技术领域Technical Field
本发明涉及碳排放监测技术领域，尤其涉及到一种大数据支持的交通碳排放监测方法及监测系统。The present invention relates to the technical field of carbon emission monitoring, and in particular to a traffic carbon emission monitoring method and monitoring system supported by big data.
背景技术Background technique
交通碳排放监测对于评估交通系统的环境影响、推动绿色出行和应对气候变化等方面具有重要意义。目前，交通碳排放主要来源于城市公路运输，为了更好地监测和管理城市交通碳排放，提高城市低碳发展水平，各类交通碳排放监测方法和技术应运而生。Traffic carbon emission monitoring is of great significance for evaluating the environmental impact of the transportation system, promoting green travel and responding to climate change. At present, traffic carbon emissions mainly come from urban road transportation. In order to better monitor and manage urban traffic carbon emissions and improve the level of urban low-carbon development, various traffic carbon emission monitoring methods and technologies have emerged.
然而，现有针对城市公路运输的碳排放监测方案：一方面，采用车流量间接计算或基于能源消耗总量和各类能源的排放系数进行估算，存在着计算精度不高、无法实现城市碳排放区域布局分析等问题；另一方面，通过在城市内布设大量的碳排放固定监测终端，具有检测范围广、成本低等优势，但由于长期暴露在户外，受环境影响导致损坏率高，使得监测数据可靠性低且因为固定设置存在着便携性和适应性差的问题；虽然，碳排放移动监测终端的投放能够改善监测数据的可靠性、便携性和适应性，但成本高导致的碳排放移动监测终端的投放数量稀少依旧会影响整个监测区域的碳排放资源调度，无法适应复杂多变的实际场景。However, existing carbon emission monitoring schemes for urban road transport: on the one hand, indirect calculations based on vehicle flow or estimates based on total energy consumption and emission coefficients of various energy sources have problems such as low calculation accuracy and inability to implement urban carbon emission regional layout analysis; on the other hand, a large number of fixed carbon emission monitoring terminals are deployed in the city, which have the advantages of wide detection range and low cost. However, due to long-term exposure to the outdoors and high damage rate caused by environmental influences, the reliability of monitoring data is low, and the fixed setting has problems with portability and adaptability; although the deployment of mobile carbon emission monitoring terminals can improve the reliability, portability and adaptability of monitoring data, the small number of mobile carbon emission monitoring terminals caused by high costs will still affect the scheduling of carbon emission resources in the entire monitoring area and cannot adapt to complex and changeable actual scenarios.
因此，如何提高城市交通碳排放监测的准确性、资源调度能力和场景适应性，是一个亟需解决的技术问题。Therefore, how to improve the accuracy, resource scheduling capabilities and scenario adaptability of urban traffic carbon emissions monitoring is a technical problem that needs to be solved urgently.
发明内容Summary of the invention
本发明的主要目的在于提供一种大数据支持的交通碳排放监测方法及监测系统，旨在解决目前城市公路运输的碳排放监测方案存在的准确性、资源调度能力和场景适应性不够理想的技术问题。The main purpose of the present invention is to provide a big data-supported transportation carbon emission monitoring method and monitoring system, aiming to solve the technical problems of the current urban road transportation carbon emission monitoring scheme, such as poor accuracy, resource scheduling capability and scenario adaptability.
为实现上述目的，本发明提供一种大数据支持的交通碳排放监测方法，包括：To achieve the above object, the present invention provides a traffic carbon emission monitoring method supported by big data, comprising:
获取目标监测区域中的碳排放固定监测信息；其中，所述碳排放固定监测信息被配置为设置于所述目标监测区域中若干个固定监测点的碳排放固定监测终端采集的历史碳排放数据；Obtaining fixed carbon emission monitoring information in a target monitoring area; wherein the fixed carbon emission monitoring information is configured as historical carbon emission data collected by fixed carbon emission monitoring terminals set at a plurality of fixed monitoring points in the target monitoring area;
基于所述碳排放固定监测信息，将目标监测区域划分为若干个碳排放重点监测子区域和碳排放非重点监测子区域；Based on the fixed carbon emission monitoring information, the target monitoring area is divided into a plurality of carbon emission key monitoring sub-areas and carbon emission non-key monitoring sub-areas;
根据每个碳排放重点监测子区域中固定监测点的分布比例，生成碳排放移动监测策略，将所述碳排放移动监测策略发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作；Generate a carbon emission mobile monitoring strategy according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area, and send the carbon emission mobile monitoring strategy to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform an initial carbon emission mobile monitoring action;
获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数，利用所述监测影响参数，调整所述碳排放移动监测策略，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作；Acquire monitoring influence parameters when each carbon emission key monitoring sub-area performs a carbon emission mobile monitoring action, and use the monitoring influence parameters to adjust the carbon emission mobile monitoring strategy to drive the carbon emission mobile monitoring terminal to perform the adjusted carbon emission mobile monitoring action;
根据所述碳排放固定监测终端在目标监测周期内采集的碳排放非重点监测子区域的第一碳排放数据和碳排放移动监测终端在目标监测周期内执行碳排放移动监测动作采集的碳排放重点监测子区域的第二碳排放数据，生成目标区域的交通碳排放监测信息。Traffic carbon emission monitoring information of the target area is generated based on the first carbon emission data of the non-key monitoring sub-area of carbon emission collected by the fixed carbon emission monitoring terminal during the target monitoring period and the second carbon emission data of the key monitoring sub-area of carbon emission collected by the mobile carbon emission monitoring terminal during the target monitoring period when performing the mobile carbon emission monitoring action.
可选的，获取目标监测区域中的碳排放固定监测信息步骤，具体包括：Optionally, the step of obtaining fixed carbon emission monitoring information in the target monitoring area specifically includes:
接收目标监测区域中每个碳排放固定监测终端采集并上传的若干条碳排放采集信息；其中，每条所述碳排放采集信息包括碳排放数据、碳排放采集时间戳和固定监测点位置；Receive a number of carbon emission collection information collected and uploaded by each carbon emission fixed monitoring terminal in the target monitoring area; wherein each of the carbon emission collection information includes carbon emission data, carbon emission collection timestamp and fixed monitoring point location;
根据所述碳排放采集时间戳，提取目标监测周期的历史关联时段内的若干条目标碳排放采集信息，并基于每条目标碳排放采集信息中的碳排放数据和固定监测点位置，构建碳排放固定监测信息。According to the carbon emission collection timestamp, several pieces of target carbon emission collection information within the historical associated time period of the target monitoring cycle are extracted, and based on the carbon emission data and the fixed monitoring point position in each piece of target carbon emission collection information, carbon emission fixed monitoring information is constructed.
可选的，基于所述碳排放固定监测信息，将目标监测区域划分为若干个碳排放重点监测子区域和碳排放非重点监测子区域步骤，具体包括：Optionally, based on the fixed carbon emission monitoring information, the target monitoring area is divided into a plurality of carbon emission key monitoring sub-areas and carbon emission non-key monitoring sub-areas, specifically comprising:
提取碳排放固定监测信息中每个固定监测点在历史关联时段内的碳排放数据，输入碳排放预测模型获得目标监测周期内的碳排放预测数据；Extract the carbon emission data of each fixed monitoring point in the carbon emission fixed monitoring information within the historical associated period, and input it into the carbon emission prediction model to obtain the carbon emission prediction data within the target monitoring period;
判断每个固定监测点在目标监测周期内碳排放预测数据是否超过预设指标，若是，将该固定监测点定义为重点监测点，若否，定义为非重点监测点；Determine whether the carbon emission forecast data of each fixed monitoring point exceeds the preset index during the target monitoring period. If yes, define the fixed monitoring point as a key monitoring point; if no, define it as a non-key monitoring point;
将相连的若干个重点监测点对应的区域范围划分为一个碳排放重点监测子区域，将目标监测区域中所有碳排放重点监测子区域之外的其他每个独立区域划分为碳排放非重点监测区域。The area corresponding to several connected key monitoring points is divided into a carbon emission key monitoring sub-area, and each independent area outside all carbon emission key monitoring sub-areas in the target monitoring area is divided into a carbon emission non-key monitoring area.
