A carbon footprint accounting system and method based on a large model and a blockchain

Abstract

The application discloses a kind of carbon footprint accounting system and method based on large model and block chain, it is related to carbon emission technical field, system includes monitoring module, carbon emission monitoring module, target identification matching module, comparison database, carbon emission prediction module, carbon emission comparison module and carbon emission correction module.Through monitoring module and carbon emission monitoring module, video monitoring and positioning emission position are carried out to target area and the amount of carbon emission is recorded, and by target identification matching module, target object is identified based on image recognition and matched with carbon emission, carbon emission footprint detection is realized.At the same time, by comparison database, carbon emission prediction module, carbon emission comparison module and carbon emission correction module, carbon emission prediction is realized to the behavior input by user, and carbon emission saving amount is calculated and compared and analyzed, for user to be referenced, reduced carbon emission and its corresponding value are displayed to user, to give user more intuitive feeling.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology, specifically to a carbon footprint accounting system and method based on large-scale models and blockchain. Background Technology

[0002] Carbon footprint (CO2 footprint) refers to the total greenhouse gas emissions generated by businesses, activities, products, or individuals through transportation, food production and consumption, and various production processes. It describes the impact of an individual's energy awareness and behavior on the natural world and calls on people to start with themselves. Some companies have already begun to implement environmental protection concepts to reduce their carbon footprint.

[0003] Chinese patent application CN115471066A discloses a carbon footprint accounting system and method, including a terminal, an application server, and a database. The application server contains multiple functional modules for carbon emission accounting, used for data processing, algorithm analysis, and function deployment. The database stores data information. The functional modules include a model field information encoding module, a carbon footprint accounting model module, a carbon accounting strategy module, a mapping relationship module, a category module, a unification module, a data acquisition module, an automatic accounting module, a comparison module, a monitoring module, and an evaluation module. This invention, based on a digital management platform, facilitates and quickly enables flexible configuration, automatic calculation, storage, comparison, querying, and display of carbon emission data. It reduces repetitive work in carbon emission calculation, improves carbon emission accounting efficiency, and ensures the granularity and accuracy of carbon emission calculations.

[0004] However, simply detecting carbon footprint and carbon emissions and displaying the results to users is not enough for those without relevant knowledge to fully understand the meaning of these results. To better promote the concept of reducing carbon emissions, it is necessary not only to detect carbon footprint and predict carbon emissions, but also to compare various carbon footprints and their emissions. When users choose lower-carbon behaviors, the reduced carbon emissions and their corresponding value should be displayed to users, giving them a more intuitive understanding. Summary of the Invention

[0005] The purpose of this invention is to provide a carbon footprint accounting system and method based on large models and blockchain to address the aforementioned shortcomings in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a carbon footprint accounting system based on a large model and blockchain, comprising a monitoring module, a carbon emission monitoring module, a target identification and matching module, a comparison database, a carbon emission prediction module, a carbon emission comparison module, and a carbon emission correction module; The monitoring module is used for video surveillance of the target area to obtain monitoring video; The carbon emission monitoring module is used to monitor carbon emissions in the target area, including locating emission locations and recording carbon emission amounts; The target recognition and matching module is used to acquire the monitoring video of the monitoring module, and perform image recognition on the monitoring video to identify the target object and the target object's behavior; The target identification and matching module is also used to associate and bind the carbon emission amount with the corresponding target object based on image recognition and the emission location and carbon emission amount located by the carbon emission monitoring module. The comparison database is used to store carbon purification information for various comparison behaviors, and value information corresponding to the emissions of the comparison behaviors; The carbon emission prediction module is used to predict the carbon emissions of user behavior based on user input. The carbon emission comparison module is used to obtain carbon emission savings based on the prediction results of the carbon emission prediction module and the target behavior identified by the target identification and matching module and the carbon emission amount associated with it. Based on the carbon emission savings and the comparison database, it calculates and outputs the comparison behavior in the comparison database corresponding to the carbon emission savings. The carbon emission correction module is used to correct the comparison database and the carbon emission prediction module based on the user-selected behavior, and / or the target behavior, and the carbon emissions of the user matched by the target identification and matching module.

[0007] Furthermore, the target identification and matching module is also used to store target information; the target identification and matching module performs feature recognition and matching on the real-time identified target and the stored target information, and associates and binds the target that matches the target information with the target information; the carbon emission record can be queried through the target information, wherein the feature recognition can use image recognition algorithms to compare the similarity between the identified target and the stored target information, and determine whether the identified target matches the stored target information through the similarity calculation result.

[0008] Furthermore, the target identification and matching module is also used to mark target objects for which no target object information is matched as target objects to be determined; the target identification and matching module determines the target objects to be determined and updates the information of the target objects to be determined into the stored target object information.

