption of the car head and the oil consumption of the refrigerator, so that the carbon emission of the whole refrigerator car is predicted according to the oil consumption of the whole life cycle; the full life cycle is the whole service cycle of the refrigerated vehicle, and it is to be noted that the application can accurately predict the carbon emission of the refrigerated vehicle in the whole service cycle and also predict the carbon emission of the refrigerated vehicle in any period of time.
The refrigerator car that this application relates to uses fuel as the energy, through the consumption to the fuel, calculates the carbon emission, and it should be noted that, this application also is applicable to electric refrigerator car equally, and electric refrigerator car uses electric energy as the energy, and the electric energy mainly relies on fossil fuel, like coal or natural gas, so also has the carbon emission, consequently, whether it is the refrigerator car of fuel or electric refrigerator car, this application all is applicable.
Similarly, the method is also applicable to common automobiles, and when the method provided by the application is used for predicting the carbon emission of the whole life cycle of the common automobiles, the influence of the refrigerator on the carbon emission is not required to be considered, and only the carbon emission of the automobiles is required to be considered. For a common automobile, the number of trips of the automobile in a preset period and driving behavior data of an automobile driver can be acquired, and the driver can be the same person or different persons, wherein the driving behavior data is determined according to actual conditions; then according to the unit fuel consumption of the automobile, the trip distance of each trip and whether other members or other articles are carried or not, if the other members or other articles are carried, acquiring the weight information of the other members or other articles (the weight information can also consider whether other requests are generated when the other members or other articles are taken into the automobile, for example, the automobile is required to be started in hot weather, the carried articles possibly need to be stored at low temperature or high temperature, and the like, and the fuel consumption of the automobile is also influenced, and the weight information is specific according to actual conditions); and then, the oil consumption of the automobile is predicted by combining the information such as the travel times of the automobile in a preset period, the driving behavior data of a driver, the unit oil consumption of the automobile, the weight of people or other articles carried in each travel and the like, so that the carbon emission in the preset period is obtained, and the carbon emission in the whole life period of the automobile can be accurately predicted.
The present application is described in further detail below with reference to the accompanying drawings.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings of the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments.
Fig. 1 is a flowchart of a method for predicting carbon emission in a full life cycle of an automobile according to an embodiment of the present application. It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows; the steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders; and at least some of the steps in fig. 1 may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur in sequence, but may be performed alternately or alternately with at least some of the other steps or sub-steps of other steps.
As shown in fig. 1, fig. 1 is a flowchart of a method for predicting carbon emission in a full life cycle of an automobile according to an embodiment of the present application, and the method includes S101-S106.
S101, acquiring the travel task times of the automobile in a preset period and driving behavior data of a driver executing each travel task.
In one example, the preset period may be one week, one month or one year, and if the automobile has a fixed trip task, the preset period may be set to one week or one month; if the travel task of the automobile is random, the preset period can be set to be one year, and the preset period can be set according to actual conditions.
According to the method, a preset period is taken as an example for a week, a worker can input the travel task times of the automobile in the future week to the computer equipment, the specific input information can also comprise the type and weight of goods transported by each travel task, the distance transported each time and the like, meanwhile, driving behavior data of the driver who executes each travel task also need to be acquired, and the driving behavior data can be acquired through a vehicle monitoring system, a GPS tracking device, a driver report, a camera, a sensor and the like, for example, the driving behavior data such as driving time, speed, sudden braking, acceleration and the like can be recorded and tracked through the vehicle monitoring system; driver reports may also provide a way of their driving behavior, such as time, speed, distance travelled, etc. to drive the vehicle.
S102, predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of goods to be transported of each travel task.
In one example, the unit fuel consumption of the vehicle head may be understood as the distance that the vehicle can travel per unit of fuel consumed during travel; the distance of each trip task can be understood as the distance to the destination and the road condition to the destination, the road condition can be a high-speed road section, a common road section, a mountain section, a smooth road section and the like, and the way of acquiring the distance and the road condition can be obtained according to a GPS positioning system; the basic information of the goods to be transported comprises the types of the goods, the weight of the goods and the like, and then the preliminary oil consumption of the corresponding travel tasks of the locomotive is predicted according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of the goods to be transported of each travel task.
The prediction mode can be adoptedThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>In order to make the fuel consumption be the initial fuel consumption,for the load factor->For the weight of the goods to be transported, +.>For the journey of each trip task +. >The unit oil consumption of the locomotive is;
load factor hereThe unit oil consumption is set by oneself, the unit oil consumption is in the unit of liter/hundred kilometers (L/100 km), the total distance is in the unit of kilometers, the formula converts the unit oil consumption into oil consumption of each kilometer, then multiply the total distance to obtain the basic oil consumption of the locomotive, multiply the load factor and the weight of the goods to be transported and the distance of each trip task to obtain the influence of the weight on the oil consumption; and then combining the weight of the goods to be transported to obtain the oil quantity required to be consumed by the whole locomotive.