可选的，提取碳排放固定监测信息中每个固定监测点在历史关联时段内的碳排放数据，输入碳排放预测模型获得目标监测周期内的碳排放预测数据步骤之前，所述方法，还包括：Optionally, before the step of extracting the carbon emission data of each fixed monitoring point in the carbon emission fixed monitoring information within the historical associated period and inputting the data into the carbon emission prediction model to obtain the carbon emission prediction data within the target monitoring period, the method further includes:
获取碳排放固定监测数据库中目标预测周期内的碳排放固定监测数据；其中，所述目标预测周期包括若干个历史监测周期对应的历史关联时段；Obtaining fixed carbon emission monitoring data within a target prediction period in a fixed carbon emission monitoring database; wherein the target prediction period includes historical associated time periods corresponding to a number of historical monitoring periods;
将目标预测周期内每个历史关联时段的碳排放固定监测数据构建作为训练样本，将每个历史关联时段对应的目标监测周期的碳排放固定监测数据附加为所述训练样本的标注信息；The fixed monitoring data of carbon emissions in each historical associated period within the target prediction period is constructed as a training sample, and the fixed monitoring data of carbon emissions in the target monitoring period corresponding to each historical associated period is attached as the annotation information of the training sample;
对附加有标注信息的所述训练样本进行特征提取输入每个固定监测点构建的初始卷积神经网络模型进行训练，获得训练完成的碳排放预测模型。The training samples with the annotated information are subjected to feature extraction and input into the initial convolutional neural network model constructed at each fixed monitoring point for training to obtain a trained carbon emission prediction model.
可选的，根据每个碳排放重点监测子区域中固定监测点的分布比例，生成碳排放移动监测策略，将所述碳排放移动监测策略发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作步骤，具体包括：Optionally, a carbon emission mobile monitoring strategy is generated according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area, and the carbon emission mobile monitoring strategy is sent to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform the initial carbon emission mobile monitoring action steps, specifically including:
获取目标监测区域碳排放移动监测终端的数量，根据每个碳排放重点监测子区域中固定监测点的分布比例，按比例将碳排放移动监测终端分配到每个碳排放重点监测子区域；Obtain the number of carbon emission mobile monitoring terminals in the target monitoring area, and distribute the carbon emission mobile monitoring terminals to each carbon emission key monitoring sub-area in proportion to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area;
基于每个所述碳排放重点监测子区域分配的碳排放移动监测终端的数量，生成碳排放移动监测策略；其中，所述碳排放移动监测策略包括碳排放移动监测终端的移动监测路线和移动监测循环频率；Based on the number of carbon emission mobile monitoring terminals allocated to each of the carbon emission key monitoring sub-areas, a carbon emission mobile monitoring strategy is generated; wherein the carbon emission mobile monitoring strategy includes a mobile monitoring route and a mobile monitoring cycle frequency of the carbon emission mobile monitoring terminal;
将所述移动监测路线和移动监测速度发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作。The mobile monitoring route and the mobile monitoring speed are sent to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform an initial carbon emission mobile monitoring action.
可选的，基于每个所述碳排放重点监测子区域分配的碳排放移动监测终端的数量，生成碳排放移动监测策略步骤，具体包括：Optionally, based on the number of carbon emission mobile monitoring terminals allocated to each of the carbon emission key monitoring sub-areas, generating a carbon emission mobile monitoring strategy step specifically includes:
调用目标监测区域的道路交通图，基于每个所述碳排放重点监测子区域中每个碳排放移动监测终端的位置，生成遍历行驶至每个碳排放移动监测终端所在位置的若干条候选监测路线；Calling a road traffic map of the target monitoring area, based on the location of each carbon emission mobile monitoring terminal in each of the carbon emission key monitoring sub-areas, generating a plurality of candidate monitoring routes that traverse and travel to the location of each carbon emission mobile monitoring terminal;
将若干条候选监测路线中距离最短的目标监测路线作为每个所述碳排放重点监测子区域中碳排放移动监测终端的移动监测路线；The target monitoring route with the shortest distance among the plurality of candidate monitoring routes is used as the mobile monitoring route of the carbon emission mobile monitoring terminal in each of the carbon emission key monitoring sub-areas;
基于所述目标监测路线的监测路线长度、所述碳排放移动监测终端执行移动监测动作的标准行驶速度和所述碳排放移动监测终端的数量，生成每个所述碳排放重点监测子区域中碳排放移动监测终端沿所述移动监测路线的移动监测循环频率。Based on the monitoring route length of the target monitoring route, the standard driving speed of the carbon emission mobile monitoring terminal to perform mobile monitoring actions and the number of the carbon emission mobile monitoring terminals, the mobile monitoring cycle frequency of the carbon emission mobile monitoring terminals along the mobile monitoring route in each of the carbon emission key monitoring sub-areas is generated.
可选的，获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数，利用所述监测影响参数，调整所述碳排放移动监测策略，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作步骤，具体包括：Optionally, obtaining monitoring influence parameters when each carbon emission key monitoring sub-area performs a carbon emission mobile monitoring action, and using the monitoring influence parameters to adjust the carbon emission mobile monitoring strategy to drive the carbon emission mobile monitoring terminal to perform the adjusted carbon emission mobile monitoring action steps specifically includes:
获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数；其中，所述监测影响参数包括环境影响参数；Acquire monitoring impact parameters when each carbon emission key monitoring sub-area performs a carbon emission mobile monitoring action; wherein the monitoring impact parameters include environmental impact parameters;
利用所述环境影响参数，对所述碳排放移动监测策略执行第一调整动作和第二调整动作，获得调整后的碳排放移动监测策略；Using the environmental impact parameter, performing a first adjustment action and a second adjustment action on the carbon emission mobile monitoring strategy to obtain an adjusted carbon emission mobile monitoring strategy;
将所述调整后的碳排放移动监测策略发送至每个碳排放重点监测子区域的碳排放移动监测终端，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作。The adjusted carbon emission mobile monitoring strategy is sent to the carbon emission mobile monitoring terminal in each carbon emission key monitoring sub-area to drive the carbon emission mobile monitoring terminal to execute the adjusted carbon emission mobile monitoring action.