[0009] Furthermore, the comparative activities include the carbon dioxide purification capacity per square meter per day of various plants, the carbon dioxide purification capacity per hour and usage cost of various carbon dioxide purification devices, and the carbon emissions per unit quantity of products produced by various production equipment.

[0010] Furthermore, the calculation and output of the comparison in the comparison database corresponding to the carbon emission savings specifically includes: Based on the carbon emission savings and the carbon dioxide purification capacity of the plant per square meter per day, calculate the number of days and time required for the plant to purify the carbon emission savings, including but not limited to displaying the amount of carbon emission savings required to purify one square meter of the plant in one day, and the amount of carbon dioxide purification required to purify one square meter of the plant in one day. Based on the carbon emission savings, the hourly carbon dioxide purification capacity of the carbon dioxide purification equipment, and the operating cost, the time and operating cost required for the equipment to purify the saved carbon emissions can be calculated. The output can display the equipment model, price, required operating time, and the cost incurred for operating the aforementioned time. Based on the carbon emission savings and the carbon emission of a unit of product produced by the production equipment, calculate the number of products produced by the production equipment consuming the carbon emission savings. If the production equipment is a thermal power plant, the output can display how many kilowatt-hours of electricity the carbon emission savings are equivalent to generated by the thermal power plant.

[0011] Furthermore, the carbon emission prediction module includes a big data model, which is trained and generated based on collected carbon emission data from various behaviors.

[0012] Compared with existing technologies, the present invention provides a carbon footprint accounting system based on a large model and blockchain. By setting up a monitoring module, a carbon emission monitoring module, a target identification and matching module, a comparison database, a carbon emission prediction module, a carbon emission comparison module, and a carbon emission correction module, it can monitor, summarize and calculate the carbon emissions of the target area to achieve the carbon footprint accounting task. At the same time, it compares the user's selected behavior with similar behaviors with higher carbon emissions, outputs the value corresponding to the carbon emission savings, and the time and money costs required to neutralize the carbon emissions, giving users a more intuitive understanding and strengthening their awareness of low-carbon and environmental protection.

[0013] A carbon footprint accounting method based on large-scale models and blockchain includes the following steps: S1. Collect data on the relationship between carbon emission behaviors and carbon emission amounts, and train a big data model; S2. Collect the carbon purification amount of various comparative behaviors and the value corresponding to the emissions of the comparative behaviors and store them in the comparative database; S3. Deploy video surveillance equipment and carbon emission monitoring equipment in the target area; S4. Based on the video surveillance equipment and carbon emission monitoring equipment, determine the target object and the corresponding carbon emission amount; S5. Combine the user information of the accounting system to match the target object with the user information. If the match is successful, bind and associate it with the user information. S6. After a user selects a behavior, use a big data model to predict the carbon emissions of that behavior. S7. Based on the prediction results and the carbon emissions matched with user information, determine the amount of carbon emission savings. S8. Calculate and output the comparison behavior in the comparison database corresponding to the carbon emission savings based on the carbon emission savings.

[0014] This invention discloses a carbon footprint accounting system and method based on a large-scale model and blockchain, relating to the field of carbon emission technology. The system includes a monitoring module, a carbon emission monitoring module, a target identification and matching module, a comparison database, a carbon emission prediction module, a carbon emission comparison module, and a carbon emission correction module. The monitoring and carbon emission monitoring modules perform video monitoring of the target area, locate emission positions, and record carbon emission amounts. The target identification and matching module identifies target objects based on images and matches them with carbon emission amounts to achieve carbon emission footprint detection. Simultaneously, the comparison database, carbon emission prediction module, carbon emission comparison module, and carbon emission correction module predict carbon emissions based on user input, calculate and compare carbon emission savings, providing users with a reference and displaying the reduced carbon emissions and their corresponding value, giving users a more intuitive understanding. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 A system structure block diagram provided for embodiments of the present invention; Figure 2 The diagram illustrates the method steps provided in this embodiment of the invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0018] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0020] Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments may be embodied in different 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, and will enable those skilled in the art to fully understand the scope of this disclosure.

[0021] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0022] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0023] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded.

[0024] The embodiments described herein can be described with reference to plan views and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations can be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to those shown in the drawings, but include modifications to configurations formed based on manufacturing processes. Therefore, the areas illustrated in the drawings are schematic in nature, and the shapes of the areas shown in the figures illustrate specific shapes of areas of an element, but are not intended to be limiting.

[0025] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0026] Please see Figures 1-2 A carbon footprint accounting system based on a large model and blockchain includes a monitoring module, a carbon emission monitoring module, a target identification and matching module, a comparison database, a carbon emission prediction module, a carbon emission comparison module, and a carbon emission correction module.