The running time of the refrigerator for completing the corresponding travel task can be obtained through the time spent by the vehicle head for completing the corresponding travel task, and the running time of the refrigerator can be indirectly obtained through calculating the running time of the vehicle. The running time of the headstock can be controlled byCalculated, wherein->Time spent for the head to complete the corresponding travel task, < >>For the journey of each trip task +.>For the average speed of the head +.>Is the oil consumption coefficient; it should be noted that->Can be set according to the distance of the journey, for example, if the journey of the goods to be transported is larger than the set threshold value, the set threshold value can If the current trip task can be completed at least one day, the driver can perform operations such as midway refueling, midway rest and midway detection on goods to be transported, at this time, the refrigerator also needs to continue to run, and the total fuel consumption of the automobile can be increased accordingly>The value of the vehicle head is smaller than the normal value, and finally the time spent for completing the corresponding travel task of the vehicle head is increased; the current distance of the goods to be transported is too small, operations such as midway oiling and midway detecting the goods to be transported are not needed, and therefore, the goods to be transported are not needed to be filled in the middle>The larger the value of the vehicle head is relative to the normal value, the less time the vehicle head takes to finish the current travel task finally. After the running time of the corresponding travel task completed by the headstock is obtained, the running time of the corresponding travel task completed by the refrigerator can be obtained according to the running time of the corresponding travel task completed by the headstock.
And S103, adjusting the corresponding preliminary oil consumption by driving behavior data of a driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task.
In one example, each travel task has a corresponding driver, driving habits of different drivers are different, and the different driving habits also affect fuel consumption, so that driving behaviors of the drivers need to be obtained, preliminary fuel consumption of the corresponding travel task is completed by the vehicle head according to the driving behaviors, a first fuel consumption range is obtained, and the first fuel consumption range can be understood as a fuel consumption range finally consumed by the vehicle head.
It should be noted that, when calculating the oil quantity range finally consumed by the vehicle head corresponding to the travel task with a relatively short distance, the driving behavior data of the driver may not be considered, and for some mountain road sections or road sections with a bumpy road section, the driving behavior data of the driver needs to be considered seriously, and specifically whether the influence of the driving behavior data of the driver on the oil consumption is considered or not may be set according to the actual situation.
The driving behavior data is generally obtained through driving data of a historical driver, is obtained through mounting a vehicle monitoring system, GPS tracking equipment, a camera and a sensor in a vehicle body, can also be obtained through driving reports of the driver and the like, and can record and track the driving behavior data such as driving time, speed, sudden braking, acceleration and the like through the vehicle monitoring system; driver reports may also provide a way of their driving behavior, such as time, speed, distance travelled, etc. to drive the vehicle.
The corresponding preliminary oil consumption is adjusted through driving behavior data of a driver corresponding to each travel task, and a first oil consumption range of the vehicle head for completing the corresponding travel task is obtained, wherein the first oil consumption range comprises the following steps: the driving behavior data of a driver corresponding to each trip task are obtained, wherein the driving behavior data comprise average sudden acceleration times, average sudden braking times and average overspeed times; according to the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver, scoring the driving behaviors of each driver to obtain the driving scores of each driver; according to the driving scores of all drivers, calculating the extra fuel consumption of the drivers corresponding to each trip task; and adjusting the corresponding preliminary oil consumption by the extra oil consumption of the driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task.
In one example, the driving behavior data of the drivers include, but are not limited to, average sudden acceleration times, average sudden braking times, and average overspeed times, where the average sudden acceleration times, average sudden braking times, and average overspeed times may be obtained by obtaining driving behavior data of each driver in a past period of time, counting the sudden acceleration times, overspeed times, and sudden braking times of each driver in the past period of time, then obtaining travel times of each driver in the past period of time, obtaining average sudden acceleration times, average sudden braking times, and average overspeed times of each driver in each travel task, where the past period of time may be arbitrarily set, may be a past month, and the past year, and the specific setting is determined according to the actual situation.
After the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver are obtained, each driver can be subjected to driving grading, the more the average sudden acceleration times, the more the average sudden braking times and the more the average overspeed times are, the lower the corresponding driving grading is, and the driving grading can be graded according to a plurality of dimensions. The lower the driving score, the higher the additional fuel consumption; the higher the driving score, the lower the additional fuel consumption. And determining an additional fuel consumption according to driving behavior data of each driver, and then adjusting the corresponding preliminary fuel consumption through the additional fuel consumption to obtain a first fuel consumption range of the vehicle head for completing the corresponding travel task.