可选的，所述环境影响参数包括区域风强和区域温度；利用所述环境影响参数，对所述碳排放移动监测策略执行第一调整动作和第二调整动作，获得调整后的碳排放移动监测策略步骤，具体包括：Optionally, the environmental impact parameters include regional wind intensity and regional temperature; using the environmental impact parameters, performing a first adjustment action and a second adjustment action on the carbon emission mobile monitoring strategy to obtain an adjusted carbon emission mobile monitoring strategy step specifically includes:
根据上一调整周期和当前调整周期每个碳排放重点监测子区域执行碳排放移动监测动作时的区域风强和区域温度，估测每个碳排放重点监测子区域的碳排放扩散速度变化量化值；Estimate the quantitative value of the change in carbon emission diffusion speed in each key carbon emission monitoring sub-region based on the regional wind intensity and regional temperature when each key carbon emission monitoring sub-region performs carbon emission mobile monitoring actions in the previous adjustment period and the current adjustment period;
基于所述碳排放扩散速度变化量化值和每个碳排放重点监测子区域在上一调整周期的碳排放移动监测终端数量，执行调整每个碳排放重点监测子区域中执行碳排放移动监测动作的碳排放移动监测终端数量的第一调整动作；Based on the quantified value of the change in the carbon emission diffusion speed and the number of carbon emission mobile monitoring terminals in each carbon emission key monitoring sub-area in the previous adjustment period, performing a first adjustment action of adjusting the number of carbon emission mobile monitoring terminals performing carbon emission mobile monitoring actions in each carbon emission key monitoring sub-area;
基于所述目标监测路线的监测路线长度、所述碳排放移动监测终端执行移动监测动作的标准行驶速度和所述碳排放移动监测终端调整后的数量，生成每个所述碳排放重点监测子区域中碳排放移动监测终端沿所述移动监测路线的调整后的移动监测循环频率，执行移动监测循环频率的第二调整动作；Based on the monitoring route length of the target monitoring route, the standard driving speed of the carbon emission mobile monitoring terminal for performing the mobile monitoring action, and the adjusted number of the carbon emission mobile monitoring terminals, an adjusted mobile monitoring cycle frequency of the carbon emission mobile monitoring terminals along the mobile monitoring route in each of the carbon emission key monitoring sub-areas is generated, and a second adjustment action of the mobile monitoring cycle frequency is performed;
将调整后的碳排放移动监测终端数量和调整后的移动监测循环频率，作为每个碳排放重点监测子区域的碳排放移动监测策略。The adjusted number of carbon emission mobile monitoring terminals and the adjusted mobile monitoring cycle frequency are used as the carbon emission mobile monitoring strategy for each carbon emission key monitoring sub-area.
可选的，所述第一调整动作被配置为：将所述碳排放扩散速度变化量化值为负值的碳排放重点监测子区域的碳排放移动监测终端按照碳排放扩散速度变化量化值的比例提取出目标数量，并将所述目标数量的碳排放移动监测终端按照所述碳排放扩散速度变化量化值为正值的碳排放重点监测子区域的碳排放扩散速度变化量化值的比例进行分配，以获得调整后的每个碳排放重点监测子区域的碳排放移动监测终端数量；所述第二调整动作被配置为：将调整前的移动监测循环频率调整后的移动监测循环频率。Optionally, the first adjustment action is configured to: extract a target number of carbon emission mobile monitoring terminals in the carbon emission key monitoring sub-area where the quantified value of the carbon emission diffusion rate change is negative according to the ratio of the quantified value of the carbon emission diffusion rate change, and distribute the target number of carbon emission mobile monitoring terminals according to the ratio of the quantified value of the carbon emission diffusion rate change in the carbon emission key monitoring sub-area where the quantified value of the carbon emission diffusion rate change is positive, so as to obtain the adjusted number of carbon emission mobile monitoring terminals in each carbon emission key monitoring sub-area; the second adjustment action is configured to: adjust the mobile monitoring cycle frequency before adjustment to the mobile monitoring cycle frequency after adjustment.
此外，为了实现上述目的，本发明还提供了一种大数据支持的交通碳排放监测系统，包括：In addition, in order to achieve the above purpose, the present invention also provides a traffic carbon emission monitoring system supported by big data, including:
获取模块，用于获取目标监测区域中的碳排放固定监测信息；其中，所述碳排放固定监测信息被配置为设置于所述目标监测区域中若干个固定监测点的碳排放固定监测终端采集的历史碳排放数据；An acquisition module, used to acquire fixed carbon emission monitoring information in a target monitoring area; wherein the fixed carbon emission monitoring information is configured as historical carbon emission data collected by fixed carbon emission monitoring terminals set at a plurality of fixed monitoring points in the target monitoring area;
划分模块，用于基于所述碳排放固定监测信息，将目标监测区域划分为若干个碳排放重点监测子区域和碳排放非重点监测子区域；A division module, used for dividing the target monitoring area into a plurality of carbon emission key monitoring sub-areas and carbon emission non-key monitoring sub-areas based on the fixed carbon emission monitoring information;
第一监测模块，用于根据每个碳排放重点监测子区域中固定监测点的分布比例，生成碳排放移动监测策略，将所述碳排放移动监测策略发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作；The first monitoring module is used to generate a carbon emission mobile monitoring strategy according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area, and send the carbon emission mobile monitoring strategy to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform an initial carbon emission mobile monitoring action;
第二监测模块，用于获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数，利用所述监测影响参数，调整所述碳排放移动监测策略，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作；The second monitoring module is used to obtain the monitoring influence parameters when each carbon emission key monitoring sub-area performs the carbon emission mobile monitoring action, and adjust the carbon emission mobile monitoring strategy by using the monitoring influence parameters to drive the carbon emission mobile monitoring terminal to perform the adjusted carbon emission mobile monitoring action;
生成模块，用于根据所述碳排放固定监测终端在目标监测周期内采集的碳排放非重点监测子区域的第一碳排放数据和碳排放移动监测终端在目标监测周期内执行碳排放移动监测动作采集的碳排放重点监测子区域的第二碳排放数据，生成目标区域的交通碳排放监测信息。A generation module is used to generate traffic carbon emission monitoring information of the target area based on the first carbon emission data of the non-key monitoring sub-area of carbon emission collected by the fixed carbon emission monitoring terminal during the target monitoring period and the second carbon emission data of the key monitoring sub-area of carbon emission collected by the mobile carbon emission monitoring terminal when performing the mobile carbon emission monitoring action during the target monitoring period.
本发明的有益效果在于：提出了一种大数据支持的交通碳排放监测方法及监测系统，通过在目标监测区域布设若干个碳排放固定监测终端，根据采集的碳排放固定监测信息来确定整个目标监测区域中碳排放移动监测终端的布设，实现具有针对性的区域碳排放高精度监测，在一定程度上降低了碳排放固定监测终端采集数据精确度不高的影响，同时，还能够根据目标监测区域实际的监测影响因素，实现对目标监测区域中碳排放移动监测终端的实时位置调整，以此提高交通碳排放监测的环境适应性，解决了目前城市公路运输的碳排放监测方案存在的准确性、资源调度能力和场景适应性不够理想的技术问题。The beneficial effects of the present invention are as follows: a method and system for monitoring carbon emissions from transportation supported by big data are proposed, which deploys a number of fixed carbon emission monitoring terminals in the target monitoring area, determines the deployment of mobile carbon emission monitoring terminals in the entire target monitoring area based on the collected fixed carbon emission monitoring information, and achieves targeted high-precision monitoring of regional carbon emissions. This reduces the impact of the low accuracy of data collected by the fixed carbon emission monitoring terminals to a certain extent, and at the same time, can also realize real-time position adjustment of mobile carbon emission monitoring terminals in the target monitoring area based on the actual monitoring influencing factors in the target monitoring area, thereby improving the environmental adaptability of traffic carbon emission monitoring, and solving the technical problems of the current carbon emission monitoring scheme for urban road transportation, such as the accuracy, resource scheduling capability and scenario adaptability that are not ideal.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明实施例提供的一种大数据支持的交通碳排放监测方法的流程示意图；FIG1 is a schematic diagram of a flow chart of a method for monitoring transportation carbon emissions supported by big data provided by an embodiment of the present invention;
图2为本发明实施例提供的一种大数据支持的交通碳排放监测装置的结构示意图。FIG2 is a schematic diagram of the structure of a traffic carbon emission monitoring device supported by big data provided in an embodiment of the present invention.