[0027] The monitoring module is used to perform video surveillance on the target area and obtain the surveillance video.

[0028] The carbon emission monitoring module is used to monitor carbon emissions in a target area, including locating emission sites and recording carbon emission amounts; it can be implemented directly using existing technologies, such as exhaust gas remote sensing monitoring systems.

[0029] The target recognition and matching module is used to acquire the monitoring video from the monitoring module and perform image recognition on the monitoring video to identify the target objects and their behavior. The target recognition and matching module is also used to associate and bind the carbon emission amount with the corresponding target object based on the emission location and carbon emission amount located by the carbon emission monitoring module.

[0030] The target recognition and matching module is also used to store target information. The target recognition and matching module performs feature recognition and matching on the real-time identified target information and the stored target information, and associates and binds the target information that matches the target information. This allows users to query their carbon emission records through the target information. The feature recognition can use image recognition algorithms to compare the similarity between the identified target information and the stored target information to calculate whether the identified target information matches the stored target information.

[0031] The target recognition and matching module can also mark targets without matching information as targets to be determined; the target recognition and matching module then identifies the targets to be determined and updates their information to the stored target information. This facilitates the addition and saving of unsaved targets.

[0032] The comparison database is used to store carbon purification information for various comparison behaviors, as well as value information corresponding to the emissions of the comparison behaviors. The comparison behaviors include the carbon dioxide purification per square meter per day for various plants, the carbon dioxide purification per hour and usage cost of various carbon dioxide purification devices, and the carbon emissions per unit quantity of products produced by various production equipment.

[0033] The carbon emission prediction module is used to predict the carbon emissions of user behaviors based on user input. Based on a big data model, the module pre-collects carbon emission data from various behaviors to train the model. In practical applications, user behaviors are input into the trained big data model, and the model outputs the predicted carbon emissions. The behaviors include the manner and extent of carbon emissions, with the extent including distance and time, such as the number of kilometers driven or the number of hours the air conditioner was on.

[0034] The carbon emission comparison module is used to calculate carbon emission savings based on the prediction results of the carbon emission prediction module and the target behavior and associated carbon emission amounts identified by the target identification and matching module. Based on the carbon emission savings and the comparison database, it calculates and outputs the corresponding comparison behavior in the database, such as determining which comparison behavior in the database the saved carbon emission amount corresponds to. Specifically, this includes: Based on the carbon emission savings and the carbon dioxide purification capacity of the plant per square meter per day, calculate the number of days and time required for the plant to purify the carbon emission savings, including but not limited to displaying the amount of carbon emission savings required per square meter per day or the amount of carbon emission savings required per square meter per day. Based on the carbon emission savings, the hourly carbon dioxide purification capacity of the carbon dioxide purification equipment, and the operating cost, the time and operating cost required for the equipment to purify the carbon emission savings can be calculated. The output can display the equipment model, price, required working time, and the cost required to work for the above time. Based on the carbon emission savings and the carbon emission of the products produced per unit quantity by the production equipment, calculate the quantity of products produced by the production equipment using the carbon emission savings. If the production equipment is a thermal power plant, the output can display how many kilowatt-hours of electricity the carbon emission savings are equivalent to generated by the thermal power plant.

[0035] In other embodiments of this application, the prediction results or the target behavior identified by the target identification and matching module and the carbon emissions associated with it can be compared with the carbon emissions of other behaviors of the same type as user behavior or target behavior in the settlement system to obtain carbon emission savings, and the comparison behaviors in the comparison database corresponding to the carbon emission savings can be calculated and output.

[0036] The carbon emission correction module is used to correct the carbon emissions of the comparison database and the carbon emission prediction module based on user-selected behaviors, such as user behavior or target behavior, and the carbon emissions of users matched by the target identification and matching module.

[0037] Furthermore, this application also includes connecting carbon footprint accounting systems in different target areas through blockchain technology, enabling data interoperability and facilitating comprehensive management.

[0038] A carbon footprint accounting method based on large-scale models and blockchain includes the following steps: S1. Collect data on the relationship between carbon emission behaviors and carbon emission amounts, and train a big data model; S2. Collect the carbon purification amount of various comparative behaviors and the value corresponding to the emissions of the comparative behaviors and store them in the comparative database; S3. Deploy video surveillance equipment and carbon emission monitoring equipment in the target area; S4. Based on the video surveillance equipment and carbon emission monitoring equipment, determine the target object and the corresponding carbon emission amount; S5. Combine the user information of the accounting system to match the target with the user information. If the match is successful, bind and associate it with the user information. S6. After a user selects a behavior, use a big data model to predict the carbon emissions of that behavior. S7. Based on the prediction results and the carbon emissions matched with user information, determine the amount of carbon emission savings. In some embodiments, behaviors similar to the user's selected behavior can also be retrieved, and the carbon emissions of the retrieved behaviors can be predicted to obtain a comparative carbon emission. If the comparative carbon emission is greater than the carbon emission of the user's selected behavior, the difference can be calculated to obtain the carbon emission savings.