According to the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver, the driving behaviors of each driver are scored to obtain the driving scores of each driver, and the method comprises the following steps: the average sudden acceleration times of each driver are distributed to be a first weight, the average sudden braking times are distributed to be a second weight, and the average overspeed times are distributed to be a third weight; arithmetically multiplying the first weight and the average rapid acceleration times of each driver to obtain a first score; arithmetically multiplying the second weight and the average sudden braking times of each driver to obtain a second score; arithmetically multiplying the third weight and the average overspeed times of all drivers to obtain a third score; and adding the first score, the second score and the third score to obtain the driving score of each driver.
In one example, the average number of sudden acceleration of each driver is assigned to be a first weight, the average number of sudden braking is assigned to be a second weight, and the average number of overspeed is assigned to be a third weight, where the weight ratio may be set according to the actual requirement, and in addition, each dimension may be increased or decreased according to the actual requirement, for example, a fourth weight may be set, and the fourth weight may be a dimension such as the number of parking, and similarly, only any one or two dimensions of the average number of sudden acceleration, the average number of sudden braking, and the average number of overspeed may be considered, and the specific real-time manner may be set according to the actual situation.
The present application is illustrated in the three dimensions described above. According to the actual influence degree, the first weight ratio may be set to 20%, the second weight ratio may be set to 30%, the third weight ratio may be set to 50%, and the setting manner may not be unique. Then, arithmetically multiplying the first weight ratio by the average rapid acceleration times of each driver to obtain a first score; arithmetically multiplying the second weight and the average sudden braking times of each driver to obtain a second score; and arithmetically multiplying the third weight by the average overspeed times of all drivers to obtain a third score, and adding the first score, the second score and the third score to obtain the driving score of all drivers.
S104, predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to basic information of goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when each travel task is executed and driving behavior data of personnel corresponding to each travel task.
In one example, after the first oil consumption range of the locomotive for completing each trip task is obtained, a second oil consumption amount of the refrigerator for completing each trip task needs to be predicted. For the refrigerator, firstly, the adaptive temperature of the goods to be transported needs to be considered, for example, the goods to be transported can be seafood, flowers and meat products, and the required refrigeration temperatures of different types of goods to be transported are different, so that firstly, the types of the goods to be transported need to be determined, and according to the types of the goods to be transported, the adaptive temperature of the goods to be transported is determined, and different temperatures correspond to different oil consumption; secondly, the operation time of the refrigerator is the same as the operation of the refrigerator, and therefore, the longer the operation time of the refrigerator is, the more the oil consumption is; the second fuel consumption is also related to the change rate of the ambient temperature when each trip task is executed, and the change rate of the ambient temperature needs to be considered because the travel path is different due to the different time spent by different trip tasks, and long-distance travel or cross-region travel is possible. Generally, when an automobile runs across regions, the temperatures in different regions are different, the temperature difference between day and night is also different, the automobile needs to continuously adjust the temperature in the refrigerator by combining the temperature range required by the goods to be transported and the change rate of the external environment temperature, and the oil consumption is also different.
Therefore, after the requirements are combined, the initial oil consumption range required by the automobile to complete the corresponding travel task can be estimated initially, and then the final oil consumption range of the refrigerator, namely the second oil consumption range, is calculated by combining the initial oil consumption range and the driving behavior data of the corresponding personnel. It should be noted that, because the oil consumption of the refrigerator is also affected by some driving behaviors of corresponding personnel in the traveling process, for example, the service time of the refrigerator can be prolonged when the driver has a rest and needs to patrol and examine goods in the refrigerator, add oil in the middle, and the like, and meanwhile, the increase of the oil consumption can be affected, and the influence of the driving behavior data of the driver on the oil consumption can be calculated by referring to the influence of the driving behavior data of the corresponding driver on the oil consumption of the headstock, so that excessive description is not needed.
Predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to basic information of goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when each travel task is executed and driving behavior data of personnel corresponding to each travel task; comprising the following steps: according to the basic information of the goods to be transported of each trip task, obtaining a refrigerating temperature range suitable for the goods to be transported of each trip task; combining a refrigerating temperature range adapted to goods to be transported of each trip task, the running time of the refrigerator in each trip task and the environmental temperature change rate when executing each trip task to obtain a preliminary oil consumption range of the refrigerator; and adjusting the corresponding preliminary oil consumption range through driving behavior data of a driver corresponding to each travel task to obtain a second oil consumption range of the refrigerator for completing the corresponding travel task.
Reference may be made to the foregoing embodiments for specific embodiment, and redundant descriptions are not repeated herein.