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
本发明实施例提供了一种大数据支持的交通碳排放监测方法，参照图1，图1为本发明大数据支持的交通碳排放监测方法实施例的流程示意图。An embodiment of the present invention provides a method for monitoring carbon emissions from transportation supported by big data. Referring to FIG. 1 , FIG. 1 is a flow chart of an embodiment of the method for monitoring carbon emissions from transportation supported by big data of the present invention.
本实施例中，所述大数据支持的交通碳排放监测方法，包括以下步骤：In this embodiment, the traffic carbon emission monitoring method supported by big data includes the following steps:
S1：获取目标监测区域中的碳排放固定监测信息；其中，所述碳排放固定监测信息被配置为设置于所述目标监测区域中若干个固定监测点的碳排放固定监测终端采集的历史碳排放数据；S1: Acquire fixed carbon emission monitoring information in a target monitoring area; wherein the fixed carbon emission monitoring information is configured as historical carbon emission data collected by fixed carbon emission monitoring terminals set at a plurality of fixed monitoring points in the target monitoring area;
S2：基于所述碳排放固定监测信息，将目标监测区域划分为若干个碳排放重点监测子区域和碳排放非重点监测子区域；S2: Based on the fixed carbon emission monitoring information, the target monitoring area is divided into a plurality of carbon emission key monitoring sub-areas and carbon emission non-key monitoring sub-areas;
S3：根据每个碳排放重点监测子区域中固定监测点的分布比例，生成碳排放移动监测策略，将所述碳排放移动监测策略发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作；S3: generating a carbon emission mobile monitoring strategy according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area, and sending the carbon emission mobile monitoring strategy to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform an initial carbon emission mobile monitoring action;
S4：获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数，利用所述监测影响参数，调整所述碳排放移动监测策略，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作；S4: obtaining monitoring influence parameters when each carbon emission key monitoring sub-area performs a carbon emission mobile monitoring action, and adjusting the carbon emission mobile monitoring strategy using the monitoring influence parameters to drive the carbon emission mobile monitoring terminal to perform the adjusted carbon emission mobile monitoring action;
S5：根据所述碳排放固定监测终端在目标监测周期内采集的碳排放非重点监测子区域的第一碳排放数据和碳排放移动监测终端在目标监测周期内执行碳排放移动监测动作采集的碳排放重点监测子区域的第二碳排放数据，生成目标区域的交通碳排放监测信息。S5: Generate traffic carbon emission monitoring information of the target area based on the first carbon emission data of the non-key monitoring sub-area of carbon emission collected by the fixed carbon emission monitoring terminal during the target monitoring period and the second carbon emission data of the key monitoring sub-area of carbon emission collected by the mobile carbon emission monitoring terminal performing the mobile carbon emission monitoring action during the target monitoring period.
需要说明的是，现有针对城市公路运输的碳排放监测方案：一方面，采用车流量间接计算或基于能源消耗总量和各类能源的排放系数进行估算，存在着计算精度不高、无法实现城市碳排放区域布局分析等问题；另一方面，通过在城市内布设大量的碳排放固定监测终端，具有检测范围广、成本低等优势，但由于长期暴露在户外，受环境影响导致损坏率高，使得监测数据可靠性低且因为固定设置存在着便携性和适应性差的问题；虽然，碳排放移动监测终端的投放能够改善监测数据的可靠性、便携性和适应性，但成本高导致的碳排放移动监测终端的投放数量稀少依旧会影响整个监测区域的碳排放资源调度，无法适应复杂多变的实际场景。It should be noted that the existing carbon emission monitoring schemes for urban road transport: on the one hand, they use indirect calculations based on vehicle flow or estimates based on total energy consumption and emission coefficients of various energy sources, which have problems such as low calculation accuracy and inability to realize urban carbon emission regional layout analysis; on the other hand, by deploying a large number of fixed carbon emission monitoring terminals in the city, it has the advantages of wide detection range and low cost, but due to long-term exposure to the outdoors and high damage rate caused by environmental influences, the reliability of monitoring data is low, and the fixed setting has problems with portability and adaptability; although the deployment of mobile carbon emission monitoring terminals can improve the reliability, portability and adaptability of monitoring data, the small number of mobile carbon emission monitoring terminals caused by high costs will still affect the carbon emission resource scheduling in the entire monitoring area and cannot adapt to complex and changeable actual scenarios.
为了解决上述问题，本实施例通过在目标监测区域布设若干个碳排放固定监测终端，根据采集的碳排放固定监测信息来确定整个目标监测区域中碳排放移动监测终端的布设，实现具有针对性的区域碳排放高精度监测，在一定程度上降低了碳排放固定监测终端采集数据精确度不高的影响，同时，还能够根据目标监测区域实际的监测影响因素，实现对目标监测区域中碳排放移动监测终端的实时位置调整，以此提高交通碳排放监测的环境适应性，解决了目前城市公路运输的碳排放监测方案存在的准确性、资源调度能力和场景适应性不够理想的技术问题。In order to solve the above problems, this embodiment deploys a number of fixed carbon emission monitoring terminals in the target monitoring area, and determines the deployment of mobile carbon emission monitoring terminals in the entire target monitoring area based on the collected fixed carbon emission monitoring information, so as to achieve targeted high-precision monitoring of regional carbon emissions, and to a certain extent reduces the impact of the low accuracy of data collected by the fixed carbon emission monitoring terminals. At the same time, it can also realize real-time position adjustment of mobile carbon emission monitoring terminals in the target monitoring area according to the actual monitoring influencing factors of the target monitoring area, thereby improving the environmental adaptability of transportation carbon emission monitoring, and solving the technical problems of the current carbon emission monitoring scheme for urban road transportation, such as the accuracy, resource scheduling capability and scenario adaptability that are not ideal.
在优选的实施例中，获取目标监测区域中的碳排放固定监测信息步骤，具体包括：In a preferred embodiment, the step of obtaining the fixed monitoring information of carbon emissions in the target monitoring area specifically includes:
S11：接收目标监测区域中每个碳排放固定监测终端采集并上传的若干条碳排放采集信息；其中，每条所述碳排放采集信息包括碳排放数据、碳排放采集时间戳和固定监测点位置；S11: receiving a plurality of carbon emission collection information collected and uploaded by each carbon emission fixed monitoring terminal in the target monitoring area; wherein each piece of carbon emission collection information includes carbon emission data, carbon emission collection timestamp and fixed monitoring point location;
S12：根据所述碳排放采集时间戳，提取目标监测周期的历史关联时段内的若干条目标碳排放采集信息，并基于每条目标碳排放采集信息中的碳排放数据和固定监测点位置，构建碳排放固定监测信息。S12: extracting several pieces of target carbon emission collection information within the historical associated time period of the target monitoring cycle according to the carbon emission collection timestamp, and constructing carbon emission fixed monitoring information based on the carbon emission data and fixed monitoring point location in each piece of target carbon emission collection information.
