Carbon emission monitoring method, electronic equipment, storage medium and program product

Abstract

The invention provides a carbon emission monitoring method, electronic equipment, a storage medium and a program product, and relates to the technical field of energy management. The method comprises the following steps: based on a digital intelligent energy management platform, marking hotspot energy charging equipment in a to-be-tested traffic road network, obtaining operation data of the hotspot energy charging equipment, and establishing an equipment carbon emission prediction model according to historical traffic data of the to-be-tested traffic road network and the operation data of the hotspot energy charging equipment; inputting the real-time traffic data of the to-be-tested traffic road network into the equipment carbon emission prediction model, and predicting the target carbon emission of the hotspot energy charging equipment; selecting a management strategy for the hot-spot energy charging equipment according to the target carbon emission of the hot-spot energy charging equipment; based on the digital intelligent energy management platform, an existing energy charging platform is subjected to refined management, hot-spot energy charging equipment with higher carbon emission can be found, emphatically monitored and managed, congestion caused by the hot-spot energy charging equipment is reduced, and the purpose of reducing the carbon emission is achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a carbon emission monitoring method, electronic equipment, storage medium and program product. Background Art

[0002] In recent years, with the development of artificial intelligence (AI) technology, the "energy + AI" management method has evolved in energy management. The industry hopes to solve the defects of low power and energy utilization rates caused by low efficiency of operation and maintenance collaboration between nodes in traditional energy management through such technological innovation. This digital intelligent energy management technology has also been applied to the energy management of traditional transportation networks due to the rapid development of new energy.

[0003] However, in the road traffic network, the charging platforms for recharging new energy vehicles are generally improved on the basis of the original gas stations due to the limited space. As a result, the management methods of the charging platforms are limited, and the data resource management of a single charging platform is also relatively rough. Multiple charging platforms are independent of each other, and the lack of collaboration can easily cause traffic jams, which may in turn cause the pressure and burden of excessive carbon emissions. Therefore, it is necessary to develop a carbon emission detection method. Summary of the invention

[0004] In view of this, the purpose of the embodiments of the present application is to provide a carbon emission monitoring method, electronic device, storage medium and program product, which can improve the problem that traditional energy management ignores the carbon emission monitoring method when applied to new charging platforms, causing unnecessary burden on the environment.

[0005] In order to achieve the above technical objectives, the technical solutions adopted in this application are as follows:

[0006] In a first aspect, an embodiment of the present application provides a carbon emission monitoring method based on digital intelligent source management, the method comprising:

[0007] Based on the digital intelligent source management platform, mark the hotspot charging equipment in the traffic network to be tested and obtain the operating data of the hotspot charging equipment, which at least includes the device IP address, chargeable capacity and energy efficiency information;

[0008] Establishing a device carbon emission prediction model based on the historical traffic data of the traffic network to be tested and the operation data of the hotspot charging device;

[0009] Inputting the real-time traffic data of the traffic network to be tested into the equipment carbon emission prediction model to predict the target carbon emission of the hotspot charging equipment;

[0010] Selecting a management strategy for the hotspot charging device according to the target carbon emissions of the hotspot charging device;

[0011] Carbon emissions are monitored for the hotspot charging device that executes the management strategy.

[0012] In conjunction with the first aspect, in some optional implementations, based on the digital intelligent source management platform, the steps of marking the hotspot charging device in the traffic network to be tested and obtaining the operation data of the hotspot charging device include:

[0013] Obtain management authority for the digital intelligent energy management platform and query the distribution data of all charging devices in the area where the traffic network to be tested is located;

[0014] Analyze the distribution data through big data technology to find out the charging equipment that needs to be monitored for carbon emissions and mark them as hot spot charging equipment;

[0015] Obtaining operation data of the hotspot charging device;

[0016] Furthermore, the steps of analyzing the distribution data by big data technology, mining charging devices that need to be monitored for carbon emissions, and marking them as hot spot charging devices include:

[0017] Based on big data technology, determine the emission index V and total carbon emissions of the traffic network to be tested;

[0018] Analyze the emission index V according to the Divisia index decomposition method, calculate the change result of the emission index V from period 0 to period T, and screen the roads in the traffic network to be tested based on the change result;

[0019] The selected roads are excavated according to the total carbon emissions to obtain the hot spot charging equipment.