S105, combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in a preset period, and predicting a second total oil consumption range of the automobile in a full life cycle according to the first total oil consumption range, wherein the full life cycle comprises N preset periods, and N is greater than or equal to 1.
In one example, after the first oil consumption range and the second oil consumption range are obtained, the total oil consumption of the automobile in the preset period, that is, the first total oil consumption, can be directly estimated; then, predicting the total oil consumption range of the automobile in the whole life cycle, namely a second total oil consumption range, according to the first total oil consumption range; the second total oil consumption range is predicted, and the number of travel tasks of the automobile in the whole life cycle can be predicted according to the number of travel tasks of the automobile in the preset period; for example, the travel task of the automobile in the preset period is ten times, the preset period is one month, the travel times of the automobile in one year can be estimated according to the travel times of the automobile in one month, the estimated travel times of the automobile in one year can be considered in combination with other dimensions, for example, the season dimensions (the travel times of different seasons may be different), the history travel times and the like can be considered. After the travel times of the automobile in one year are obtained, the travel times of the whole automobile in the whole life cycle can be predicted, and the whole life cycle can be predicted by referring to dimensions such as the average service life of the automobile, the current service life of the automobile, maintenance data of the automobile and the like, and redundant description is omitted.
After the total number of travel tasks of the automobile in the full life cycle is obtained, multiplying the average oil consumption of the automobile in the preset period in each travel with the total number of travel tasks of the automobile in the full life cycle to obtain a second total oil consumption of the automobile in the full life cycle.
Predicting a second total fuel consumption range of the automobile in the full life cycle according to the first total fuel consumption range, wherein the method comprises the following steps: predicting the number of travel tasks of the automobile in the whole life cycle according to the number of travel tasks of the automobile in the preset period; combining the trip task quantity of the automobile in the full life cycle and the total oil consumption range of the automobile in the full life cycle to obtain the average oil consumption quantity of each trip task; and multiplying the average fuel consumption of each trip task by the number of the trip tasks of the automobile in the full life cycle to obtain a second total fuel consumption range of the automobile in the full life cycle.
The specific implementation manner can refer to the above embodiment, and the total oil consumption of each travel task is obtained mainly by obtaining the total oil consumption of the automobile in the period, then the travel times in the full life period are obtained, and the travel times in the full life period are multiplied by the total oil consumption of each travel task to obtain the total oil consumption range of the automobile in the full life period.
S106, predicting the carbon emission of the automobile in the full life cycle according to the second total fuel consumption range.
In one example, the maximum carbon emission and the minimum carbon emission of the vehicle in the full life cycle may be obtained according to the second total fuel consumption range, and when the maximum carbon emission and the minimum carbon emission are calculated according to the total fuel consumption range, the maximum carbon emission and the minimum carbon emission may be calculated according to the vehicle carbon emission=mileage×unit fuel consumption×emission factor, where the unit fuel consumption refers to the fuel consumption per kilometer, and the emission factor is determined according to the type of fuel used, for example, the carbon dioxide emission generated per liter of gasoline or diesel is about 2.3-2.7 kg of carbon dioxide. And then predicting the carbon emission of the automobile in the whole life cycle according to the method.
Predicting the carbon emission of the automobile in the full life cycle according to the second total fuel consumption range, wherein the method comprises the following steps of: calculating the maximum carbon emission and the minimum carbon emission of the automobile in the full life cycle according to the second total oil consumption range; calculating a difference between the maximum carbon emission and the minimum carbon emission, and if the difference is greater than a preset difference, calculating a historical average carbon emission of the automobile according to historical carbon emission data; predicting a first carbon emission of the automobile in a full life cycle according to the historical average carbon emission of the automobile; judging whether the first carbon emission is between the minimum carbon emission and the maximum carbon emission, if so, determining that the first carbon emission is the carbon emission of the automobile in the full life cycle; and if not, taking the intermediate quantity of the minimum carbon emission quantity and the maximum carbon emission quantity as the carbon emission quantity of the automobile in the whole life cycle.
In one example, since the fuel consumption determined in the present application is a range, to obtain a specific fuel consumption value and a specific carbon emission amount, calculation may be performed according to the following method.
Calculating a difference between a maximum carbon emission amount and a minimum carbon emission amount, if the difference is larger than a preset difference, the preset difference is set by itself according to actual conditions, and in general, the difference is too large, and it is difficult to determine a specific fuel consumption amount and a specific carbon emission amount, so that when the difference is too large, a historical fuel consumption amount and a carbon emission amount can be considered, an average carbon emission amount of an automobile history can be calculated, and according to the historical average carbon emission amount of the automobile, a carbon emission amount of the automobile in a full life cycle can be calculated, namely, a first carbon emission amount, and then whether the first carbon emission amount is between the minimum carbon emission amount and the maximum carbon emission amount is judged, if so, the first carbon emission amount can be selected as the specific carbon emission value, and if not, an intermediate amount of the minimum carbon emission amount and the maximum carbon emission amount can be selected as the carbon emission amount of the automobile in the full life cycle. Of course, there are various methods for obtaining a specific fuel consumption value and a specific carbon emission, and the values may be obtained according to actual situations in the actual application process.