在此基础上，基于所述碳排放固定监测信息，将目标监测区域划分为若干个碳排放重点监测子区域和碳排放非重点监测子区域步骤，具体包括：On this basis, based on the fixed carbon emission monitoring information, the target monitoring area is divided into a number of carbon emission key monitoring sub-areas and carbon emission non-key monitoring sub-areas, specifically including:
S21：提取碳排放固定监测信息中每个固定监测点在历史关联时段内的碳排放数据，输入碳排放预测模型获得目标监测周期内的碳排放预测数据；S21: extracting carbon emission data of each fixed monitoring point in the carbon emission fixed monitoring information within the historical associated period, and inputting the data into the carbon emission prediction model to obtain carbon emission prediction data within the target monitoring period;
S22：判断每个固定监测点在目标监测周期内碳排放预测数据是否超过预设指标，若是，将该固定监测点定义为重点监测点，若否，定义为非重点监测点；S22: Determine whether the carbon emission forecast data of each fixed monitoring point exceeds the preset index during the target monitoring period. If so, define the fixed monitoring point as a key monitoring point; if not, define it as a non-key monitoring point.
S23：将相连的若干个重点监测点对应的区域范围划分为一个碳排放重点监测子区域，将目标监测区域中所有碳排放重点监测子区域之外的其他每个独立区域划分为碳排放非重点监测区域。S23: Divide the area corresponding to several connected key monitoring points into a carbon emission key monitoring sub-area, and divide each independent area outside all carbon emission key monitoring sub-areas in the target monitoring area into a carbon emission non-key monitoring area.
更进一步的，提取碳排放固定监测信息中每个固定监测点在历史关联时段内的碳排放数据，输入碳排放预测模型获得目标监测周期内的碳排放预测数据步骤之前，所述方法，还包括：Furthermore, before the step of extracting the carbon emission data of each fixed monitoring point in the carbon emission fixed monitoring information within the historical associated period and inputting the carbon emission prediction model to obtain the carbon emission prediction data within the target monitoring period, the method further includes:
S24：获取碳排放固定监测数据库中目标预测周期内的碳排放固定监测数据；其中，所目标预测周期包括若干个历史监测周期对应的历史关联时段；S24: Obtaining fixed carbon emission monitoring data within a target prediction period in a fixed carbon emission monitoring database; wherein the target prediction period includes historical associated time periods corresponding to several historical monitoring periods;
S25：将目标预测周期内每个历史关联时段的碳排放固定监测数据构建作为训练样本，将每个历史关联时段对应的目标监测周期的碳排放固定监测数据附加为所述训练样本的标注信息；S25: constructing the fixed monitoring data of carbon emissions in each historical associated period within the target prediction period as training samples, and attaching the fixed monitoring data of carbon emissions in the target monitoring period corresponding to each historical associated period as annotation information of the training samples;
S26：对附加有标注信息的所述训练样本进行特征提取输入每个固定监测点构建的初始卷积神经网络模型进行训练，获得训练完成的碳排放预测模型。S26: Extract features from the training samples with annotated information and input them into the initial convolutional neural network model constructed at each fixed monitoring point for training to obtain a trained carbon emission prediction model.
本实施例中，首先通过布设于目标监测区域中的若干个碳排放固定监测终端采集每个固定监测点在目标监测周期（例如1天）的历史关联时段（例如目标监测周期的前1个月）的碳排放固定监测信息，利用该碳排放固定监测信息与根据目标预测周期（例如1年）采集的历史监测数据构建的碳排放预测模型来预测目标监测周期的碳排放预测数据，以此根据预测的数据将目标监测周期内的目标监测区域划分为重点监测区域和非重点监测区域。本实施例通过获取目标预测周期中每个目标监测周期的碳排放数据与该目标监测周期的历史关联时段的碳排放数据，以此作为训练样本，表征碳排放数据与监测时间之间的映射关系，训练获得碳排放预测模型，能够根据历史碳排放数据，提取出碳排放数据随时间变化的规律。在此以后，对重点监测区域实现具有针对性的区域碳排放高精度监测（即采用监测精度更高的碳排放移动监测终端），对非重点监测区域依旧采用碳排放固定监测终端，降低了碳排放固定监测终端采集数据精确度不高的影响。In this embodiment, first, a number of fixed carbon emission monitoring terminals arranged in the target monitoring area collect fixed carbon emission monitoring information of each fixed monitoring point in the historical associated period (e.g., the first month of the target monitoring period) of the target monitoring period (e.g., 1 day), and use the fixed carbon emission monitoring information and the carbon emission prediction model constructed according to the historical monitoring data collected in the target prediction period (e.g., 1 year) to predict the carbon emission prediction data of the target monitoring period, so as to divide the target monitoring area in the target monitoring period into key monitoring areas and non-key monitoring areas according to the predicted data. In this embodiment, the carbon emission data of each target monitoring period in the target prediction period and the carbon emission data of the historical associated period of the target monitoring period are obtained as training samples to characterize the mapping relationship between carbon emission data and monitoring time, and the carbon emission prediction model is obtained by training, so that the law of carbon emission data changing over time can be extracted according to the historical carbon emission data. After this, targeted high-precision regional carbon emission monitoring (i.e., using a mobile carbon emission monitoring terminal with higher monitoring accuracy) is realized for the key monitoring area, and the fixed carbon emission monitoring terminal is still used for the non-key monitoring area, which reduces the impact of the low accuracy of the data collected by the fixed carbon emission monitoring terminal.
在优选的实施例中，根据每个碳排放重点监测子区域中固定监测点的分布比例，生成碳排放移动监测策略，将所述碳排放移动监测策略发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作步骤，具体包括：In a preferred embodiment, a carbon emission mobile monitoring strategy is generated according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area, and the carbon emission mobile monitoring strategy is sent to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform the initial carbon emission mobile monitoring action steps, specifically including:
S31：获取目标监测区域碳排放移动监测终端的数量，根据每个碳排放重点监测子区域中固定监测点的分布比例，按比例将碳排放移动监测终端分配到每个碳排放重点监测子区域；S31: obtaining the number of carbon emission mobile monitoring terminals in the target monitoring area, and allocating the carbon emission mobile monitoring terminals to each carbon emission key monitoring sub-area in proportion according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area;
S32：基于每个所述碳排放重点监测子区域分配的碳排放移动监测终端的数量，生成碳排放移动监测策略；其中，所述碳排放移动监测策略包括碳排放移动监测终端的移动监测路线和移动监测循环频率；S32: generating a carbon emission mobile monitoring strategy based on the number of carbon emission mobile monitoring terminals allocated to each of the carbon emission key monitoring sub-areas; wherein the carbon emission mobile monitoring strategy includes a mobile monitoring route and a mobile monitoring cycle frequency of the carbon emission mobile monitoring terminal;
S33：将所述移动监测路线和移动监测速度发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作。S33: Sending the mobile monitoring route and the mobile monitoring speed to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform an initial carbon emission mobile monitoring action.
更进一步的，基于每个所述碳排放重点监测子区域分配的碳排放移动监测终端的数量，生成碳排放移动监测策略步骤，具体包括：Furthermore, based on the number of carbon emission mobile monitoring terminals allocated to each of the carbon emission key monitoring sub-areas, a carbon emission mobile monitoring strategy step is generated, specifically including:
S321：调用目标监测区域的道路交通图，基于每个所述碳排放重点监测子区域中每个碳排放移动监测终端的位置，生成遍历行驶至每个碳排放移动监测终端所在位置的若干条候选监测路线；S321: calling a road traffic map of the target monitoring area, and based on the location of each carbon emission mobile monitoring terminal in each of the carbon emission key monitoring sub-areas, generating a plurality of candidate monitoring routes that traverse and travel to the location of each carbon emission mobile monitoring terminal;
S322：将若干条候选监测路线中距离最短的目标监测路线作为每个所述碳排放重点监测子区域中碳排放移动监测终端的移动监测路线；S322: Using the target monitoring route with the shortest distance among the plurality of candidate monitoring routes as the mobile monitoring route of the carbon emission mobile monitoring terminal in each of the carbon emission key monitoring sub-areas;
S323：基于所述目标监测路线的监测路线长度、所述碳排放移动监测终端执行移动监测动作的标准行驶速度和所述碳排放移动监测终端的数量，生成每个所述碳排放重点监测子区域中碳排放移动监测终端沿所述移动监测路线的移动监测循环频率。S323: Based on the monitoring route length of the target monitoring route, the standard driving speed of the carbon emission mobile monitoring terminal to perform mobile monitoring actions and the number of the carbon emission mobile monitoring terminals, generate the mobile monitoring cycle frequency of the carbon emission mobile monitoring terminals along the mobile monitoring route in each of the carbon emission key monitoring sub-areas.