[0020] Among them, the emission index V is analyzed according to the Divisia index decomposition method, and the change results of the emission index V from period 0 to period T are calculated, including:

[0021] Assume that an emission index V is affected by n influencing factors, satisfying:

[0022] V=∑ i x 1i x 2i …x ni , and V i =x 1i x 2i …x ni ;

[0023] Where i represents the category that constitutes the emission index V;

[0024] Index V from 0 period V 0 Change to V in T phase T, where the carbon emission change in period T is multiplied and decomposed to obtain:

[0025]

[0026] In multiplication decomposition mode,

[0027] Among them, W i * =(V i T / V T +V i 0 / V 0 ) / 2, which represents the weight term of the multiplicative decomposition;

[0028] D ot Represents the change of emission index V during period T.

[0029] In combination with the first aspect, in some optional implementations, the step of mining the selected roads according to the total carbon emissions to obtain the hotspot charging device includes:

[0030] Establish twin charging links for the selected roads;

[0031] Under the constraint of satisfying the total carbon emission, the electricity cost of the charging equipment in each charging link is calculated, and an average electricity cost is determined;

[0032] Charging devices whose cost per kilowatt-hour is greater than the average cost per kilowatt-hour are defined as hot spot charging devices.

[0033] In combination with the first aspect, in some optional implementations, the method further includes:

[0034] Marking a charging device whose kilowatt-hour cost is less than a second cost threshold as a secondary charging device; wherein the second cost threshold is less than the average kilowatt-hour cost;

[0035] Taking the hotspot charging device as the center, the secondary charging devices are clustered, and the secondary charging devices in the same cluster are connected to the hotspot charging devices to share the energy consumption pressure of the hotspot charging devices.

[0036] In combination with the first aspect, in some optional embodiments, after the step of monitoring carbon emissions of the hotspot charging device that executes the management strategy, the method further includes: judging whether to divert vehicles that are about to reach the hotspot charging device based on the monitoring results.

[0037] In a second aspect, an embodiment of the present application further provides an electronic device, comprising a processor and a memory coupled to each other, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the electronic device executes the above method.

[0038] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above method when executed by a computer.

[0039] In a fourth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0040] The invention adopting the above technical solution has the following advantages:

[0041] In the technical solution provided in the present application, the layout of the established charging equipment or charging platform can be obtained from the existing digital intelligent source management platform, and then superimposed on the traffic network and associated with the traffic network to establish a carbon emission prediction model for the equipment; and in the traffic network, some charging equipment is busy and some are relatively idle; after obtaining the historical traffic data of the traffic network to be tested, the hot charging equipment can be analyzed; in this way, when conducting carbon emission monitoring, targeted monitoring can be carried out, and the target carbon emissions of the hot charging equipment in the future period can be predicted. After the prediction results come out, the hot charging equipment can be controlled in advance to avoid actual carbon emissions exceeding the limit, and the emission reduction pressure of the hot charging equipment can be shared by other charging equipment, so as to avoid its carbon emissions exceeding the limit and avoid the total carbon emissions exceeding the limit; at the same time, the risk of vehicle congestion caused by the hot charging equipment is reduced, and unnecessary burdens are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present application may be further described by the non-limiting embodiments given in the accompanying drawings. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings may be obtained based on these drawings without creative effort.

[0043] Figure 1 A structural block diagram of an electronic device provided in an embodiment of the present application.

[0044] Figure 2 A schematic diagram of the process of the carbon emission monitoring method provided in an embodiment of the present application.

[0045] Reference numerals in the figure: 100 - electronic device; 101 - processor; 102 - memory. DETAILED DESCRIPTION

[0046] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that in the drawings or descriptions, similar or identical parts use the same figure numbers, and the implementation methods not shown or described in the drawings are forms known to ordinary technicians in the relevant technical field. In the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0047] Please refer to Figure 1 , an embodiment of the present application provides an electronic device 100, which may include a processor 101 and a memory 102. The memory 102 stores a computer program, and when the computer program is executed by the processor 101, the electronic device 100 can perform the corresponding steps in the following carbon emission monitoring method.

[0048] In this embodiment, the processor 101 may be an integrated circuit chip having signal processing capabilities. The processor 101 may be a general-purpose processor. For example, the processor 101 may be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and may implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0049] The memory 102 may be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the memory 102 may be used to store data sampling points, sampled data, target sub-regions, preset interpolation strategies, data change curves, preset conditions, prompt information, etc. Of course, the memory 102 may also be used to store programs, and the processor 101 executes the program after receiving the execution instruction.

[0050] Understandably, Figure 1 The structure of the electronic device 100 shown in FIG. 1 is only a schematic diagram of a structure. The electronic device 100 may also include Figure 1 More components are shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0051] In this embodiment, the electronic device 100 can be a personal computer, a laptop computer, a cloud server, etc. This embodiment takes a personal computer as an example. The electronic device 100 is used to obtain sampling data, and then cluster the sampling data to obtain multiple target sub-areas. For each sub-area, the sampling data of the area is fitted to obtain a data change curve. Finally, the data change curve that meets the preset conditions is used as the target curve, and a prompt message indicating that the target sub-area corresponding to the target curve has a pollution risk is issued.

[0052] Please refer to Figure 2 The present application also provides a carbon emission monitoring method based on digital intelligent source management, which can be applied to the above electronic device 100, and each step of the method is executed or implemented by the electronic device 100. The carbon emission monitoring method based on digital intelligent source management can include the following steps:

[0053] Step S210, based on the digital intelligent source management platform, marking the hotspot charging device in the traffic network to be tested and obtaining the operation data of the hotspot charging device, the operation data at least including the device IP address, chargeable capacity and energy efficiency information;

[0054] Step S220, establishing a device carbon emission prediction model based on the historical traffic data of the traffic network to be tested and the operation data of the hotspot charging device;

[0055] Step S230, inputting the real-time traffic data of the traffic network to be tested into the equipment carbon emission prediction model to predict the target carbon emission of the hotspot charging equipment;

[0056] Step S240, selecting a management strategy for the hotspot charging device according to the target carbon emission of the hotspot charging device;

[0057] Step S250, monitoring carbon emissions of the hotspot charging device that executes the management strategy.

[0058] The following is a detailed description of the steps of a carbon emission monitoring method based on digital intelligent source management, as follows:

[0059] In step S210, generally, the digital intelligent energy management platform is built based on a region, industry or organization; it manages a new energy consumption network consisting of power supply, green power generation, energy storage, and load in a region, where the power supply is the alternating current provided by the power grid, green power generation includes wind power generation, solar power generation, etc., and energy storage can use batteries, hydrogen fuel cells, etc.; batteries can be used in charging stations or charging platforms on more roads; in this way, carbon emission reduction and sustainable development can be achieved.

[0060] In this embodiment, the area where the traffic network to be tested is located is a larger administrative region, such as City A, City B, City C and other administrative regions, so the traffic network may be more complex, but it is also easier to design new energy consumption. The traffic network includes: main roads, secondary roads and branch roads, as well as viaducts, tunnels and other roads; charging stations or charging platforms with charging equipment are mainly set on or around main roads, secondary roads and branch roads, and are managed through the digital intelligent resource management platform.

[0061] In this embodiment, the charging equipment can charge the new energy vehicle by battery replacement, high-voltage charging or high-current charging; the hotspot charging equipment is a charging equipment whose service frequency exceeds a certain frequency, and this frequency can be designed according to the actual situation. The acquisition of the hotspot charging equipment in the traffic network to be tested can be obtained from the public data of the relevant departments, from the public data of the construction company and the operating company of the charging station, or from the open interface of the map software, such as Amap, Baidu Map, etc.; or, it can be obtained from the Internet by means of a web crawler, or multi-source acquisition can be performed from these sources. The obtained data is integrated by Python software, and a charging equipment layout database can be established. In this way, the hotspot charging equipment can be found and marked by analyzing the charging equipment layout database; and the data of the discovered hotspot charging equipment can be monitored and collected to reduce the monitoring scope, carry out targeted and accurate carbon emission monitoring, optimize the equipment layout space, and reduce the burden of carbon emissions.