Based on the method, the embodiment of the application also discloses a schematic diagram of a carbon emission prediction device of the whole life cycle of the automobile.
FIG. 2 is a schematic diagram of a carbon emission prediction device for a full life cycle of an automobile according to an embodiment of the present disclosure; the device comprises: the system comprises an acquisition module 1, a first prediction module 2, a second prediction module 3, a third prediction module 4, a combination module 5 and a fourth prediction module 6; the acquisition module 1 is used for acquiring the travel task times of the automobile in a preset period and driving behavior data of a driver executing each travel task; the first prediction module 2 is used for predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of the goods to be transported of each travel task; the second prediction module 3 is configured to adjust the corresponding preliminary fuel consumption according to driving behavior data of a driver corresponding to each travel task, so as to obtain a first fuel consumption range in which the vehicle head completes the corresponding travel task; the third prediction module 4 is configured to predict a second oil consumption range of the refrigerator for completing the corresponding travel task according to basic information of goods to be transported in each travel task, running time of the refrigerator in each travel task, an environmental temperature change rate when executing each travel task, and driving behavior data of a person corresponding to each travel task; the combination module 5 is used for combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in a preset period, and predicting a second total oil consumption range of the automobile in a full life cycle according to the first total oil consumption range, wherein the full life cycle comprises N preset periods, and N is greater than or equal to 1; the fourth prediction module 6 is configured to predict a carbon emission of the vehicle in a full life cycle according to the second total fuel consumption range.
In one example, the device is further configured to predict a preliminary fuel consumption of the vehicle head for completing the corresponding travel task according to a preliminary fuel consumption formula, where the preliminary fuel consumption formula is that；
Wherein,for preliminary fuel consumption>For the load factor->For the weight of the goods to be transported, +.>For the journey of each trip task +.>The unit oil consumption of the locomotive is;
predicting the time spent by the headstock to complete the corresponding travel task according to a running time formula,the time formula isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Time spent for the head to complete the corresponding travel task, < >>For the journey of each trip task,for the average speed of the head +.>Is the oil consumption coefficient; and obtaining the running time of the refrigerator for completing the corresponding travel task according to the time spent by the vehicle head for completing the corresponding travel task.
In one example, the device is further configured to obtain driving behavior data of a driver corresponding to each trip task, where the driving behavior data includes an average sudden acceleration number, an average sudden braking number, and an average overspeed number; according to the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver, scoring the driving behaviors of each driver to obtain the driving scores of each driver; according to the driving scores of all drivers, calculating the extra fuel consumption of the drivers corresponding to each trip task; and adjusting the corresponding preliminary oil consumption by the extra oil consumption of the driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task.
In one example, the device is further configured to assign an average number of sudden acceleration of each driver to be a first weight, an average number of sudden braking to be a second weight, and an average number of overspeed to be a third weight; arithmetically multiplying the first weight and the average rapid acceleration times of each driver to obtain a first score; arithmetically multiplying the second weight and the average sudden braking times of each driver to obtain a second score; arithmetically multiplying the third weight and the average overspeed times of all drivers to obtain a third score; and adding the first score, the second score and the third score to obtain the driving score of each driver.
In one example, the device is further used for obtaining a refrigerating temperature range suitable for the goods to be transported of each trip task according to the basic information of the goods to be transported of each trip task; combining a refrigerating temperature range adapted to goods to be transported of each trip task, the running time of the refrigerator in each trip task and the environmental temperature change rate when each trip task is executed to obtain a preliminary oil consumption range of the refrigerator; and adjusting the corresponding preliminary oil consumption range through driving behavior data of a driver corresponding to each travel task to obtain a second oil consumption range of the refrigerator for completing the corresponding travel task.
In one example, the device is further configured to predict the number of travel tasks of the vehicle in the full life cycle according to the number of travel tasks of the vehicle in the preset cycle; combining the trip task quantity of the automobile in the full life cycle and the total oil consumption range of the automobile in the full life cycle to obtain the average oil consumption quantity of each trip task; and multiplying the average fuel consumption of each trip task by the number of the trip tasks of the automobile in the full life cycle to obtain a second total fuel consumption range of the automobile in the full life cycle.