本实施例中，首先根据每个碳排放重点监测子区域中固定监测点的分布比例，分配每个重点监测子区域执行移动监测动作的碳排放移动监测终端数量，然后根据每个碳排放重点监测子区域中需要遍历到达的碳排放监测点，生成距离最短的移动监测路线，最后根据碳排放移动监测终端的标准形式速度计算出碳排放移动监测终端沿所述移动监测路线的移动监测循环频率，基于这样的碳排放移动监测策略执行初始的碳排放移动监测动作。由此，实现对目标监测区域中碳排放移动监测终端合理分配，提高碳排放监测资源利用率，提升整体监测准确性。In this embodiment, firstly, according to the distribution ratio of fixed monitoring points in each key carbon emission monitoring sub-area, the number of carbon emission mobile monitoring terminals for performing mobile monitoring actions in each key carbon emission monitoring sub-area is allocated, then according to the carbon emission monitoring points that need to be traversed and reached in each key carbon emission monitoring sub-area, a mobile monitoring route with the shortest distance is generated, and finally, according to the standard form speed of the carbon emission mobile monitoring terminal, the mobile monitoring cycle frequency of the carbon emission mobile monitoring terminal along the mobile monitoring route is calculated, and the initial carbon emission mobile monitoring action is performed based on such a carbon emission mobile monitoring strategy. In this way, the carbon emission mobile monitoring terminals in the target monitoring area are reasonably allocated, the utilization rate of carbon emission monitoring resources is improved, and the overall monitoring accuracy is improved.
在优选的实施例中，获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数，利用所述监测影响参数，调整所述碳排放移动监测策略，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作步骤，具体包括：In a preferred embodiment, the monitoring influence parameters when each carbon emission key monitoring sub-area performs the carbon emission mobile monitoring action are obtained, and the carbon emission mobile monitoring strategy is adjusted using the monitoring influence parameters to drive the carbon emission mobile monitoring terminal to perform the adjusted carbon emission mobile monitoring action steps, specifically including:
S41：获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数；其中，所述监测影响参数包括环境影响参数；S41: Acquire monitoring impact parameters when each carbon emission key monitoring sub-area performs a carbon emission mobile monitoring action; wherein the monitoring impact parameters include environmental impact parameters;
S42：利用所述环境影响参数，对所述碳排放移动监测策略执行第一调整动作和第二调整动作，获得调整后的碳排放移动监测策略；S42: using the environmental impact parameter, performing a first adjustment action and a second adjustment action on the carbon emission mobile monitoring strategy to obtain an adjusted carbon emission mobile monitoring strategy;
S43：将所述调整后的碳排放移动监测策略发送至每个碳排放重点监测子区域的碳排放移动监测终端，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作。S43: Sending the adjusted carbon emission mobile monitoring strategy to the carbon emission mobile monitoring terminal in each carbon emission key monitoring sub-area to drive the carbon emission mobile monitoring terminal to execute the adjusted carbon emission mobile monitoring action.
更进一步的，所述环境影响参数包括区域风强和区域温度；利用所述环境影响参数，对所述碳排放移动监测策略执行第一调整动作和第二调整动作，获得调整后的碳排放移动监测策略步骤，具体包括：Furthermore, the environmental impact parameters include regional wind intensity and regional temperature; using the environmental impact parameters, performing a first adjustment action and a second adjustment action on the carbon emission mobile monitoring strategy to obtain an adjusted carbon emission mobile monitoring strategy step specifically includes:
S421：根据上一调整周期和当前调整周期每个碳排放重点监测子区域执行碳排放移动监测动作时的区域风强和区域温度，估测每个碳排放重点监测子区域的碳排放扩散速度变化量化值；S421: estimating a quantitative value of a change in carbon emission diffusion speed in each key carbon emission monitoring sub-region according to the regional wind intensity and regional temperature when each key carbon emission monitoring sub-region performs a carbon emission mobile monitoring action in the previous adjustment period and the current adjustment period;
S422：基于所述碳排放扩散速度变化量化值和每个碳排放重点监测子区域在上一调整周期的碳排放移动监测终端数量，执行调整每个碳排放重点监测子区域中执行碳排放移动监测动作的碳排放移动监测终端数量的第一调整动作；S422: Based on the quantified value of the change in the carbon emission diffusion speed and the number of carbon emission mobile monitoring terminals in each carbon emission key monitoring sub-area in the previous adjustment period, performing a first adjustment action of adjusting the number of carbon emission mobile monitoring terminals performing carbon emission mobile monitoring actions in each carbon emission key monitoring sub-area;
S423：基于所述目标监测路线的监测路线长度、所述碳排放移动监测终端执行移动监测动作的标准行驶速度和所述碳排放移动监测终端调整后的数量，生成每个所述碳排放重点监测子区域中碳排放移动监测终端沿所述移动监测路线的调整后的移动监测循环频率，执行移动监测循环频率的第二调整动作；S423: Based on the monitoring route length of the target monitoring route, the standard driving speed of the carbon emission mobile monitoring terminal for performing the mobile monitoring action, and the adjusted number of the carbon emission mobile monitoring terminals, generate the adjusted mobile monitoring cycle frequency of the carbon emission mobile monitoring terminals along the mobile monitoring route in each of the carbon emission key monitoring sub-areas, and perform a second adjustment action of the mobile monitoring cycle frequency;
S424：将调整后的碳排放移动监测终端数量和调整后的移动监测循环频率，作为每个碳排放重点监测子区域的碳排放移动监测策略。S424: The adjusted number of carbon emission mobile monitoring terminals and the adjusted mobile monitoring cycle frequency are used as the carbon emission mobile monitoring strategy for each carbon emission key monitoring sub-area.
具体而言，所述第一调整动作被配置为：将所述碳排放扩散速度变化量化值为负值的碳排放重点监测子区域的碳排放移动监测终端按照碳排放扩散速度变化量化值的比例提取出目标数量，并将所述目标数量的碳排放移动监测终端按照所述碳排放扩散速度变化量化值为正值的碳排放重点监测子区域的碳排放扩散速度变化量化值的比例进行分配，以获得调整后的每个碳排放重点监测子区域的碳排放移动监测终端数量；所述第二调整动作被配置为：将调整前的移动监测循环频率调整后的移动监测循环频率。Specifically, the first adjustment action is configured to: extract the target number of carbon emission mobile monitoring terminals in the carbon emission key monitoring sub-area where the quantified value of the carbon emission diffusion speed change is negative according to the ratio of the quantified value of the carbon emission diffusion speed change, and distribute the target number of carbon emission mobile monitoring terminals according to the ratio of the quantified value of the carbon emission diffusion speed change in the carbon emission key monitoring sub-area where the quantified value of the carbon emission diffusion speed change is positive, so as to obtain the adjusted number of carbon emission mobile monitoring terminals in each carbon emission key monitoring sub-area; the second adjustment action is configured to: adjust the mobile monitoring cycle frequency before adjustment to the mobile monitoring cycle frequency after adjustment.