[0062] In this embodiment, the operation data of the hotspot charging device is obtained, and address management and communication management can be performed according to the device IP address. In this way, the charging devices can be coordinated, and the available power of the idle charging devices can be called to the hotspot charging devices according to the chargeable and discharge capacity of each device, or the service capacity of the idle charging devices can be improved, so that the energy efficiency utilization of each device is more balanced, and the energy efficiency information is collected to optimize the power calling logic and improve energy efficiency.

[0063] In this embodiment, based on the digital intelligent source management platform, the steps of marking the hotspot charging equipment in the traffic network to be tested and obtaining the operation data of the hotspot charging equipment include:

[0064] Obtain management authority for the digital intelligent energy management platform and query the distribution data of all charging devices in the area where the traffic network to be tested is located;

[0065] Analyze the distribution data through big data technology to find out the charging equipment that needs to be monitored for carbon emissions and mark them as hot spot charging equipment;

[0066] Obtaining operation data of the hotspot charging device;

[0067] For example, a carbon emission monitoring software for the traffic network to be tested can be installed on the digital intelligent energy management platform. The software is designed based on the CALPUFF model and can monitor, analyze and predict gas pollution in the traffic network.

[0068] In this embodiment, the steps of analyzing the distribution data by big data technology, mining charging devices that need to be monitored for carbon emissions, and marking them as hot spot charging devices include:

[0069] Based on big data technology, determine the emission index V and total carbon emissions of the traffic network to be tested;

[0070] Analyze the emission index V according to the Divisia index decomposition method, calculate the change result of the emission index V from period 0 to period T, and screen the roads in the traffic network to be tested based on the change result;

[0071] The selected roads are excavated according to the total carbon emissions to obtain the hot spot charging equipment.

[0072] In this embodiment, for the traffic network to be tested: conventional intelligent transportation systems can combine multiple monitoring methods (such as cameras, sensors, GPS, etc.) to achieve comprehensive monitoring and management of the traffic network through big data analysis and artificial intelligence technology. In this way, it is possible to monitor traffic flow, vehicle speed, congestion, etc. in real time, provide decision support for emission reduction, optimize traffic organization plans, and improve road traffic efficiency.

[0073] The greater the traffic volume on the roads in the traffic network, the more carbon emissions are generated; therefore, the emission index V includes the energy consumed and the regional GDP; for the traffic network, the influencing factors include lane type, traffic volume, travel time period, vehicle type and vehicle power; the total carbon emissions can be determined by querying the relevant national and regional standards; for example: the carbon concentration range is in the following intervals, (0, 0.05%], (0.05%, 0.07%], (0.07%, 0.1%], (0.1%, 0.3%), when the carbon concentration exceeds 0.1%, it is considered to cause pollution and carbon reduction measures need to be taken; the total carbon emissions of the traffic network to be tested can be set according to this threshold.

[0074] Among them, the emission index V is analyzed according to the Divisia index decomposition method, and the change results of the emission index V from period 0 to period T are calculated, including:

[0075] Assume that an emission index V is affected by n influencing factors, satisfying:

[0076] V=∑ i x 1i x 2i …x ni , and V i =x1i x 2i …x ni ;

[0077] Where i represents the category that constitutes the emission index V;

[0078] Index V from 0 period V 0 Change to V in T phase T , where the carbon emission change in period T is multiplied and decomposed to obtain:

[0079]

[0080]

[0081] In multiplication decomposition mode,

[0082] Among them, W i * =(V i T / V T +V i 0 / V 0 ) / 2, which represents the weight term of the multiplicative decomposition;

[0083] D ot It represents the change of emission index V within period T, where T is a constant greater than 1. The specific value is selected according to actual needs.

[0084] As mentioned above, after analyzing the emission index V, the influencing factors that can be optimized can be located according to the changes in V. The specific n influencing factors are: lane type, traffic flow, travel time period, vehicle type and vehicle power; and targeted collection and processing of monitoring data are carried out for these influencing factors to improve the accuracy and pertinence of monitoring; in addition, in the subsequent disposal, carbon emission reduction measures can be selected for the influencing factors, which is conducive to the promotion of environmental protection.

