Enterprise carbon footprinting system, method and storage medium with data evidence

Abstract

The present application relates to the technical field of carbon emission measurement, and particularly refers to an enterprise carbon portrait system and method with data evidence storage and a storage medium. The method comprises the following steps: converting information data of collected energy consumption meters and other energy consumption data related to enterprise production into carbon emission data, and displaying the carbon emission data through a carbon table; adding a time stamp and an address stamp to the information data of the collected energy consumption meters, and uploading the information data together with corresponding image data to a blockchain platform for storage; constructing a dynamic carbon portrait prediction model based on deep learning technology; inputting enterprise operation data, information data of energy consumption meters and other energy consumption data related to enterprise production into the dynamic carbon portrait prediction model, and giving a carbon portrait prediction result of the enterprise through the dynamic carbon portrait prediction model. The present application can ensure that the data is not tamperable, and also dynamically depicts the behavior of energy saving and carbon reduction of the enterprise and accurately predicts the energy consumption trend of the enterprise.

Description

Technical Field

[0001] This invention relates to the technical field of carbon emission measurement, and specifically to a corporate carbon profiling system, method, and storage medium with data-based evidence storage. Background Technology

[0002] Among the steps for enterprises to achieve energy conservation and emission reduction, carbon inventory and carbon auditing are the most crucial. Currently, the data reported by enterprises may be incomplete or distorted, failing to accurately and comprehensively reflect the actual carbon emissions. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a corporate carbon profiling system, method and storage medium with data evidence storage, so as to solve the problem that the existing carbon emission measurement lacks intuitive, efficient and practical tools and thus cannot meet the monitoring requirements.

[0004] The technical solution to achieve the above objectives is:

[0005] This invention provides a data-based carbon profiling method for enterprises, comprising the following steps:

[0006] Collect information data from the enterprise's energy consumption meters, and simultaneously photograph the meter dials to generate image data;

[0007] Collect other energy consumption data related to enterprise production;

[0008] The collected energy consumption meter data and other energy consumption data related to enterprise production are converted into carbon emission data, and the carbon emission data is displayed through a carbon meter.

[0009] The collected energy consumption meter data is added with timestamps and address stamps, and uploaded to the blockchain platform for storage along with the corresponding image data;

[0010] A dynamic carbon profiling prediction model was built based on deep learning technology;

[0011] The system collects the enterprise's operating data, inputs the enterprise's operating data, energy consumption meter data, and other energy consumption data related to the enterprise's production into the dynamic carbon profile prediction model, and then provides the enterprise's carbon profile prediction results through the dynamic carbon profile prediction model.

[0012] A further improvement of the present invention’s data-provable enterprise carbon profiling method is that, when constructing a dynamic carbon profiling prediction model, information data from the enterprise’s energy consumption meters, other energy consumption data related to the enterprise’s production, and the enterprise’s operating data are collected daily and stored in the form of a time series to form a data source.

[0013] The aforementioned data source is used to train a deep learning model, thereby obtaining a dynamic carbon profiling prediction model.

[0014] A further improvement of the present invention's data-provable enterprise carbon profiling method is that it also includes:

[0015] Establish a regression model;

[0016] The collected energy consumption meter data is fitted and predicted using the established regression model to obtain the predicted basic energy consumption.

[0017] A further improvement of the present invention's data-provable enterprise carbon profiling method is that it also includes:

[0018] Generate and distribute enterprise questionnaires based on the knowledge base;

[0019] Collect feedback from the questionnaires, conduct statistical analysis on the feedback, and then generate a carbon report for the company based on the statistical analysis results.

[0020] A further improvement of the present invention's data-provable enterprise carbon profiling method is that it also includes:

[0021] The collected energy consumption meter data is compared with the corresponding image data. If the comparison results are inconsistent, the collected energy consumption meter data and the corresponding image data are deleted. This is to prevent the background data from being manually modified.

[0022] The present invention also provides a storage medium storing a program for a data-provable corporate carbon profiling method.

[0023] When the program of the data-provable enterprise carbon profiling method is executed by the processor, the steps of the data-provable enterprise carbon profiling method are implemented.

[0024] This invention also provides a data-based enterprise carbon profiling system, comprising:

[0025] The data acquisition module is used to collect information data from the company's energy consumption meters, capture image data of the meter dials, and collect other energy consumption data related to the company's production; it is also used to collect the company's operational data.