本实施例中，在驱使碳排放移动监测终端执行初始的碳排放移动监测动作时，还通过获取每个重点监测子区域的环境影响参数，考虑环境影响参数对每个重点监测子区域的碳排放扩散速度的影响（区域风强大和区域温度高会提高碳排放扩散速度，若采用原本的移动监测循环频率，会使得采集获得的碳排放数据不够准确），通过实时调整重点监测子区域的碳排放移动监测终端数量和移动监测循环频率，使更多、更精确的碳排放监测资源在每个调整周期（例如1个小时）分配到最需要（即扩散速度更大）的重点监测子区域，对目标监测区域中碳排放移动监测终端的实时位置调整，以此提高交通碳排放监测的环境适应性，解决了目前城市公路运输的碳排放监测方案存在的准确性、资源调度能力和场景适应性不够理想的技术问题。In this embodiment, when the carbon emission mobile monitoring terminal is driven to perform the initial carbon emission mobile monitoring action, the environmental impact parameters of each key monitoring sub-area are obtained, and the influence of the environmental impact parameters on the carbon emission diffusion speed of each key monitoring sub-area is considered (strong regional wind and high regional temperature will increase the carbon emission diffusion speed. If the original mobile monitoring cycle frequency is used, the collected carbon emission data will be inaccurate). By adjusting the number of carbon emission mobile monitoring terminals and the mobile monitoring cycle frequency in the key monitoring sub-area in real time, more and more accurate carbon emission monitoring resources are allocated to the key monitoring sub-areas that are most in need (i.e., have a faster diffusion speed) in each adjustment period (for example, 1 hour), and the real-time position of the carbon emission mobile monitoring terminal in the target monitoring area is adjusted, thereby improving the environmental adaptability of transportation carbon emission monitoring, and solving the technical problems of the current carbon emission monitoring scheme for urban road transportation, such as the accuracy, resource scheduling capability and scenario adaptability that are not ideal.
本实施例提供了一种大数据支持的交通碳排放监测方法，通过在目标监测区域布设若干个碳排放固定监测终端，根据采集的碳排放固定监测信息来确定整个目标监测区域中碳排放移动监测终端的布设，实现具有针对性的区域碳排放高精度监测，在一定程度上降低了碳排放固定监测终端采集数据精确度不高的影响，同时，还能够根据目标监测区域实际的监测影响因素，实现对目标监测区域中碳排放移动监测终端的实时位置调整，以此提高交通碳排放监测的环境适应性，解决了目前城市公路运输的碳排放监测方案存在的准确性、资源调度能力和场景适应性不够理想的技术问题。This embodiment provides a method for monitoring carbon emissions from transportation supported by big data. By deploying a number of fixed carbon emission monitoring terminals in the target monitoring area, the deployment of mobile carbon emission monitoring terminals in the entire target monitoring area is determined based on the collected fixed carbon emission monitoring information, thereby achieving targeted high-precision monitoring of regional carbon emissions. This reduces the impact of the low accuracy of data collected by the fixed carbon emission monitoring terminals to a certain extent. At the same time, it can also achieve real-time position adjustment of mobile carbon emission monitoring terminals in the target monitoring area based on the actual monitoring influencing factors in the target monitoring area, thereby improving the environmental adaptability of traffic carbon emission monitoring, and solving the technical problems of the current carbon emission monitoring scheme for urban road transportation, such as unsatisfactory accuracy, resource scheduling capabilities and scenario adaptability.
参照图2，图2为本发明实施例提供的大数据支持的交通碳排放监测装置实施例的结构示意图。Refer to FIG. 2 , which is a schematic diagram of the structure of an embodiment of a traffic carbon emission monitoring device supported by big data provided in an embodiment of the present invention.
如图2所示，本实施例提出的大数据支持的交通碳排放监测装置包括：As shown in FIG2 , the traffic carbon emission monitoring device supported by big data proposed in this embodiment includes:
获取模块10，用于获取目标监测区域中的碳排放固定监测信息；其中，所述碳排放固定监测信息被配置为设置于所述目标监测区域中若干个固定监测点的碳排放固定监测终端采集的历史碳排放数据；The acquisition module 10 is used to acquire the fixed monitoring information of carbon emissions in the target monitoring area; wherein the fixed monitoring information of carbon emissions is configured as historical carbon emission data collected by the fixed monitoring terminals of carbon emissions set at several fixed monitoring points in the target monitoring area;
划分模块20，用于基于所述碳排放固定监测信息，将目标监测区域划分为若干个碳排放重点监测子区域和碳排放非重点监测子区域；A division module 20, for dividing the target monitoring area into a plurality of carbon emission key monitoring sub-areas and carbon emission non-key monitoring sub-areas based on the carbon emission fixed monitoring information;
第一监测模块30，用于根据每个碳排放重点监测子区域中固定监测点的分布比例，生成碳排放移动监测策略，将所述碳排放移动监测策略发送至对应的碳排放移动监测终端，以驱使碳排放移动监测终端执行初始的碳排放移动监测动作；The first monitoring module 30 is used to generate a carbon emission mobile monitoring strategy according to the distribution ratio of fixed monitoring points in each carbon emission key monitoring sub-area, and send the carbon emission mobile monitoring strategy to the corresponding carbon emission mobile monitoring terminal to drive the carbon emission mobile monitoring terminal to perform an initial carbon emission mobile monitoring action;
第二监测模块40，用于获取每个碳排放重点监测子区域执行碳排放移动监测动作时的监测影响参数，利用所述监测影响参数，调整所述碳排放移动监测策略，以驱使碳排放移动监测终端执行调整后的碳排放移动监测动作；The second monitoring module 40 is used to obtain the monitoring influence parameters when each carbon emission key monitoring sub-area performs the carbon emission mobile monitoring action, and adjust the carbon emission mobile monitoring strategy by using the monitoring influence parameters to drive the carbon emission mobile monitoring terminal to perform the adjusted carbon emission mobile monitoring action;
生成模块50，用于根据所述碳排放固定监测终端在目标监测周期内采集的碳排放非重点监测子区域的第一碳排放数据和碳排放移动监测终端在目标监测周期内执行碳排放移动监测动作采集的碳排放重点监测子区域的第二碳排放数据，生成目标区域的交通碳排放监测信息。The generation module 50 is used to generate traffic carbon emission monitoring information of the target area based on the first carbon emission data of the non-key monitoring sub-area of carbon emission collected by the fixed carbon emission monitoring terminal during the target monitoring period and the second carbon emission data of the key monitoring sub-area of carbon emission collected by the mobile carbon emission monitoring terminal when performing the mobile carbon emission monitoring action during the target monitoring period.
本发明大数据支持的交通碳排放监测装置的其他实施例或具体实现方式可参照上述各方法实施例，此处不再赘述。Other embodiments or specific implementations of the traffic carbon emission monitoring device supported by big data of the present invention can refer to the above-mentioned method embodiments, which will not be repeated here.