[0039] S8. Calculate and output the comparison behavior in the comparison database corresponding to the carbon emission savings based on the carbon emission savings.

[0040] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A carbon footprint accounting system based on large-scale models and blockchain, characterized in that: It includes a monitoring module, a carbon emission monitoring module, a target identification and matching module, a comparison database, a carbon emission prediction module, a carbon emission comparison module, and a carbon emission correction module; The monitoring module is used for video surveillance of the target area to obtain monitoring video; The carbon emission monitoring module is used to monitor carbon emissions in the target area, including locating emission locations and recording carbon emission amounts; The target recognition and matching module is used to acquire the monitoring video of the monitoring module and perform image recognition on the monitoring video to identify the target object and the target object behavior. The target object behavior includes the carbon emission mode and execution degree of the target object, and the execution degree includes execution distance and execution time. The target identification and matching module is also used to associate and bind the carbon emission amount with the corresponding target object based on the emission location and carbon emission amount located by the image recognition and the carbon emission monitoring module, and to calculate the total carbon emission amount of the target area. The comparison database is used to store carbon purification information for various comparison behaviors, and value information corresponding to the emissions of the comparison behaviors; The carbon emission prediction module is used to predict the carbon emissions of user behavior based on user input. The carbon emission comparison module is used to obtain carbon emission savings based on the prediction results of the carbon emission prediction module and the target behavior identified by the target identification and matching module and the carbon emission amount associated with it. Based on the carbon emission savings and the comparison database, it calculates and outputs the comparison behavior in the comparison database corresponding to the carbon emission savings. The carbon emission correction module is used to correct the comparison database and the carbon emission prediction module based on the user's selected user behavior, and / or target behavior, and the carbon emission amount of the user matched by the target identification and matching module. The target identification and matching module is also used to store target information; The target recognition and matching module is also used to perform feature recognition and matching on the real-time identified target objects and the stored target object information, and to associate and bind the target objects that match the target object information with the target object information. The target identification and matching module is further configured to mark target objects for which no target object information is matched as target objects to be determined; the target identification and matching module is further configured to determine the target objects to be determined and update the information of the target objects to be determined to the stored target object information; The calculation and output of the carbon emission savings in the comparison database specifically includes: Based on the carbon emission savings and the carbon dioxide purification capacity per square meter per day of the plant, calculate the number of days and time required for the plant to purify the said carbon emission savings. Based on the carbon emission savings, the hourly carbon dioxide purification capacity of the carbon dioxide purification equipment, and the operating cost, calculate the time required and operating cost for the equipment to purify the aforementioned carbon emission savings. Calculate the number of products produced by the production equipment using the saved carbon emissions and the carbon emissions per unit quantity of products produced by the production equipment.

2. The carbon footprint accounting system based on a large model and blockchain according to claim 1, characterized in that: The comparative behaviors include the carbon dioxide purification capacity per square meter per day of various plants, the carbon dioxide purification capacity and usage cost per hour of various carbon dioxide purification equipment, and the carbon emissions per unit quantity of products produced by various production equipment.

3. The carbon footprint accounting system based on a large model and blockchain according to claim 1, characterized in that: The carbon emission prediction module includes a big data model, which is trained and generated based on carbon emission data collected from various behaviors.

4. A carbon footprint accounting method based on large-scale models and blockchain, applied to a carbon footprint accounting system based on large-scale models and blockchain as described in any one of claims 1-3, characterized in that: Includes the following steps: S1. Collect data on the relationship between carbon emission behaviors and carbon emission amounts, and train a big data model; S2. Collect the carbon purification amount of various comparative behaviors and the value corresponding to the emissions of the comparative behaviors and store them in the comparative database; S3. Deploy video surveillance equipment and carbon emission monitoring equipment in the target area; S4. Based on the video surveillance equipment and carbon emission monitoring equipment, determine the target object and the corresponding carbon emission amount; S5. Combine the user information of the accounting system to match the target object with the user information. If the match is successful, bind and associate it with the user information. S6. After a user selects a behavior, use a big data model to predict the carbon emissions of that behavior. S7. Based on the prediction results and the carbon emissions matched with user information, determine the amount of carbon emission savings. S8. Calculate and output the comparison behavior in the comparison database corresponding to the carbon emission savings based on the carbon emission savings.

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