[0026] A carbon conversion module is connected to the acquisition module. The carbon conversion module is used to convert the information data collected from the energy consumption meter and other energy consumption data related to enterprise production into carbon emission data.

[0027] A carbon meter, connected to the carbon conversion module, is used to display the carbon emission data;

[0028] The data upload module is connected to the acquisition module. The data upload module is used to add timestamps and address stamps to the information data of the collected energy consumption meter, and upload it together with the corresponding image data to the blockchain platform for storage.

[0029] The dynamic carbon profiling prediction model is built based on deep learning technology.

[0030] The processing module is connected to the acquisition module and the dynamic carbon profile prediction model. The processing module is used to input the enterprise's operating data, energy consumption meter information data and other energy consumption data related to the enterprise's production into the dynamic carbon profile prediction model, and to give the enterprise's carbon profile prediction result through the dynamic carbon profile prediction model.

[0031] A further improvement of the present invention's data-based enterprise carbon profiling system is that it also includes a regression model;

[0032] The processing module is connected to the regression model. The processing module is also used to fit and predict the information data of the collected energy consumption meter using the established regression model in order to obtain the prediction result of basic energy consumption.

[0033] A further improvement of the present invention’s data-based enterprise carbon profiling system is that it also includes a carbon inventory quality control module and an automatic form filling module.

[0034] The carbon inventory quality control module is used to generate and distribute enterprise questionnaires based on the knowledge base, and also to perform statistical analysis on the feedback from the questionnaires.

[0035] The automatic form filling module is connected to the carbon inventory quality control module, and the automatic form filling module is used to generate a carbon report for the enterprise based on the feedback statistical analysis results of the questionnaire.

[0036] A further improvement of the enterprise carbon profiling system with data storage capability of the present invention is that it also includes a data filtering module connected to the acquisition module. The data filtering module is used to compare the information data of the acquired energy consumption meter with the corresponding image data. If the comparison results are inconsistent, the acquired information data of the energy consumption meter and the corresponding image data are deleted.

[0037] The beneficial effects of the present invention's enterprise carbon profiling method, system, and storage medium with data-based evidence storage include:

[0038] This invention collects carbon data for enterprises, including information data from energy consumption meters, and also uses imaging technology to collect image data. The data is then uploaded to a blockchain platform for storage, ensuring that the data is tamper-proof and that the collected data is accurate and reliable.

[0039] This invention also creates a carbon profile of an enterprise's energy and carbon behavior, dynamically depicts the enterprise's energy-saving and carbon-reduction behaviors, and accurately predicts the enterprise's energy consumption trends, providing enterprises with dynamic energy consumption strategies to achieve the goal of energy saving and carbon reduction.

[0040] This invention also utilizes carbon meters to intuitively measure and display a company's carbon emissions, making it easy to view. Attached Figure Description

[0041] Figure 1 This is an architecture diagram of the enterprise carbon profiling system platform with data storage capability of the present invention.

[0042] Figure 2 This is a module diagram of the smart carbon meter in the enterprise carbon profiling system with data storage capability of the present invention.

[0043] Figure 3 This is a schematic diagram of the energy consumption prediction module in the enterprise carbon profiling system with data storage capability of the present invention.

[0044] Figure 4 This is a schematic diagram of the structure for constructing a carbon profile in the enterprise carbon profile system with data storage capability of the present invention.

[0045] Figure 5 This is a system diagram of the enterprise carbon profiling system with data storage capability of the present invention.

[0046] Figure 6 This is a flowchart of the enterprise carbon profiling method with data storage capability according to the present invention. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0048] See Figure 1 This invention provides a data-based carbon profiling method, system, and storage medium for enterprises. It primarily utilizes methods and technologies for carbon data collection and storage, combining multiple means and stages to verify the high security and accuracy of carbon data, preventing human tampering, and effectively improving the quality of carbon data monitoring. Accurate carbon data is fundamental for formulating emission reduction strategies, evaluating emission reduction effects, and promoting green and low-carbon development. The following description, in conjunction with the accompanying drawings, illustrates the data-based carbon profiling method, system, and storage medium for enterprises of this invention.

[0049] See Figure 5 This shows a system diagram of the enterprise carbon profiling system with data storage capability of the present invention. The following is in conjunction with... Figure 5 This invention describes the enterprise carbon profiling system with data storage capability.

[0050] like Figure 5As shown, the enterprise carbon profiling system with data storage capability of the present invention includes a data acquisition module 21, a carbon conversion module 22, a carbon meter 23, a data upload module 24, a dynamic carbon profiling prediction model 27, and a processing module 26. The data acquisition module 21 is connected to the carbon conversion module 22, the data upload module 24, and the processing module 26. The carbon conversion module 22 is connected to the carbon meter 23. The data upload module 24 is connected to the blockchain platform 25. The processing module 26 is connected to the dynamic carbon profiling prediction model 27. The data acquisition module 21 is used to collect information data from the enterprise's energy consumption meters, image data from the meter dials, and other energy consumption data related to the enterprise's production. The data acquisition module 21 is also used to collect the enterprise's operational data. The carbon conversion module 22 is used to convert the collected information data from the energy consumption meters and other energy consumption data related to the enterprise's production into carbon emission data. The carbon meter 23 is used to display the carbon emission data. The carbon meter 23 is placed within the enterprise, such as in an exhibition hall, to conveniently and intuitively display the enterprise's carbon emissions. The data upload module 24 adds timestamps and address stamps to the collected energy consumption meter information data and uploads it along with the corresponding image data to the blockchain platform 25 for storage. The dynamic carbon profile prediction model 27 is built based on deep learning technology; the processing module 26 inputs the enterprise's operating data, energy consumption meter information data, and other energy consumption data related to the enterprise's production into the dynamic carbon profile prediction model 27, and the dynamic carbon profile prediction model 27 provides the enterprise's carbon profile prediction results.

[0051] In one specific embodiment of the present invention, the acquisition module 21 is communicatively connected to a camera or camera distributed at the energy consumption meters of the enterprise. The camera or camera can take pictures of the energy consumption meters to form image data, and then send the image data to the acquisition module 21.

[0052] The data acquisition module 21 also communicates with the enterprise's energy consumption meters to collect information data from the energy consumption meters, including devices such as electricity meters, water meters, gas meters, and photovoltaic power generation meters. The collected information data from the energy consumption meters includes the display information data of the electricity meter, water meter, gas meter, and photovoltaic power generation meter at the time of acquisition.

[0053] Furthermore, the acquisition module 21 can send acquisition commands to the corresponding energy consumption meter and camera. The acquisition command controls the energy consumption meter to send the current information data to the acquisition module 21, and controls the camera to take pictures of the energy consumption meter to form image data. This realizes that the acquisition of information data and image data are carried out simultaneously, that is, the acquisition time of the two data is the same.

[0054] Furthermore, the data acquisition module 21 also collects other energy consumption data related to enterprise production. This data includes production environment temperature and humidity data, product inventory data, personnel entry and exit data, customer visit data, vehicle entry and exit data, elevator operation data, and other production-related data. Specifically, the data acquisition module 21 is communicatively connected to temperature and humidity sensors deployed in the production environment, such as the production workshop, to collect production environment temperature and humidity data. The data acquisition module 21 is also connected to an information system, including an enterprise production management system and a building information system (such as the building's low-voltage electrical system), to collect production-related data such as product inventory data, personnel entry and exit data, customer visit data, vehicle entry and exit data, and elevator operation data.

[0055] Furthermore, the carbon conversion module 22 uses carbon methodology algorithms to convert the information data from the energy consumption meter and other energy consumption data related to enterprise production into carbon emission data, i.e., carbon emissions.

[0056] Furthermore, the data upload module 24 encrypts the image data, adds timestamps and address stamps to the collected energy consumption meter information data, and uploads it along with the encrypted image data to the blockchain platform 25 for data storage. Blockchain technology has the characteristic of immutability; once data is on the chain, it cannot be easily modified or deleted. Using the blockchain platform to store data ensures the authenticity and integrity of the data and prevents the possibility of data tampering during transmission and storage.

[0057] The data upload module 24 is also used to add timestamps and address stamps to other energy consumption data related to enterprise production and upload them to the blockchain platform 25 for data storage.