[0085] For example, logarithmic intelligent source management is mainly to achieve new energy consumption, taking into account economy and sustainability. The categories of emission index V are such as the types of energy consumed, fossil energy, electric energy, and the means of replacing oil vehicles with new energy vehicles.

[0086] After that, the carbon emission changes within the T period can be analyzed; then the roads in the traffic network to be tested can be screened according to the changes; a change rate threshold can be set during the screening, and the roads whose carbon emission changes exceed the change rate threshold can be screened out and marked. In addition, the monitoring method can be changed according to the changes to improve the timeliness and accuracy of monitoring.

[0087] In this embodiment, after the operation data of the hotspot charging device is obtained, it is uploaded to the processor 101 of the electronic device 100 in real time, and the electronic device 100 performs subsequent processing; or, after the data is obtained, it is stored in the memory 102 of the electronic device 100 or any database that can transmit data with the electronic device 100, and the database is called based on user instructions in the subsequent processing process. The method of obtaining data is not specifically limited here.

[0088] In this embodiment, the steps of mining the selected roads according to the total carbon emissions to obtain the hotspot charging equipment include:

[0089] A twin charging link is established for the selected roads. A road traffic pollution analysis model can be established through the CALPUFF model. Then, based on the known distribution of charging equipment, nodes representing the charging equipment are established in the model, and these nodes are connected to obtain the charging link. The charging link and the charging equipment on the road are twins.

[0090] At the same time, the road traffic pollution analysis model can be used to obtain the road pollutant concentration, which can then be converted into carbon emission concentration, thereby estimating the carbon emissions of each road and the total carbon emissions of the traffic network to be tested.

[0091] Under the constraint of satisfying the total carbon emission, the electricity cost of the charging equipment in each charging link is calculated, and an average electricity cost is determined;

[0092] Charging devices whose cost per kilowatt-hour is greater than the average cost per kilowatt-hour are defined as hot spot charging devices.

[0093] Exemplarily, the constraint on the total carbon emissions is as follows: the sum of the carbon emissions of the charging link and the carbon emissions of each road is less than the total carbon emissions.

[0094] In this embodiment, the method further includes:

[0095] Marking a charging device whose kilowatt-hour cost is less than a second cost threshold as a secondary charging device; wherein the second cost threshold is less than the average kilowatt-hour cost;

[0096] Exemplarily, the second cost threshold is flexibly set according to actual needs; for example, the average cost per kilowatt-hour is 0.75 yuan / kilowatt-hour, and the second cost threshold is set to 0.55 yuan / kilowatt-hour.

[0097] In this embodiment, the secondary charging devices are clustered with the hot charging device as the center, and the secondary charging devices in the same cluster are connected to the hot charging device to share the energy consumption pressure of the hot charging device. The clustering algorithm used is the K-means algorithm.

[0098] In this embodiment, the K-means algorithm may specifically include:

[0099] The positions of all charging devices are used as sampling data;

[0100] Take each hot spot charging device as the cluster center point and set the number N of charging devices near each cluster center point;

[0101] Calculate the distance between each sampled data and the cluster center point, and use the minimum distance between each sampled data and the cluster center point as the loss function;

[0102] Divide the sampled data into multiple data clusters according to the loss function;

[0103] Adjusting the position of the cluster center point according to the loss function until the loss function meets a preset convergence condition;

[0104] When it is determined that the loss function meets the preset convergence condition, multiple data clusters corresponding to the sampled data are used as multiple management strategy coverage areas.

[0105] Understandably, the preset convergence conditions can be flexibly set according to user needs. For example, if the cluster center points adjusted multiple times all refer to the same sampled data, or if the deviation between the iterative loss function and the previous loss function does not exceed 0.5%, then the loss function is determined to have converged. In this way, the sampled data is divided into multiple target sub-areas through the K-means algorithm to achieve regional monitoring on the link, ensuring that when a hot spot charging device is monitored, the adjacent charging devices can also be monitored without the need for full coverage monitoring.

[0106] In addition, in practical applications, even with the same N value, different classification results may appear in multiple independent clusterings. Therefore, in order to avoid local optimal solutions, multiple K-means clustering can be performed using the global optimal value of the particle at the current moment to obtain multiple clustering results, and one of the clustering results with the same cluster center value is randomly selected as the target clustering result for subsequent fitness calculations.

[0107] In some optional embodiments, after the step of monitoring carbon emissions of the hotspot charging device that executes the management strategy, the method further includes: judging whether to divert vehicles that are about to reach the hotspot charging device based on the monitoring results; the basis for the judgment is whether the carbon emission concentration value exceeds 0.1%.

[0108] In step S220, a device carbon emission prediction model is established based on the historical traffic data of the traffic network to be tested and the operation data of the hotspot charging device, including:

[0109] Preprocess the historical traffic data of the traffic network to be tested and the operation data of the hotspot charging device to obtain a sample set, and divide the sample set into a training data set, a test data set and a verification data set; the preprocessing includes but is not limited to cleaning, amplification, deletion and normalization;

[0110] A deep learning model that has been migrated to the digital intelligent resource management platform is trained using the training data set to obtain a model for predicting equipment carbon emissions.

[0111] The model is tested and verified through the test data set and the verification data set, and the verification is not trained through recycling; after the verification conditions are met, the trained equipment carbon emission prediction model is obtained.

[0112] In step S23, before the real-time traffic data of the traffic network to be tested is input into the equipment carbon emission prediction model, the real-time traffic data can be pre-processed, and the data obtained from the idle roads can be excluded, thereby reducing the magnitude of data processing and improving timeliness. The idle roads can be pre-screened in the intelligent transportation system, and the screening results can be fed back to the data intelligent resource management platform.

[0113] In step S240, a management strategy for the hotspot charging device is selected according to the target carbon emissions of the hotspot charging device;

[0114] In fact, a policy library can be set up to store some management policies for remote control of charging devices, and these policies can be customized for hotspot charging devices. These policies include, but are not limited to: charging charging devices, charging charging devices during idle time, discharging charging devices, stopping discharging charging devices; reducing the charging efficiency of charging devices, improving the charging efficiency of charging devices; and switching the charging interface of charging devices to the power grid, discharging charging devices, stopping discharging charging devices; switching the charging interface of charging devices to adjacent charging devices, etc.

[0115] In step S250, carbon emission monitoring is performed on the hot spot charging equipment that executes the management strategy, which can form a closed loop, so that the carbon emission monitoring of the hot spot charging equipment in the traffic network to be tested is timely and effective.

[0116] In this embodiment, carbon emissions monitoring can also be performed on charging devices other than hot spot charging devices to avoid sudden changes in carbon emissions due to short-term, localized huge changes in traffic.

[0117] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the electronic device 100 described above can refer to the corresponding process of each step in the aforementioned method, and will not be elaborated herein.

[0118] The embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is run on a computer, the computer executes the method described in the above embodiment.

[0119] The embodiment of the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is run on a computer, the computer executes the method described in the above embodiment.

[0120] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented by hardware, and can also be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0121] In summary, the embodiments of the present application provide a carbon emission monitoring method, electronic device, storage medium and program product. In this technical solution, firstly, the layout of the established charging equipment is obtained through the authorized digital intelligent source management platform; and the layout can be mapped with the traffic road network to be tested, and associated with the traffic road network, so as to establish a carbon emission prediction model for the equipment; and some charging equipment in the traffic road network is busy, and some are relatively idle; after obtaining the historical traffic data of the traffic road network to be tested, the hot charging equipment can be analyzed; in this way, when performing carbon detection, targeted monitoring can be carried out, and the target carbon emissions of the hot charging equipment in the future period of time can be predicted. After the prediction results come out, the hot charging equipment can be controlled in advance to avoid the actual carbon emissions exceeding the limit, and the emission reduction pressure of the hot charging equipment can be shared by other charging equipment, so as to avoid its carbon emissions exceeding the limit and avoid the total carbon emissions exceeding the limit; at the same time, the risk of vehicle congestion caused by the hot charging equipment is reduced, and unnecessary burdens are reduced.