可以理解的是，在本说明书的描述中，参考术语“一实施例”、“另一实施例”、“其他实施例”、或“第一实施例～第N实施例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。It is understood that, in the description of this specification, the description with reference to the terms "one embodiment", "another embodiment", "other embodiments", or "first to Nth embodiments" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410808017.3ACN118376738B (en) | 2024-06-21 | 2024-06-21 | Big data supported traffic carbon emission monitoring method and monitoring system |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410808017.3ACN118376738B (en) | 2024-06-21 | 2024-06-21 | Big data supported traffic carbon emission monitoring method and monitoring system |
| Publication Number | Publication Date |
|---|---|
| CN118376738Atrue CN118376738A (en) | 2024-07-23 |
| CN118376738B CN118376738B (en) | 2024-09-10 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410808017.3AActiveCN118376738B (en) | 2024-06-21 | 2024-06-21 | Big data supported traffic carbon emission monitoring method and monitoring system |
| Country | Link |
|---|---|
| CN (1) | CN118376738B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793773A (en)* | 2012-11-02 | 2014-05-14 | 王亚利 | Account system and method for realizing management of carbon emission or carbon emission reduction behavior |
| CN106611290A (en)* | 2016-12-08 | 2017-05-03 | 华北水利水电大学 | Carbon emission management system based on urban planning |
| CN108885764A (en)* | 2016-03-17 | 2018-11-23 | 瑞士再保险有限公司 | Telematics system and corresponding method |
| CN110346518A (en)* | 2019-07-25 | 2019-10-18 | 中南大学 | A kind of traffic emission pollution visualization method for early warning and its system |
| CN115035713A (en)* | 2022-04-29 | 2022-09-09 | 银江技术股份有限公司 | Intelligent traffic carbon emission monitoring system and method |
| CN115856206A (en)* | 2022-11-15 | 2023-03-28 | 深圳供电局有限公司 | Carbon emission detection method and device, computer equipment and storage medium |
| CN116187613A (en)* | 2023-04-26 | 2023-05-30 | 广东埃文低碳科技股份有限公司 | Big data-based carbon emission flow real-time monitoring system and method thereof |
| CN116363538A (en)* | 2023-06-01 | 2023-06-30 | 贵州交投高新科技有限公司 | Bridge detection method and system based on unmanned aerial vehicle |
| CN116628395A (en)* | 2023-05-31 | 2023-08-22 | 重庆交通大学 | Urban terrain feature-based road vehicle carbon emission measuring and calculating method and system |
| EP4307189A1 (en)* | 2022-07-15 | 2024-01-17 | Mother Nature Resources Limited | System and method for simulating and predicting forecasts for carbon emissions |
| CN117723706A (en)* | 2023-11-14 | 2024-03-19 | 国网江苏省电力有限公司营销服务中心 | Mobile carbon monitoring device and monitoring method |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103793773A (en)* | 2012-11-02 | 2014-05-14 | 王亚利 | Account system and method for realizing management of carbon emission or carbon emission reduction behavior |
| CN108885764A (en)* | 2016-03-17 | 2018-11-23 | 瑞士再保险有限公司 | Telematics system and corresponding method |
| CN106611290A (en)* | 2016-12-08 | 2017-05-03 | 华北水利水电大学 | Carbon emission management system based on urban planning |
| CN110346518A (en)* | 2019-07-25 | 2019-10-18 | 中南大学 | A kind of traffic emission pollution visualization method for early warning and its system |
| CN115035713A (en)* | 2022-04-29 | 2022-09-09 | 银江技术股份有限公司 | Intelligent traffic carbon emission monitoring system and method |
| EP4307189A1 (en)* | 2022-07-15 | 2024-01-17 | Mother Nature Resources Limited | System and method for simulating and predicting forecasts for carbon emissions |
| CN115856206A (en)* | 2022-11-15 | 2023-03-28 | 深圳供电局有限公司 | Carbon emission detection method and device, computer equipment and storage medium |
| CN116187613A (en)* | 2023-04-26 | 2023-05-30 | 广东埃文低碳科技股份有限公司 | Big data-based carbon emission flow real-time monitoring system and method thereof |
| CN116628395A (en)* | 2023-05-31 | 2023-08-22 | 重庆交通大学 | Urban terrain feature-based road vehicle carbon emission measuring and calculating method and system |
| CN116363538A (en)* | 2023-06-01 | 2023-06-30 | 贵州交投高新科技有限公司 | Bridge detection method and system based on unmanned aerial vehicle |
| CN117723706A (en)* | 2023-11-14 | 2024-03-19 | 国网江苏省电力有限公司营销服务中心 | Mobile carbon monitoring device and monitoring method |
| Title |
|---|
| HANDAYANI, F等: "Exploring the Carbon Pricing Strategy to Decarbonizing Coal-Fired Power Plants: A System Thinking Approach", PROCEDIA COMPUTER SCIENCE, vol. 234, 30 May 2024 (2024-05-30), pages 1650 - 7, XP087518004, DOI: 10.1016/j.procs.2024.03.169* |
| 张君翼: ""双碳"约束下组团式山地城市低碳交通发展路径研究", 交通科技与管理, vol. 4, no. 6, 20 March 2023 (2023-03-20), pages 17 - 19* |
| 张梦成;谭洪卫;胡婷莛;段玉森;: "基于移动监测的隧道内部PM_(2.5)与PM_(10)质量浓度分布研究", 环境工程, no. 06, 22 June 2017 (2017-06-22), pages 127 - 134* |
| Publication number | Publication date |
|---|---|
| CN118376738B (en) | 2024-09-10 |
| Publication | Publication Date | Title |
|---|---|---|
| CN111653088A (en) | A method for constructing a vehicle trip volume prediction model and a prediction method and system | |
| CN108648457B (en) | A method, apparatus and computer-readable storage medium for speed prediction | |
| CN101261768B (en) | Highway Network Traffic Survey Data Acquisition and Analysis Application System and Its Working Method | |
| RU2012109387A (en) | METHOD AND DEVICE FOR QUEUE ROUTING | |
| WO2019237522A1 (en) | Vehicle leasing method and apparatus, and computer device and storage medium | |
| CN109916413A (en) | Mesh-based road matching method, system, device and storage medium | |
| CN110849384A (en) | Navigation route generation method and device, readable storage medium and computer equipment | |
| CN106935034B (en) | Regional traffic flow prediction system and method for Internet of Vehicles | |
| JP6543180B2 (en) | Destination prediction apparatus, method, and program | |
| Ghandeharioun et al. | Link travel time estimation for arterial networks based on sparse GPS data and considering progressive correlations | |
| CN115219810A (en) | Line trip prediction method based on lightning positioning system | |
| CN119135657A (en) | A method and system for allocating IP address resources based on machine learning | |
| CN115412857B (en) | Resident trip information prediction method | |
| CN118376738B (en) | Big data supported traffic carbon emission monitoring method and monitoring system | |
| CN108154296A (en) | Net about vehicle dispatching method and system, operator's platform | |
| CN118798580A (en) | Integrated smart management platform for urban public services | |
| CN113743680B (en) | Hydrogen energy demand prediction method, device and storage medium | |
| CN119647762A (en) | Floor trash can overflow monitoring method, system, monitoring equipment and storage medium | |
| CN109743723A (en) | A method for allocating mobile phone base station data to surrounding space units | |
| CN104700616A (en) | Urban traffic pressure prediction method and system | |
| JP6668227B2 (en) | Spatio-temporal prediction model learning device, spatio-temporal prediction device, method, and program | |
| CN112600728B (en) | 5G mobile base station flow prediction analysis system based on big data | |
| CN113706000A (en) | Environment monitoring resource allocation method and system based on Internet of things | |
| CN116150298B (en) | Data acquisition method, system and readable storage medium based on internet of things | |
| Antoniou et al. | An event-based bus monitoring system |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |