CN116862118A

CN116862118A - Carbon emission information generation method, device, electronic equipment and computer readable medium

Info

Publication number: CN116862118A
Application number: CN202311134253.3A
Authority: CN
Inventors: 李强; 赵峰; 赵林林; 安丽利; 刘茂凯; 许中平; 谢可; 王誉博; 吴晓峰; 张维; 张朔
Original assignee: Beijing Sgitg Accenture Information Technology Co ltd; State Grid Information and Telecommunication Group Co Ltd
Current assignee: Beijing Sgitg Accenture Information Technology Co ltd; State Grid Information and Telecommunication Group Co Ltd
Priority date: 2023-09-05
Filing date: 2023-09-05
Publication date: 2023-10-10
Anticipated expiration: 2043-09-05
Also published as: CN116862118B

Abstract

Embodiments of the present disclosure disclose carbon emission information generation methods, devices, electronic devices, and computer-readable media. A specific implementation of the method includes: creating each device node according to each device information corresponding to the target object; for each device information, creating at least one data collection node; performing clustering processing on each data collection node; for each data Collect node groups, create carbon emission nodes; create carbon summary nodes; create carbon emission knowledge graphs; in response to the carbon emission knowledge graph meeting the preset startup conditions, obtain the carbon data group set, and store each carbon data obtained to the data collection node ; Generate each carbon emission information corresponding to each carbon emission node, and store each generated carbon emission information to the carbon emission node; generate carbon emission summary information; store the carbon emission summary information to the carbon summary node. This implementation can save memory resources during querying when fully tracing the carbon emission data source.

Description

Carbon emission information generation method, device, electronic equipment and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a method, an apparatus, an electronic device, and a computer readable medium for generating carbon emission information.

Background

In recent years, global warming has become one of the most recognized global crisis, and greenhouse gas (carbon dioxide) emissions are generally considered to be the most leading cause of global warming, so control of carbon emissions has become a mainstream initiative. Currently, in determining the carbon emission amount, the following methods are generally adopted: and determining the total carbon emission based on the reported or filled carbon activity data directly, and realizing the prediction of the total carbon emission based on the historical data so as to directly guide the subsequent business by the predicted total carbon emission.

However, the inventors found that when the log file is manipulated in the above manner, there are often the following technical problems:

first, complete traceability of the data source cannot be achieved, and various data in the carbon emission amount determining process cannot be stored based on the transaction flow, so that the data source needs to be queried from all data when checking, and memory resource waste is caused during query.

Secondly, the predicted single total carbon emission lacks a comparison reference, whether the predicted total carbon emission is reliable or not cannot be directly determined, historical carbon activity data are checked, and data sources still need to be queried from all data during checking, so that memory resource waste is caused during query.

The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose a carbon emission information generation method, apparatus, electronic device, and computer readable medium to solve one or more of the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide a carbon emission information generation method, the method including: creating each equipment node according to the previously configured equipment information of the corresponding target object; for each piece of equipment information in the pieces of equipment information, at least one data acquisition node corresponding to the piece of equipment information is created according to the data acquisition address information corresponding to the piece of equipment information; clustering the created data acquisition nodes to obtain a data acquisition node group set; for each data collection node group in the data collection node group set, creating a carbon emission node corresponding to the data collection node group; according to each created carbon emission node, creating a carbon summary node corresponding to the target object; creating a carbon emission knowledge graph according to the equipment nodes, the data acquisition node group set, the carbon emission nodes and the carbon summarization nodes; in response to detecting that the carbon emission knowledge graph meets a preset starting condition, acquiring a carbon data set corresponding to the data acquisition node set according to each data acquisition address information corresponding to each equipment information, and storing each acquired carbon data to a data acquisition node corresponding to the carbon data; generating respective carbon emission information corresponding to the respective carbon emission nodes according to the carbon data set, and storing each generated carbon emission information to a carbon emission node corresponding to the carbon emission information; generating carbon emission summary information according to the generated carbon emission information; and storing the carbon emission summary information into the carbon summary node.

In a second aspect, some embodiments of the present disclosure provide a carbon emission information generation device, the device including: a first creation unit configured to create respective device nodes from respective device information of the corresponding target objects configured previously; a second creation unit configured to create, for each of the respective device information, at least one data collection node corresponding to the device information, based on data collection address information corresponding to the device information; the clustering unit is configured to perform clustering processing on each created data acquisition node to obtain a data acquisition node group set; a third creating unit configured to create, for each of the data collection node groups in the data collection node group set, a carbon emission node corresponding to the data collection node group; a fourth creation unit configured to create a carbon summary node corresponding to the target object, based on the created carbon emission nodes; a fifth creation unit configured to create a carbon emission knowledge graph from the respective device nodes, the data collection node group set, the respective carbon emission nodes, and the carbon summary node: an acquisition unit configured to acquire a carbon data group set corresponding to the data acquisition node group set according to respective data acquisition address information corresponding to respective equipment information in response to detection that the carbon emission knowledge graph satisfies a preset starting condition, and store each acquired carbon data to a data acquisition node corresponding to the carbon data; a first generation unit configured to generate respective carbon emission information corresponding to the respective carbon emission nodes based on the carbon data group set, and store each of the generated carbon emission information to a carbon emission node corresponding to the carbon emission information; a second generation unit configured to generate carbon emission summary information from the generated respective carbon emission information; and a storage unit configured to store the carbon emission summary information to the carbon summary node.

In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.

In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

The above embodiments of the present disclosure have the following advantageous effects: by the carbon emission information generation method of some embodiments of the present disclosure, memory resources during query can be saved when the carbon emission data source is completely traced. Specifically, when the carbon emission data source is completely traced, the reason for wasting the memory resource during the query is as follows: the complete traceability of the data source cannot be realized, and various data in the carbon emission amount determining process cannot be stored based on the transaction flow, so that the data source is required to be inquired from all data when checking, and the memory resource is wasted during inquiry. Based on this, the carbon emission information generation method of some embodiments of the present disclosure first creates each device node from each device information of the corresponding target object configured previously. Thereby, the traceability of the carbon emission data source can be linked to the original equipment node. Then, for each piece of the above-mentioned individual piece of equipment information, at least one data acquisition node corresponding to the above-mentioned piece of equipment information is created based on the data acquisition address information corresponding to the above-mentioned piece of equipment information. Thus, the data acquisition address of each device can be configured and embodied as a transactional data acquisition node. And then, clustering the created data acquisition nodes to obtain a data acquisition node group set. Therefore, the data acquisition nodes for acquiring the same type of carbon data can be gathered in the same data acquisition node group. And secondly, for each data acquisition node group in the data acquisition node group set, creating a carbon emission node corresponding to the data acquisition node group. Thus, a carbon emission node storing carbon emission information of each type of carbon data can be created. Then, according to each carbon emission node created, a carbon summary node corresponding to the target object is created. Thus, a carbon summary node storing carbon emission summary information of the corresponding target object can be created. And then, creating a carbon emission knowledge graph according to the equipment nodes, the data acquisition node group set, the carbon emission nodes and the carbon summarization nodes. Therefore, the knowledge graph can be constructed based on the nodes for determining the business processes in the process of the carbon emission summary information, and the knowledge graph can be used for realizing the structural storage of the data for determining the business processes in the process of the carbon emission summary information. And secondly, responding to the detection that the carbon emission knowledge graph meets a preset starting condition, acquiring a carbon data set corresponding to the data acquisition node set according to the data acquisition address information corresponding to the equipment information, and storing each acquired carbon data to a data acquisition node corresponding to the carbon data. Therefore, the carbon data can be automatically acquired and stored according to the data acquisition address information stored in each data acquisition adding point when the carbon emission knowledge graph meets the preset starting condition. Next, respective carbon emission information corresponding to the respective carbon emission nodes is generated based on the carbon data group collection, and each generated carbon emission information is stored to the carbon emission node corresponding to the carbon emission information. Thus, according to each carbon data stored in each data acquisition node group, carbon emission information corresponding to the data acquisition node group can be automatically generated and stored based on the dimension of the carbon emission node. Then, carbon emission summary information is generated from the generated respective carbon emission information. Thus, the carbon emission summary information may characterize the total amount of carbon emissions of the target object. And finally, storing the carbon emission summary information into the carbon summary node. Thus, the carbon emission summary information can be stored at the carbon summary node. And because all the information (including carbon data, carbon emission information and carbon emission summary information) in the carbon emission summary information process is stored in all the nodes in the carbon emission knowledge graph, and all the nodes in the carbon emission knowledge graph are nodes of the transaction flow type, the complete traceability of the carbon emission data source based on the transaction flow can be realized. And further, memory resources during inquiry can be saved when the carbon emission data source is completely traced.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a flow chart of some embodiments of a carbon emission information generation method according to the present disclosure;

FIG. 2 is a schematic structural view of some embodiments of a carbon emission information generation device according to the present disclosure;

fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

Operations such as collection, storage, and use of personal information (e.g., object information) of a user involved in the present disclosure, and the like, until the corresponding operations are performed, the relevant organization or individual is up to the end to include carrying out personal information security impact evaluation, fulfilling notification obligations to the personal information body, obtaining authorized consent of the personal information body in advance, and the like.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates a flow 100 of some embodiments of a carbon emission information generation method according to the present disclosure. The carbon emission information generation method comprises the following steps:

step 101, creating each device node according to each device information of the corresponding target object configured in advance.

In some embodiments, an execution subject (e.g., a terminal device) of the carbon emission information generation method may create respective device nodes from respective device information of the corresponding target object configured previously. The target object may be any subject. For example, the target object may be a power plant. The respective device information may be device-related information of respective devices in the target object. The device information may include, but is not limited to, a device identification. In practice, for each piece of equipment information, the execution body may create a graph database node as an equipment node with the equipment identifier included in the piece of equipment information as a node identifier.

Optionally, before step 101, the foregoing execution body may further execute the following steps:

the method comprises the steps of responding to detection of control dragging operation of a device control displayed in a device configuration page corresponding to the target object, enabling a stopping position of the control dragging operation to be in a preset device configuration page area, and displaying a device model corresponding to a device type of the device control in the preset device configuration page area. The device configuration page may be a page of each device for configuring the target object. The device control may be a preset control characterizing the device. The preset device configuration page area may be a page area of a device for visualizing the custom configuration target object. The device model may be a preconfigured two-dimensional or three-dimensional model of the visualized device. In practice, the execution body may display the device model at the stop position.

And a second step of displaying a device configuration window in response to detecting a selection operation acting on the device model. The device identification configuration page module, the at least one sensor configuration page module and the at least one data acquisition interface configuration page module are displayed in the device configuration window. The device configuration window may be a window configuring detailed information of the device. The selection operation may include, but is not limited to: clicking and hovering. The device identification configuration page module may be a page module for configuring a device identification. The sensor configuration page module may be a page module for configuring sensors and sensor data acquisition addresses. The data acquisition interface configuration page module may be a page module for configuring the data acquisition interface and the data acquisition interface address.

And a third step of determining configuration information corresponding to the device identification configuration operation as device information in response to detecting the device identification configuration operation acting on the device identification configuration page module. Wherein the device identification configuration operation may be an operation of inputting a device identification. The configuration information may be an input device identification.

And fourthly, in response to detection of the sensor configuration operation acting on any sensor configuration page module, determining the sensor identification and the sensor data acquisition address corresponding to the sensor configuration operation as sensor information corresponding to the equipment identification. The sensor configuration operation may be an operation of selecting or inputting a sensor identification and a sensor data acquisition address.

And fifthly, responding to detection of data acquisition interface configuration operation acting on any data acquisition interface configuration page module, and determining a data acquisition interface identifier and a data acquisition interface address corresponding to the data acquisition interface configuration operation as data acquisition interface information corresponding to the equipment identifier. The data acquisition interface configuration operation may be an operation of selecting or inputting a data acquisition interface identifier and a data acquisition interface address. Therefore, the user can customize and configure each device of the target object in advance and the data acquisition interface information of each device.

Step 102, for each piece of equipment information in the pieces of equipment information, at least one data acquisition node corresponding to the equipment information is created according to the data acquisition address information corresponding to the equipment information.

In some embodiments, for each device information in the respective device information, the executing body may create at least one data collection node corresponding to the device information according to data collection address information corresponding to the device information. The data collection address information may be at least one address information for collecting carbon data. For example, the data acquisition address information may include at least one url.

In some optional implementations of some embodiments, the executing entity may create at least one data collection node corresponding to the device information according to data collection address information corresponding to the device information by:

in response to determining that the device information corresponds to a sensor identifier, determining at least one sensor identifier corresponding to the device information as a set of sensor identifiers. The sensor identification may be an identification of a sensor that detects real-time data of the device to which the device information corresponds. The sensors may include, but are not limited to: flow sensor, weight sensor.

And a second step of determining a sensor data acquisition address corresponding to each sensor identifier in the sensor identifier set to obtain a sensor data acquisition address set. The sensor data acquisition address may be a data acquisition address of a sensor corresponding to the sensor identifier in the device corresponding to the device information.

And thirdly, adding the sensor data acquisition address set to data acquisition address information corresponding to the equipment information so as to update the data acquisition address information. The initial value of the data acquisition address information may be a null value.

Fourth, for each sensor data acquisition address included in the data acquisition address information, using a sensor identifier corresponding to the sensor data acquisition address as a node identifier, using the sensor data acquisition address as node attribute information, and creating a node corresponding to the node identifier and the node attribute information as a data acquisition node. In practice, the execution body may create, as the data collection node, a graph data node corresponding to the node identifier and the node attribute information. Thus, each sensor data acquisition address corresponding to the device can be embodied as a transactional data acquisition node.

And fifthly, determining at least one data acquisition interface identifier corresponding to the equipment information as a data acquisition interface identifier set in response to determining that the equipment information corresponds to the data acquisition interface identifier. The data collection interface identifier may be an identifier of a preconfigured interface for collecting real-time data of the device corresponding to the device information.

And sixthly, determining the data acquisition interface address corresponding to each data acquisition interface identifier in the data acquisition interface identifier set to obtain a data acquisition interface address set. The data acquisition interface address may be a link address of the data acquisition interface.

And seventh, adding the data acquisition interface address set to data acquisition address information corresponding to the equipment information so as to update the data acquisition address information.

Eighth, for each data collection interface address included in the data collection address information, using a data collection interface identifier corresponding to the data collection interface address as a node identifier, using the data collection interface address as node attribute information, and creating a node corresponding to the node identifier and the node attribute information as a data collection node. In practice, the execution body may create a graph data node corresponding to the node identifier and the node attribute information as a data acquisition node. Thus, each data acquisition interface corresponding to the device can be embodied as a transactional data acquisition node.

And 103, clustering the created data acquisition nodes to obtain a data acquisition node group set.

In some embodiments, the executing body may perform clustering processing on each created data collection node to obtain a data collection node group set. In practice, the execution body may collect each data collection node corresponding to the same data type into the same data collection node group, to obtain a data collection node group set. Here, the data type may be a type of carbon data. For example, the data type may be, but is not limited to, one of the following: raw coal, clean coal, coal products, coke, coal gas, crude oil, gasoline, kerosene, diesel oil, fuel oil, natural gas and external power supply.

Step 104, for each data collection node group in the data collection node group set, creating a carbon emission node of the corresponding data collection node group.

In some embodiments, for each data collection node group in the collection of data collection node groups, the execution body may create a carbon emission node corresponding to the data collection node group. In practice, the execution subject may create a carbon emission node identification corresponding to the data collection node group, and then may create a graph database node with the carbon emission node identification as a carbon emission node. And each data acquisition node identifier corresponding to the data acquisition node group can be stored in the carbon emission node.

And 105, creating a carbon summary node corresponding to the target object according to each created carbon emission node.

In some embodiments, the executing entity may create a carbon summary node corresponding to the target object according to each created carbon emission node. In practice, the execution body may create a carbon summary node identifier corresponding to each carbon emission node, and then may create a graph database node with the carbon summary node identifier as the carbon summary node. The carbon summary node may store each carbon emission node identifier corresponding to each carbon emission node.

And 106, creating a carbon emission knowledge graph according to each equipment node, the data acquisition node group set, each carbon emission node and the carbon summarization node.

In some embodiments, the executing entity may create a carbon emission knowledge graph based on the respective device nodes, the set of data collection nodes, the respective carbon emission nodes, and the carbon summary nodes.

In some optional implementations of some embodiments, the executing entity may create the carbon emission knowledge graph from the respective device nodes, the data collection node group set, the respective carbon emission nodes, and the carbon summary node by:

first, creating a carbon data acquisition node corresponding to the target object. The carbon data collection node may represent a transactional node that begins to collect carbon data for a target object. The carbon data collection node may store therein, but is not limited to, at least one of the following: object identification of the target object and a period of collecting carbon data.

And secondly, determining each equipment node as each child node of the carbon data acquisition node. The child nodes and the upper nodes may have a hierarchical relationship.

And thirdly, for each equipment node in the equipment nodes, determining at least one data acquisition node corresponding to the equipment node as each associated node of the equipment information. There may be an association between a node and an associated node.

Fourth, for each of the carbon emission nodes, determining each data collection node in the data collection node group corresponding to the carbon emission node as each associated node of the carbon emission node.

And fifthly, determining each carbon emission node as each sub-node of the carbon summary node.

And sixthly, generating a carbon emission knowledge graph according to the carbon data acquisition node, the carbon summarization node, the determined child nodes and the determined association nodes. In practice, the executing body may generate a carbon emission knowledge graph according to the carbon data collection node, the carbon summary node, the determined sub-nodes and the determined associated nodes by using a knowledge graph creation manner in a graph database.

Step 107, in response to detecting that the carbon emission knowledge graph meets a preset starting condition, acquiring a carbon data set corresponding to the data acquisition node set according to the data acquisition address information corresponding to the equipment information, and storing each piece of acquired carbon data to the data acquisition node corresponding to the carbon data.

In some embodiments, the executing body may acquire a carbon data group set corresponding to the data acquisition node group set according to respective data acquisition address information corresponding to respective device information in response to detecting that the carbon emission knowledge graph satisfies a preset starting condition, and store each acquired carbon data to a data acquisition node corresponding to the carbon data. The preset starting condition may be that the current time is a starting time of the carbon emission knowledge graph. The preset starting condition may be that a starting operation corresponding to the carbon emission knowledge graph is detected at the current time. For example, the launch operation may be a selection operation acting on the launch control. In practice, for each data acquisition address information in the data acquisition address information, the executing body may access an address corresponding to the data acquisition address information through a wired connection manner or a wireless connection manner, so as to obtain carbon data of a data acquisition node corresponding to the data acquisition address information, and store the carbon data to the data acquisition node corresponding to the carbon data. And then, combining each carbon data corresponding to each data acquisition node group in the data acquisition node group set into a carbon data group to obtain a carbon data group set.

It should be noted that the wireless connection may include, but is not limited to, 3G/4G connections, wiFi connections, bluetooth connections, wiMAX connections, zigbee connections, UWB (ultra wideband) connections, and other now known or later developed wireless connection means.

And 108, generating each carbon emission information corresponding to each carbon emission node according to the carbon data set, and storing each generated carbon emission information to the carbon emission node corresponding to the carbon emission information.

In some embodiments, the executing body may generate respective carbon emission information corresponding to the respective carbon emission nodes according to the carbon data set, and store each generated carbon emission information to a carbon emission node corresponding to the carbon emission information.

In some optional implementations of some embodiments, the executing entity may generate the respective carbon emission information corresponding to the respective carbon emission nodes from the carbon data set by:

the first step, for each carbon data set in the set of carbon data sets, is performed the steps of:

and a first sub-step of determining the data acquisition node group corresponding to the carbon data group as a target data acquisition node group.

And a second sub-step of determining the carbon emission node corresponding to the target data collection node group as a target carbon emission node.

And a third sub-step of determining a carbon data type corresponding to the carbon data group.

And a fourth sub-step of carrying out dimension unification processing on each carbon data in the carbon data group to obtain each carbon data after dimension unification processing as a carbon data group to be summarized, and storing each carbon data to be summarized in the carbon data group to be summarized into a data acquisition node corresponding to the carbon data to be summarized. In practice, the execution body may convert each carbon data into each carbon data corresponding to the same carbon data unit, so as to perform dimension unification processing on each carbon data, and obtain each carbon data after the dimension unification processing as the carbon data group to be summarized.

And a fifth sub-step, summarizing the carbon data to be summarized in the carbon data group to be summarized to obtain summarized carbon data. In practice, the execution subject may determine the sum of the respective carbon data to be summarized as summarized carbon data. For example, the summarized carbon data may be the total weight of the summarized raw coal.

And a sixth sub-step of acquiring carbon emission factor information corresponding to the carbon data type. The carbon emission factor information may be a carbon emission coefficient.

And a second step of generating carbon emission information corresponding to the target carbon emission node according to the summarized carbon data and the carbon emission factor information. In practice, the execution subject may determine the product of the summarized carbon data and the carbon emission factor information as carbon emission information corresponding to the target carbon emission node. Thus, the carbon emission information of the carbon emission node corresponding to each carbon data type may be determined from the dimensions of the different carbon data types.

And step 109, generating carbon emission summary information according to the generated carbon emission information.

In some embodiments, the executing entity may generate carbon emission summary information according to the generated respective carbon emission information. In practice, the execution subject may determine the sum of the respective carbon emission information as carbon emission summary information.

And step 110, storing the carbon emission summary information to a carbon summary node.

In some embodiments, the executive may store the carbon emission summary information to the carbon summary node.

Optionally, the above execution body may further execute the following steps:

and a first step of acquiring residual energy carbon information corresponding to the target object. The remaining energy carbon information may be the total amount of carbon dioxide that can be emitted by the target object.

And a second step of updating the residual energy carbon information according to the carbon emission summary information. In practice, the execution body may update the remaining energy carbon information to a preset value in response to determining that the carbon emission summary information is greater than the remaining energy carbon information. The preset value may be 0. The execution body may further update remaining energy carbon information to a difference between the remaining energy carbon information and the carbon emission summary information in response to determining that the carbon emission summary information is equal to or less than the remaining energy carbon information.

Third, in response to determining that the updated remaining energy carbon information satisfies a preset carbon check condition, performing the steps of:

and a first sub-step of generating carbon investigation prompt information corresponding to the carbon summary node, and storing the carbon investigation prompt information into the carbon summary node. The above-mentioned preset carbon investigation condition may be that the updated remaining energy carbon information is smaller than a preset threshold. The preset threshold value may be preset, and is not particularly limited. In practice, the execution subject may input the object identifier of the target object, updated remaining energy carbon information, and the carbon emission summary information into a preset carbon investigation prompting template, so as to obtain carbon investigation prompting information. The preset carbon investigation prompting template may be a preset corpus template, and is not particularly limited.

A second sub-step of, for each carbon emission node in the above carbon emission knowledge graph, performing the steps of:

first, according to the type of the carbon data corresponding to the carbon emission node, determining carbon emission intensity information corresponding to the carbon emission node. Wherein each carbon data type may correspond to a carbon emission intensity range information set. The carbon emission intensity range information in the carbon emission intensity range information set may include preset carbon emission intensity information and a carbon emission information range corresponding to the above-mentioned preset carbon emission intensity information. The preset carbon emission intensity information may be intensity representing the amount of carbon emission. For example, the preset carbon emission intensity information may be, but is not limited to, one of the following: primary, secondary, tertiary, quaternary, and penta. In practice, the execution subject may determine the carbon emission intensity range information set corresponding to the carbon data type. Then, carbon emission intensity range information including the above carbon emission information in the carbon emission intensity range included in the carbon emission intensity range information set may be determined as target carbon emission intensity range information. Then, the preset carbon emission intensity information included in the above-described target carbon emission intensity range information may be determined as carbon emission intensity information.

And secondly, sequencing the carbon data groups to be summarized corresponding to the carbon emission nodes to obtain a carbon data sequence to be summarized as a carbon data sequence in response to determining that the carbon emission intensity information meets a preset carbon emission intensity condition. The preset carbon emission intensity condition may be that the carbon emission intensity information is greater than three levels. In practice, the execution body may perform descending order arrangement on each carbon data to be summarized in the carbon data group to be summarized corresponding to the carbon emission node, so as to obtain a carbon data sequence to be summarized as the carbon data sequence.

Thirdly, determining carbon data difference information of every two carbon data in the carbon data sequence to obtain each carbon data difference information. In practice, the execution body may determine the absolute value of the difference between the two carbon data as the carbon data difference information.

Fourth, previous carbon data corresponding to the carbon data difference information satisfying the preset maximum condition in the carbon data difference information is combined into a carbon data sequence to be examined. The preset maximum condition may be that the carbon data difference information is the maximum value of the carbon data difference information. The previous carbon data corresponding to the carbon data difference information may be the first carbon data of the two carbon data corresponding to the carbon data difference information.

Fifthly, for each carbon data to be inspected in the carbon data sequence to be inspected, determining carbon inspection rendering information of a data acquisition node corresponding to the carbon data to be inspected according to the carbon data sequence to be inspected, and storing the carbon inspection rendering information to the data acquisition node corresponding to the carbon data to be inspected. In practice, the execution body may determine a normalized value of the carbon data to be inspected corresponding to the carbon data sequence to be inspected. Then, according to the preset rendering color value range information, a rendering color value corresponding to the normalized value can be determined. In practice, the execution body may determine, as the aggregate rendering color value, a sum of a minimum rendering color value and a maximum rendering color value corresponding to the preset rendering color value range information. Then, a product of the aggregate rendering color value and the normalized value may be determined as a rendering color value corresponding to the normalized value. Here, the color value may be a one-channel color value or a three-channel color value, which is not particularly limited.

Sixth, in response to determining that the carbon emission intensity information does not satisfy the preset carbon emission intensity condition, storing preset carbon investigation rendering information into each data acquisition node in the data acquisition node group corresponding to the carbon emission node. The preset carbon investigation rendering information may be a rendering color value representing colorless.

Seventh, determining carbon investigation rendering information corresponding to the carbon emission node according to the carbon emission intensity information, and storing the carbon investigation rendering information to the carbon emission node. Wherein, each carbon emission intensity information may correspond to preset rendering color information. In practice, the execution body may determine rendering color information preset corresponding to the carbon emission intensity information as carbon investigation rendering information corresponding to the carbon emission node,

a third sub-step of, in response to detection of a visualization operation acting on the above-described carbon emission knowledge graph, performing the following steps:

first, the carbon investigation prompt information is displayed in a carbon summary node in the displayed carbon emission knowledge graph. The visualization operation may be a selection operation acting on a knowledge-graph display control. Specifically, the execution body may display the carbon investigation prompt information in a visual circle corresponding to the carbon summary node.

Secondly, according to the carbon investigation rendering information corresponding to each carbon emission node, rendering each carbon emission node in the displayed carbon emission knowledge graph. In practice, for each carbon emission node, the execution body may render a visual circle corresponding to the carbon emission node according to the rendering color value corresponding to the carbon emission node.

Thirdly, according to the carbon investigation rendering information corresponding to each data acquisition node, rendering each data acquisition node in the displayed carbon emission knowledge graph. In practice, for each data acquisition node, the execution body may render the visualized circle corresponding to the data acquisition node according to the rendering color value corresponding to the data acquisition node.

Fourth, according to the carbon emission knowledge graph after rendering, the time for acquiring the carbon data set and the object information of the target object, a carbon investigation knowledge graph image is generated. In practice, the execution subject may generate the carbon investigation knowledge graph image using the carbon emission knowledge graph after the rendering process as an image and the time and the object information as an image text.

Fifth, the carbon investigation knowledge graph image is displayed in a popup window. Wherein, the popup window also displays an image saving control. The image save control may be used to save the carbon investigation knowledge-graph image to the local. Therefore, the carbon check can be rapidly performed by using the carbon check knowledge graph image after rendering.

Optionally, the above execution body may further execute the following steps:

And combining each carbon data stored in each data acquisition node in the data acquisition node group set in a preset historical time period into a carbon data sequence set. The carbon data sequences in the carbon data sequence set correspond to the data acquisition nodes in the data acquisition nodes.

And secondly, carrying out dimension unification on each group of carbon data sequences corresponding to the same data acquisition node group in the carbon data sequence set to obtain each carbon data sequence after the dimension unification as a carbon data sequence group.

And thirdly, inputting the obtained carbon data sequence groups into a pre-trained carbon data prediction model to obtain predicted carbon data groups. The carbon data prediction model may be a time sequence prediction model which is trained in advance and takes a carbon data sequence as input data and predicted carbon data as output data. The predicted carbon data sets in the respective predicted carbon data sets correspond to data collection node sets in the set of data collection node sets. The predicted carbon data in the predicted carbon data set corresponds to data collection nodes in the data collection node set.

Fourth, for each of the above-described respective predicted carbon data sets, the following steps are performed:

A first sub-step of determining a carbon data type corresponding to the predicted carbon data set.

And a second sub-step of summarizing each predicted carbon data in the predicted carbon data group to obtain summarized predicted carbon data. In practice, the execution subject may determine the sum of the respective predicted carbon data as the aggregated predicted carbon data.

And a third sub-step of acquiring carbon emission factor information corresponding to the carbon data type.

And a fourth sub-step of generating predicted carbon emission information of a carbon emission node corresponding to the predicted carbon data set as first predicted carbon emission information based on the summarized predicted carbon data and the carbon emission factor information. In practice, the execution subject may determine the product of the summarized predicted carbon data and the carbon emission factor information as predicted carbon emission information corresponding to the carbon emission node of the predicted carbon data group.

And fifth, combining each carbon emission information stored in each carbon emission node in the preset history period into a carbon emission information sequence set. The carbon emission information sequences in the carbon emission information sequence set correspond to the carbon emission nodes in the respective carbon emission nodes described above.

And sixthly, inputting the carbon emission information sequence set into a pre-trained carbon emission information prediction model to obtain each piece of predicted carbon emission information as each piece of second predicted carbon emission information, wherein the carbon emission information prediction model can be a pre-trained time sequence prediction model which takes the carbon emission information sequence as input data and takes the predicted carbon emission information as output data. The predicted carbon emission information in the respective predicted carbon emission information corresponds to a carbon emission node in the respective carbon emission nodes.

Seventh, for each of the above-described respective carbon emission nodes, the following steps are performed:

a first sub-step of determining first predicted carbon emission information and second predicted carbon emission information corresponding to the carbon emission nodes.

And a second sub-step of determining a predicted carbon emission information absolute difference between the determined first predicted carbon emission information and the second predicted carbon emission information. In practice, the execution subject may determine an absolute value of a difference between the first predicted carbon emission information and the second predicted carbon emission information as a predicted carbon emission information absolute difference.

And a third sub-step of determining total predicted carbon emission information of the determined first predicted carbon emission information and the second predicted carbon emission information. In practice, the execution subject may determine the sum of the first predicted carbon emission information and the second predicted carbon emission information as total predicted carbon emission information.

And a fourth sub-step of generating confidence information corresponding to the first predicted carbon emission information and the second predicted carbon emission information based on the absolute difference of the predicted carbon emission information and the total predicted carbon emission information. In practice, the execution subject may determine a ratio of the absolute difference of the predicted carbon emission information to the total predicted carbon emission information as the first value. The difference between the second value and the first value may then be determined as confidence information. The second value may be 1.

Eighth, generating aggregate first predicted carbon emission information according to each generated first predicted carbon emission information. In practice, the above-described execution subject may determine the sum of the respective first predicted carbon emission information as the aggregated first predicted carbon emission information.

And a ninth step of generating summarized second predicted carbon emission information according to each piece of second predicted carbon emission information. In practice, the above-described execution subject may determine each of the second predicted carbon emission information as the aggregated second predicted carbon emission information.

And a tenth step of determining an absolute difference of the determined total predicted carbon emission information of the total first predicted carbon emission information and the total second predicted carbon emission information. In practice, the execution subject may determine an absolute value of a difference between the summarized first predicted carbon emission information and the summarized second predicted carbon emission information as a summarized predicted carbon emission information absolute difference.

And eleventh step, determining total predicted carbon emission information of the determined total first predicted carbon emission information and the determined total second predicted carbon emission information. In practice, the execution subject may determine the sum of the summarized first predicted carbon emission information and the summarized second predicted carbon emission information as the summarized predicted carbon emission information.

And a twelfth step of generating confidence information corresponding to the summarized first predicted carbon emission information and the summarized second predicted carbon emission information as summarized confidence information based on the absolute difference of the summarized predicted carbon emission information and the summarized predicted carbon emission information. In practice, the execution subject may determine a ratio of the absolute difference of the total predicted carbon emission information and the total predicted carbon emission information as the third value. The difference between the second value and the third value may then be determined as aggregate confidence information.

And thirteenth step, generating a carbon prediction report based on the generated first predicted carbon emission information, the second predicted carbon emission information, the generated confidence information, the summarized first predicted carbon emission information, and the summarized second predicted carbon emission information. In practice, the execution subject may organize the first predicted carbon emission information, the second predicted carbon emission information, the confidence information, the aggregate first predicted carbon emission information, and the aggregate second predicted carbon emission information according to a preset document content format, to obtain a carbon prediction document as a carbon prediction report. Here, the specific setting of the preset document content format is not limited. The carbon prediction document may be a PDF document.

The first step-thirteenth step are taken as an invention point of the embodiment of the disclosure, so that the technical problem mentioned in the background art is solved, the predicted single total carbon emission is lack of comparison reference, whether the predicted total carbon emission is reliable or not cannot be directly determined, historical carbon activity data is checked, data sources still need to be queried from all data during checking, and memory resource waste during query is caused. ". The data source still needs to be queried from all data during checking, so that the factors causing memory resource waste during query are often as follows: the predicted single total carbon emission lacks a comparison reference, and whether the predicted total carbon emission is reliable or not cannot be directly determined, and historical carbon activity data is checked. If the above factors are solved, the effect of saving the memory resource in the query process during the carbon check can be achieved. To achieve this, the present disclosure not only predicts carbon data from the dimensions of the historical carbon data stored by the initial data collection node, but also generates predicted carbon emission information for each carbon emission node using the predicted carbon data. The carbon emission information is also predicted from the dimension of the historical carbon emission information stored by the carbon emission nodes, and thus the predicted carbon emission information of each carbon emission node can be compared in two aspects, and the confidence of the predicted carbon emission information can be determined by using the confidence information. And finally, determining summarized predicted carbon emission information by using predicted carbon emission information predicted in two modes, and determining the reliability of the summarized predicted carbon emission information by using summarized confidence information. Thus, the predicted carbon emission related information can be compared from two dimensions and used as a reference, and the confidence related information can be used for judging, so that the historical carbon activity data does not need to be checked again. And further, the memory resource during inquiry can be saved during carbon check.

With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a carbon emission information generation apparatus, which correspond to those method embodiments shown in fig. 1, and which are particularly applicable in various electronic devices.

As shown in fig. 2, the carbon emission information generation device 200 of some embodiments includes: a first creation unit 201, a second creation unit 202, a clustering unit 203, a third creation unit 204, a fourth creation unit 205, a fifth creation unit 206, an acquisition unit 207, a first generation unit 208, a second generation unit 209, and a storage unit 210. Wherein the first creating unit 201 is configured to create each device node according to each device information of the corresponding target object configured previously; the second creating unit 202 is configured to create, for each piece of the above-mentioned respective piece of equipment information, at least one data collection node corresponding to the above-mentioned piece of equipment information, based on the data collection address information corresponding to the above-mentioned piece of equipment information; the clustering unit 203 is configured to perform clustering processing on each created data acquisition node to obtain a data acquisition node group set; the third creating unit 204 is configured to create, for each of the data collection node groups in the set of data collection node groups, a carbon emission node corresponding to the data collection node group; the fourth creating unit 205 is configured to create a carbon summary node corresponding to the above-described target object from the created respective carbon emission nodes; the fifth creating unit 206 is configured to create a carbon emission knowledge graph from the above-described respective device nodes, the above-described data collection node group set, the above-described respective carbon emission nodes, and the above-described carbon summary nodes: the obtaining unit 207 is configured to obtain a carbon data group set corresponding to the data collection node group set according to respective data collection address information corresponding to respective device information in response to detecting that the carbon emission knowledge graph satisfies a preset start condition, and store each obtained carbon data to a data collection node corresponding to the carbon data; the first generation unit 208 is configured to generate respective carbon emission information corresponding to the respective carbon emission nodes based on the carbon data group set, and store each of the generated carbon emission information to the carbon emission node corresponding to the carbon emission information; the second generation unit 209 is configured to generate carbon emission summary information from the generated respective carbon emission information; the storage unit 210 is configured to store the carbon emission summary information to the carbon summary node.

It will be appreciated that the elements described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the apparatus 200 and the units contained therein, and are not described in detail herein.

Referring now to fig. 3, a schematic diagram of an electronic device 300 (e.g., a terminal device) suitable for use in implementing some embodiments of the present disclosure is shown. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.

As shown in fig. 3, the electronic device 300 may include a processing means 301 (e.g., a central processing unit, a graphics processor, etc.) that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.

It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: creating each equipment node according to the previously configured equipment information of the corresponding target object; for each piece of equipment information in the pieces of equipment information, at least one data acquisition node corresponding to the piece of equipment information is created according to the data acquisition address information corresponding to the piece of equipment information; clustering the created data acquisition nodes to obtain a data acquisition node group set; for each data collection node group in the data collection node group set, creating a carbon emission node corresponding to the data collection node group; according to each created carbon emission node, creating a carbon summary node corresponding to the target object; creating a carbon emission knowledge graph according to the equipment nodes, the data acquisition node group set, the carbon emission nodes and the carbon summarization nodes; in response to detecting that the carbon emission knowledge graph meets a preset starting condition, acquiring a carbon data set corresponding to the data acquisition node set according to each data acquisition address information corresponding to each equipment information, and storing each acquired carbon data to a data acquisition node corresponding to the carbon data; generating respective carbon emission information corresponding to the respective carbon emission nodes according to the carbon data set, and storing each generated carbon emission information to a carbon emission node corresponding to the carbon emission information; generating carbon emission summary information according to the generated carbon emission information; and storing the carbon emission summary information into the carbon summary node.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a first creation unit, a second creation unit, a clustering unit, a third creation unit, a fourth creation unit, a fifth creation unit, an acquisition unit, a first generation unit, a second generation unit, and a storage unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the first creation unit may also be described as "a unit that creates each device node from each device information of the corresponding target object configured previously".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A carbon emission information generation method, comprising:

creating each equipment node according to the previously configured equipment information of the corresponding target object;

for each piece of equipment information in the pieces of equipment information, at least one data acquisition node corresponding to the equipment information is created according to the data acquisition address information corresponding to the equipment information;

clustering the created data acquisition nodes to obtain a data acquisition node group set;

for each data collection node group in the data collection node group set, creating a carbon emission node corresponding to the data collection node group;

creating a carbon summary node corresponding to the target object according to each created carbon emission node;

creating a carbon emission knowledge graph according to the equipment nodes, the data acquisition node group set, the carbon emission nodes and the carbon summarization nodes;

responding to the fact that the carbon emission knowledge graph meets a preset starting condition, acquiring a carbon data set corresponding to the data acquisition node set according to each piece of data acquisition address information corresponding to each piece of equipment information, and storing each piece of acquired carbon data to a data acquisition node corresponding to the carbon data;

Generating respective carbon emission information corresponding to the respective carbon emission nodes according to the carbon data set, and storing each generated carbon emission information to a carbon emission node corresponding to the carbon emission information;

generating carbon emission summary information according to the generated carbon emission information;

and storing the carbon emission summary information to the carbon summary node.

2. The method of claim 1, wherein the creating a carbon emission knowledge-graph from the respective equipment nodes, the set of data collection nodes, the respective carbon emission nodes, and the carbon summary node comprises:

creating a carbon data acquisition node corresponding to the target object;

determining each equipment node as each child node of the carbon data acquisition node;

for each equipment node in the equipment nodes, determining at least one data acquisition node corresponding to the equipment node as each associated node of the equipment information;

for each of the respective carbon emission nodes, determining a respective data collection node in a data collection node group corresponding to the carbon emission node as a respective associated node of the carbon emission node;

Determining the respective carbon emission nodes as respective child nodes of the carbon summary node;

and generating a carbon emission knowledge graph according to the carbon data acquisition node, the carbon summarization node, the determined child nodes and the determined association nodes.

3. The method of claim 1, wherein the creating at least one data collection node corresponding to the device information from the data collection address information corresponding to the device information comprises:

in response to determining that the device information corresponds to a sensor identification, determining at least one sensor identification corresponding to the device information as a set of sensor identifications;

determining a sensor data acquisition address corresponding to each sensor identifier in the sensor identifier set to obtain a sensor data acquisition address set;

adding the sensor data acquisition address set to data acquisition address information corresponding to the equipment information so as to update the data acquisition address information;

for each sensor data acquisition address included in the data acquisition address information, taking a sensor identifier corresponding to the sensor data acquisition address as a node identifier, taking the sensor data acquisition address as node attribute information, and creating a node corresponding to the node identifier and the node attribute information as a data acquisition node.

4. The method of claim 3, wherein the creating at least one data collection node corresponding to the device information from the data collection address information corresponding to the device information further comprises:

determining at least one data acquisition interface identifier corresponding to the equipment information as a data acquisition interface identifier set in response to determining that the equipment information corresponds to the data acquisition interface identifier;

determining a data acquisition interface address corresponding to each data acquisition interface identifier in the data acquisition interface identifier set to obtain a data acquisition interface address set;

adding the data acquisition interface address set to data acquisition address information corresponding to the equipment information so as to update the data acquisition address information;

and for each data acquisition interface address included in the data acquisition address information, taking a data acquisition interface identifier corresponding to the data acquisition interface address as a node identifier, taking the data acquisition interface address as node attribute information, and creating a node corresponding to the node identifier and the node attribute information as a data acquisition node.

5. The method of claim 1, wherein prior to creating each device node from each device information of a corresponding target object of a previous configuration, the method further comprises:

Responding to control dragging operation of a device control displayed in a device configuration page corresponding to the target object, wherein the stopping position of the control dragging operation is in a preset device configuration page area, and a device model corresponding to the device type of the device control is displayed in the preset device configuration page area;

in response to detecting a selection operation acting on the device model, displaying a device configuration window, wherein a device identification configuration page module, at least one sensor configuration page module and at least one data acquisition interface configuration page module are displayed in the device configuration window;

in response to detecting a device identification configuration operation acting on the device identification configuration page module, determining configuration information corresponding to the device identification configuration operation as device information;

in response to detecting a sensor configuration operation acting on an arbitrary sensor configuration page module, determining a sensor identification and a sensor data acquisition address corresponding to the sensor configuration operation as sensor information corresponding to the device identification;

and in response to detection of data acquisition interface configuration operation acting on any data acquisition interface configuration page module, determining a data acquisition interface identifier and a data acquisition interface address corresponding to the data acquisition interface configuration operation as data acquisition interface information corresponding to the equipment identifier.

6. The method of claim 1, wherein the generating respective carbon emission information corresponding to the respective carbon emission nodes from the set of carbon data sets comprises:

for each carbon data set in the set of carbon data sets, performing the steps of:

determining a data acquisition node group corresponding to the carbon data group as a target data acquisition node group;

determining a carbon emission node corresponding to the target data acquisition node group as a target carbon emission node;

determining a carbon data type corresponding to the carbon data set;

performing dimension unification on each carbon data in the carbon data group to obtain each carbon data subjected to dimension unification as a carbon data group to be summarized, and storing each carbon data to be summarized in the carbon data group to be summarized into a data acquisition node corresponding to the carbon data to be summarized;

summarizing each piece of carbon data to be summarized in the carbon data group to be summarized to obtain summarized carbon data;

acquiring carbon emission factor information corresponding to the carbon data type;

and generating carbon emission information corresponding to the target carbon emission node according to the summarized carbon data and the carbon emission factor information.

7. The method according to one of claims 1-6, wherein the method further comprises:

acquiring residual energy carbon information corresponding to the target object;

updating the residual energy carbon information according to the carbon emission summary information;

in response to determining that the updated residual energy carbon information satisfies the preset carbon check condition, performing the steps of:

generating carbon investigation prompt information corresponding to the carbon summary node, and storing the carbon investigation prompt information to the carbon summary node;

for each carbon emission node in the carbon emission knowledge graph, performing the steps of:

determining carbon emission intensity information corresponding to carbon emission information corresponding to the carbon emission node according to the carbon data type corresponding to the carbon emission node;

in response to determining that the carbon emission intensity information meets a preset carbon emission intensity condition, sequencing carbon data sets to be summarized corresponding to the carbon emission nodes to obtain a carbon data sequence to be summarized as a carbon data sequence;

determining carbon data difference information of every two carbon data in the carbon data sequence to obtain each carbon data difference information;

combining previous carbon data corresponding to the carbon data difference information meeting a preset maximum condition in the carbon data difference information into a carbon data sequence to be examined, wherein the previous carbon data is before the previous carbon data in the carbon data sequence;

For each carbon data to be inspected in the carbon data sequence to be inspected, determining carbon inspection rendering information of a data acquisition node corresponding to the carbon data to be inspected according to the carbon data sequence to be inspected, and storing the carbon inspection rendering information to the data acquisition node corresponding to the carbon data to be inspected;

in response to determining that the carbon emission intensity information does not meet the preset carbon emission intensity condition, storing preset carbon investigation rendering information into each data acquisition node in a data acquisition node group corresponding to the carbon emission node;

determining carbon investigation rendering information corresponding to the carbon emission node according to the carbon emission intensity information, and storing the carbon investigation rendering information to the carbon emission node;

in response to detecting a visualization operation acting on the carbon emission knowledge graph, performing the steps of:

displaying the carbon investigation prompt information in a carbon summary node in the displayed carbon emission knowledge graph;

according to the carbon investigation rendering information corresponding to each carbon emission node, rendering each carbon emission node in the displayed carbon emission knowledge graph;

according to the carbon investigation rendering information corresponding to each data acquisition node, rendering each data acquisition node in the displayed carbon emission knowledge graph;

Generating a carbon investigation knowledge graph image according to the carbon emission knowledge graph after rendering treatment, the time for acquiring the carbon data set and the object information of the target object;

and displaying the carbon investigation knowledge graph image in a popup window, wherein an image storage control is also displayed in the popup window.

8. A carbon emission information generation device, comprising:

a first creation unit configured to create respective device nodes from respective device information of the corresponding target objects configured previously;

a second creation unit configured to create, for each piece of the respective piece of equipment information, at least one data collection node corresponding to the piece of equipment information according to data collection address information corresponding to the piece of equipment information;

the clustering unit is configured to perform clustering processing on each created data acquisition node to obtain a data acquisition node group set;

a third creating unit configured to create, for each data collection node group in the data collection node group set, a carbon emission node corresponding to the data collection node group;

a fourth creation unit configured to create a carbon summary node corresponding to the target object from the created respective carbon emission nodes;

A fifth creating unit configured to create a carbon emission knowledge graph from the respective equipment nodes, the data collection node group set, the respective carbon emission nodes, and the carbon summary node:

an acquisition unit configured to acquire a carbon data group set corresponding to the data acquisition node group set according to respective data acquisition address information corresponding to respective equipment information in response to detection that the carbon emission knowledge graph satisfies a preset starting condition, and store each acquired carbon data to a data acquisition node corresponding to the carbon data;

a first generation unit configured to generate respective carbon emission information corresponding to the respective carbon emission nodes from the carbon data group set, and store each generated carbon emission information to a carbon emission node corresponding to the carbon emission information;

a second generation unit configured to generate carbon emission summary information from the generated respective carbon emission information;

and a storage unit configured to store the carbon emission summary information to the carbon summary node.

9. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.

10. A computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-7.