In one example, the apparatus is further configured to calculate a maximum carbon emission and a minimum carbon emission for the vehicle over a full life cycle based on the second total fuel consumption range; calculating a difference between the maximum carbon emission and the minimum carbon emission, and if the difference is greater than a preset difference, calculating a historical average carbon emission of the automobile according to historical carbon emission data; predicting a first carbon emission of the automobile in a full life cycle according to the historical average carbon emission of the automobile; judging whether the first carbon emission is between the minimum carbon emission and the maximum carbon emission, if so, determining that the first carbon emission is the carbon emission of the automobile in the full life cycle; and if not, taking the intermediate quantity of the minimum carbon emission quantity and the maximum carbon emission quantity as the carbon emission quantity of the automobile in the whole life cycle.
It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.
Embodiments of the present application provide a computer readable storage medium storing instructions that, when executed, perform one or more of the methods of the embodiments described above.
The following takes fig. 3 as an example, and a schematic structural diagram of the electronic device in the example of the present application will be described in detail.
A schematic structural diagram of an electronic device is provided for an embodiment of the present application. As shown in fig. 3, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.
Wherein the communication bus 1002 is used to enable connected communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1001 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1001 and may be implemented by a single chip.
The Memory 1005 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 1005 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 3, an operating system, a network communication module, a user interface module, and an application program of a carbon emission prediction method for a full life cycle of an automobile may be included in a memory 1005 as a computer storage medium.
In the electronic device 1000 shown in fig. 3, the user interface 1003 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke an application in the memory 1005 that stores a method for predicting carbon emissions for a full life cycle of an automobile, which when executed by one or more processors, causes the electronic device to perform the method as described in one or more of the embodiments above.
An electronic device readable storage medium storing instructions. When executed by one or more processors, cause an electronic device to perform the method as described in one or more of the embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims (8)

1. A method for predicting carbon emissions in a full life cycle of an automobile, the method comprising:
acquiring the number of travel tasks of the automobile in a preset period and driving behavior data of a driver executing each travel task;
predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of the goods to be transported of each travel task;
The corresponding preliminary oil consumption is adjusted according to the driving behavior data of the driver corresponding to each travel task, and a first oil consumption range of the vehicle head for completing the corresponding travel task is obtained;
predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to the basic information of the goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when the travel task is executed and the driving behavior data of the personnel corresponding to each travel task;
combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in the preset period, and predicting a second total oil consumption range of the automobile in a full life period according to the first total oil consumption range, wherein the full life period comprises N preset periods, and N is greater than or equal to 1;
predicting the carbon emission of the automobile in the full life cycle according to the second total fuel consumption range;
the method for predicting the initial oil consumption of the locomotive to complete the corresponding travel task and the running time of the refrigerator in the automobile to complete the corresponding travel task comprises the following steps:
Predicting the initial fuel consumption of the locomotive for completing the corresponding travel task according to an initial fuel consumption formula, wherein the initial fuel consumption formula is psi=k 1 X M x s+ (n/100) S; wherein,
psi is the initial fuel consumption, K 1 As load factor, M is the weight of the goods to be transportedThe quantity S is the distance of each trip task, and n is the unit oil consumption of the locomotive;
predicting time spent by the headstock for completing corresponding travel tasks according to a running time formula, wherein the time formula is T=S/V+S/K 2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
t is the time spent by the headstock for completing the corresponding trip task, S is the distance of each trip task, V is the average speed of the headstock, K 2 Is the oil consumption coefficient;
obtaining the running time of the refrigerator for completing the corresponding travel task according to the time spent by the vehicle head for completing the corresponding travel task;
the second oil consumption range of the refrigerator for completing the corresponding travel task is predicted according to the basic information of the goods to be transported of each travel task, the running time of the refrigerator in each travel task, the environmental temperature change rate when the travel task is executed and the driving behavior data of the personnel corresponding to the travel task; comprising the following steps:
Obtaining a refrigerating temperature range suitable for the goods to be transported of each trip task according to the basic information of the goods to be transported of each trip task;
combining a refrigerating temperature range adapted to goods to be transported of each trip task, the running time of a refrigerator in each trip task and the environmental temperature change rate when the trip task is executed to obtain a preliminary oil consumption range of the refrigerator; and adjusting the corresponding preliminary oil consumption range according to the driving behavior data of the driver corresponding to each trip task to obtain a second oil consumption range for the refrigerator to finish the corresponding trip task.
2. The method for predicting carbon emissions in a full life cycle of an automobile according to claim 1, wherein the adjusting the corresponding preliminary fuel consumption according to driving behavior data of a driver corresponding to each trip task to obtain a first fuel consumption range for the vehicle head to complete the corresponding trip task includes:
obtaining driving behavior data of a driver corresponding to each trip task, wherein the driving behavior data comprises average sudden acceleration times, average sudden braking times and average overspeed times;
According to the average sudden acceleration times, the average sudden braking times and the average overspeed times of each driver, scoring the driving behaviors of each driver to obtain the driving scores of each driver;
calculating the extra fuel consumption of the drivers corresponding to each trip task according to the driving scores of the drivers;
and adjusting the corresponding preliminary oil consumption by the extra oil consumption of the driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task.
3. The method for predicting carbon emissions in a full life cycle of an automobile according to claim 2, wherein the scoring the driving behavior of each driver according to the average number of sudden acceleration, the average number of sudden braking and the average number of overspeed of each driver to obtain the driving score of each driver comprises:
the average sudden acceleration times of each driver are distributed to be a first weight, the average sudden braking times are distributed to be a second weight, and the average overspeed times are distributed to be a third weight;
arithmetically multiplying the first weight and the average rapid acceleration times of each driver to obtain a first score;
Arithmetically multiplying the second weight and the average sudden braking times of each driver to obtain a second score;
arithmetically multiplying the third weight and the average overspeed times of all drivers to obtain a third score;
and adding the first score, the second score and the third score to obtain the driving score of each driver.
4. The method for predicting carbon emissions in a full life cycle of an automobile of claim 1, wherein predicting a second total fuel consumption range of the automobile over the full life cycle based on the first total fuel consumption range comprises:
predicting the number of travel tasks of the automobile in the full life cycle according to the number of travel tasks of the automobile in the preset period;
combining the trip task quantity of the automobile in the full life cycle and the total oil consumption range of the automobile in the full life cycle to obtain the average oil consumption quantity of each trip task;
and multiplying the average oil consumption of each trip task by the trip task quantity of the automobile in the full life cycle to obtain a second total oil consumption range of the automobile in the full life cycle.
5. The method for predicting carbon emissions in a full life cycle of an automobile according to claim 1, wherein predicting the carbon emissions in the full life cycle of the automobile from the second total fuel consumption range comprises:
Calculating the maximum carbon emission and the minimum carbon emission of the automobile in the full life cycle according to the second total oil consumption range; calculating a difference between the maximum carbon emission and the minimum carbon emission, and if the difference is greater than a preset difference, calculating a historical average carbon emission of the automobile according to historical carbon emission data;
predicting a first carbon emission of the automobile in a full life cycle according to the historical average carbon emission of the automobile;
judging whether the first carbon emission is between the minimum carbon emission and the maximum carbon emission, if so, determining that the first carbon emission is the carbon emission of the automobile in a full life cycle;
and if not, taking the intermediate quantity of the minimum carbon emission quantity and the maximum carbon emission quantity as the carbon emission quantity of the automobile in the whole life cycle.
6. A carbon emission prediction device for a full life cycle of an automobile, the device comprising: the system comprises an acquisition module (1), a first prediction module (2), a second prediction module (3), a third prediction module (4), a combination module (5) and a fourth prediction module (6); wherein,
the acquisition module (1) is used for acquiring the travel task times of the automobile in a preset period and driving behavior data of a driver executing each travel task;
The first prediction module (2) is used for predicting the initial oil consumption of the locomotive for completing the corresponding travel task and the running time of the refrigerator in the automobile for completing the corresponding travel task according to the unit oil consumption of the locomotive in the automobile, the distance of each travel task and the basic information of the goods to be transported of each travel task;
the second prediction module (3) is used for adjusting the corresponding preliminary oil consumption according to driving behavior data of a driver corresponding to each travel task to obtain a first oil consumption range of the vehicle head for completing the corresponding travel task;
the third prediction module (4) is used for predicting a second oil consumption range of the refrigerator for completing the corresponding travel task according to the basic information of the goods to be transported of each travel task, the running time of the refrigerator in each travel task, the ambient temperature change rate when the travel task is executed and the driving behavior data of the personnel corresponding to the travel task;
the combination module (5) is used for combining the first oil consumption range and the second oil consumption range to obtain a first total oil consumption range of the automobile in the preset period, and predicting a second total oil consumption range of the automobile in a full life period according to the first total oil consumption range, wherein the full life period comprises N preset periods, and N is greater than or equal to 1;
The fourth prediction module (6) is used for predicting the carbon emission of the automobile in the full life cycle according to the second total fuel consumption range;
the first prediction module (2) is further configured to predict, according to a preliminary fuel consumption formula, a preliminary fuel consumption amount of the vehicle head for completing a corresponding travel task, where the preliminary fuel consumption formula is ψ=K 1 X M x s+ (n/100) S; wherein psi is the initial oil consumption, K 1 As a load factor, M is the weight of goods to be transported, S is the distance of each trip task, and n is the unit oil consumption of the locomotive; predicting time spent by the headstock for completing corresponding travel tasks according to a running time formula, wherein the time formula is T=S/V+S/K 2 The method comprises the steps of carrying out a first treatment on the surface of the Wherein T is the time spent by the head to complete the corresponding travel task, S is the distance of each travel task, V is the average speed of the head, K 2 Is the oil consumption coefficient; obtaining the running time of the refrigerator for completing the corresponding travel task according to the time spent by the vehicle head for completing the corresponding travel task;
the third prediction module (4) is further used for obtaining a refrigerating temperature range suitable for the goods to be transported of each trip task according to the basic information of the goods to be transported of each trip task; combining a refrigerating temperature range adapted to goods to be transported of each trip task, the running time of a refrigerator in each trip task and the environmental temperature change rate when the trip task is executed to obtain a preliminary oil consumption range of the refrigerator; and adjusting the corresponding preliminary oil consumption range according to the driving behavior data of the driver corresponding to each trip task to obtain a second oil consumption range for the refrigerator to finish the corresponding trip task.
7. An electronic device comprising a processor, a memory, a user interface, and a network interface, the memory to store instructions, the user interface and the network interface to communicate to other devices, the processor to execute the instructions stored in the memory to cause the electronic device to perform the method of any of claims 1-5.
8. A computer readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any of claims 1-5.
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006133010A (en) * 2004-11-04 2006-05-25 Toyota Motor Corp Fuel information display unit
JP2009031046A (en) * 2007-07-25 2009-02-12 Hitachi Ltd Fuel consumption estimation system of automobile, route searching system, and driving guiding system
JP2010108065A (en) * 2008-10-28 2010-05-13 Fujitsu Ltd Emission amount proportional division device and emission amount proportional division program
CN108877274A (en) * 2018-09-23 2018-11-23 王虹 Air navigation aid, device and terminal based on cold chain
CN109374010A (en) * 2018-10-21 2019-02-22 胡佩仙 Automobile fuel filler based reminding method, device and mobile terminal based on cold chain
CN109405845A (en) * 2018-10-07 2019-03-01 宁夏中科汇联科技服务有限公司 Oiling based reminding method, device and mobile terminal based on cold chain transportation
CN109872075A (en) * 2019-03-05 2019-06-11 北京经纬恒润科技有限公司 A kind of appraisal procedure and system of the driving behavior of oil consumption correlation
CN110716527A (en) * 2019-09-09 2020-01-21 北京航空航天大学 Vehicle energy consumption analysis method and analysis system based on kinematic segments
CN116135653A (en) * 2021-11-16 2023-05-19 博泰车联网(武汉)有限公司 Method for forming image of driving behavior
CN116579527A (en) * 2023-07-14 2023-08-11 车泊喜智能科技(山东)有限公司 Intelligent analysis management system for oil statistical data of enterprise

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120229469A1 (en) * 2010-11-08 2012-09-13 Lizotte Todd E Dynamic 3d analytic tools: a method for maintaining situational awareness during high tempo warfare or mass casualty operations
EP4215402A1 (en) * 2022-01-20 2023-07-26 Volvo Truck Corporation Processing unit for managing energy consumption in a vehicle and method therefor

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006133010A (en) * 2004-11-04 2006-05-25 Toyota Motor Corp Fuel information display unit
JP2009031046A (en) * 2007-07-25 2009-02-12 Hitachi Ltd Fuel consumption estimation system of automobile, route searching system, and driving guiding system
JP2010108065A (en) * 2008-10-28 2010-05-13 Fujitsu Ltd Emission amount proportional division device and emission amount proportional division program
CN108877274A (en) * 2018-09-23 2018-11-23 王虹 Air navigation aid, device and terminal based on cold chain
CN109405845A (en) * 2018-10-07 2019-03-01 宁夏中科汇联科技服务有限公司 Oiling based reminding method, device and mobile terminal based on cold chain transportation
CN109374010A (en) * 2018-10-21 2019-02-22 胡佩仙 Automobile fuel filler based reminding method, device and mobile terminal based on cold chain
CN109872075A (en) * 2019-03-05 2019-06-11 北京经纬恒润科技有限公司 A kind of appraisal procedure and system of the driving behavior of oil consumption correlation
CN110716527A (en) * 2019-09-09 2020-01-21 北京航空航天大学 Vehicle energy consumption analysis method and analysis system based on kinematic segments
CN116135653A (en) * 2021-11-16 2023-05-19 博泰车联网(武汉)有限公司 Method for forming image of driving behavior
CN116579527A (en) * 2023-07-14 2023-08-11 车泊喜智能科技(山东)有限公司 Intelligent analysis management system for oil statistical data of enterprise

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