[0122] In the embodiments provided by the present application, it should be understood that the disclosed method can also be implemented in other ways. The method embodiments described above are merely schematic, for example, the flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and a part of the module, a program segment or a code includes one or more executable instructions for implementing the specified logical function. It should also be noted that each box in the block diagram and / or the flow chart, and the combination of the boxes in the block diagram and / or the flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0123] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A carbon emission monitoring method based on digital intelligent source management, characterized in that: The method comprises: Based on the digital intelligent source management platform, mark the hotspot charging equipment in the traffic network to be tested and obtain the operating data of the hotspot charging equipment, which at least includes the device IP address, chargeable capacity and energy efficiency information; Establishing a device carbon emission prediction model based on the historical traffic data of the traffic network to be tested and the operation data of the hotspot charging device; Inputting the real-time traffic data of the traffic network to be tested into the equipment carbon emission prediction model to predict the target carbon emission of the hotspot charging equipment; Selecting a management strategy for the hotspot charging device according to the target carbon emissions of the hotspot charging device; Carbon emissions are monitored for the hotspot charging device that executes the management strategy.

2. The method according to claim 1, characterized in that Based on the digital intelligent source management platform, the steps of marking the hotspot charging equipment in the traffic network to be tested and obtaining the operation data of the hotspot charging equipment include: Obtain management authority for the digital intelligent energy management platform and query the distribution data of all charging devices in the area where the traffic network to be tested is located; Analyze the distribution data through big data technology to find out the charging equipment that needs to be monitored for carbon emissions and mark them as hot spot charging equipment; Obtaining the operating data of the hotspot charging device.

3. The method according to claim 2, characterized in that The steps of analyzing the distribution data by big data technology, mining charging equipment that needs to be monitored for carbon emissions, and marking them as hot spot charging equipment include: Based on big data technology, determine the emission index V and total carbon emissions of the traffic network to be tested; Analyze the emission index V according to the Divisia index decomposition method, calculate the change result of the emission index V from period 0 to period T, and screen the roads in the traffic network to be tested based on the change result; The selected roads are excavated according to the total carbon emissions to obtain the hot spot charging equipment.

4. The method according to claim 3, characterized in that The emission index V is analyzed according to the Divisia index decomposition method, and the change results of the emission index V from period 0 to period T are calculated, including: Assume that an emission index V is affected by n influencing factors, satisfying: V=∑ i x 1i x 2i …x ni , and V i =x 1i x 2i …x ni ; Where i represents the category that constitutes the emission index V; Index V from 0 period V 0 Change to V in T phase T , where the carbon emission change in period T is multiplied and decomposed to obtain: In multiplication factorization mode in, represents the weight term of the multiplicative decomposition; D ot Represents the change of emission index V during period T.

5. The method according to claim 3, characterized in that: The step of mining the selected roads according to the total carbon emissions to obtain the hotspot charging device includes: Establish twin charging links for the selected roads; Under the constraint of satisfying the total carbon emission, the electricity cost of the charging equipment in each charging link is calculated, and an average electricity cost is determined; Charging devices whose cost per kilowatt-hour is greater than the average cost per kilowatt-hour are defined as hot spot charging devices.

6. The method according to claim 5, characterized in that The method further comprises: Marking a charging device whose kilowatt-hour cost is less than a second cost threshold as a secondary charging device; wherein the second cost threshold is less than the average kilowatt-hour cost; Taking the hotspot charging device as the center, the secondary charging devices are clustered, and the secondary charging devices in the same cluster are connected to the hotspot charging devices to share the energy consumption pressure of the hotspot charging devices.

7. The method according to claim 1, characterized in that After the step of monitoring carbon emissions of the hotspot charging device that executes the management strategy, the method further includes: According to the monitoring result, it is determined whether to divert the vehicles that are about to reach the hot spot charging device.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory coupled to each other, wherein the memory stores a computer program. When the computer program is executed by the processor, the electronic device executes the method as claimed in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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