[0058] In one specific embodiment of the present invention, such as Figure 1 As shown, the enterprise carbon profiling system with data storage capability of the present invention also includes a carbon inventory system platform. The data upload module 24 is also connected to the carbon inventory system platform. While uploading the information data of the energy consumption meter with added timestamps and address stamps, the encrypted image data, and other energy consumption data related to enterprise production with added timestamps and address stamps to the blockchain platform 25, the data upload module 24 also uploads the above data to the carbon inventory system platform. The carbon inventory system platform is used to store and manage the above data.

[0059] When the aforementioned data needs to be viewed, the carbon inventory system platform can decrypt the image data and compare it with the information data from energy consumption meters that have been timestamped and address-stamped. This prevents tampering during information processing and transmission, and ensures the evidence preservation of the collected data. The system of this invention achieves comprehensive data tamper prevention, ensuring the accuracy and reliability of carbon data collection. The carbon meter visually displays the enterprise's carbon emissions data on the screen, making the previously invisible and intangible carbon data visible in the form of a meter, and providing evidence preservation of the data, thus meeting the requirements of "monitorable, reportable, and verifiable."

[0060] Furthermore, the system of the present invention also includes a data filtering module connected to the acquisition module. The data filtering module is used to compare the acquired energy consumption meter information data with the corresponding image data. If the comparison results are inconsistent, the acquired energy consumption meter information data and the corresponding image data are deleted.

[0061] The data filtering module is also used to detect anomalies in the collected data, including information data from energy consumption meters, image data, and other energy consumption data related to enterprise production. When anomalies are detected, the data is deleted to improve the reliability and accuracy of the data.

[0062] The data filtering module is connected to the data uploading module. After the data filtering module filters the collected data, the filtered data is uploaded by the data uploading module for evidence storage.

[0063] The data filtering module filters and detects anomalies in the collected data, including sensor data, image data, and location information, eliminating outliers and improving the reliability and accuracy of the data.

[0064] The data acquisition system of this invention improves the reliability of data by cross-verifying data from multiple sources. The acquisition module collects information data through IoT devices (such as electricity meters, water meters, etc.), and collects image data through cameras or cameras. The image data is also encrypted and stored. Time information and location information are added as part of the acquired data, so that the source and transmission path of the data can be traced.

[0065] The system of this invention processes data on the blockchain and performs distributed storage, distributing the data across multiple nodes, thereby improving data security and reliability. Even if some nodes fail, the integrity of the data will not be affected.

[0066] The data acquisition module 21, carbon conversion module 22, and data upload module 24 in the system of the present invention can be integrated into the carbon meter 23, such as... Figure 2As shown, carbon meter 23 can be configured for each enterprise, meaning one carbon meter 23 is configured for each enterprise. It can collect and store daily carbon data, as well as calculate and display daily carbon emission data. The carbon meter has a blockchain interface for connecting to a blockchain platform, and the data upload module 24 uploads data to the blockchain platform through this interface. The carbon meter 23 also has a screen to display the enterprise's current carbon emission data, which represents the enterprise's cumulative carbon emissions. The carbon meter 23 stores a carbon methodology algorithm. The carbon conversion module 22 uses this algorithm to convert the information from the energy consumption meter collected by the collection module 21 and other energy consumption data related to the enterprise's production into carbon emission data, which is then displayed on the screen of the carbon meter. The carbon meter 23 also stores visual evidence and algorithms. The data upload module 24 uses these visual evidence and algorithms to encrypt the image data collected by the acquisition module 21. The carbon meter 23 is also equipped with an IoT gateway. The acquisition module 21 collects information data from energy consumption meters (such as electricity meters, water meters, etc.) detected by IoT sensors and production-related data from the information system through the IoT gateway. Then, the data upload module 24 adds timestamps and address stamps to the information data and uploads it to the blockchain platform along with the encrypted image data.

[0067] like Figure 1 As shown, carbon table 23 is also connected to the carbon inventory system platform, enabling data upload module 24 to upload collected data to the carbon inventory system platform for unified data collection and device management. The dynamic carbon profile prediction model 27 and processing module 26 in the system of this invention are integrated into the carbon inventory system platform. The carbon inventory system platform of this invention is an application that can be installed in the cloud or in a carbon data control center (such as the enterprise's own database or a third-party institution's database). The carbon inventory system platform sets login accounts and passwords for enterprises. Enterprises can install the corresponding program on their own client or directly access the carbon inventory system platform through a browser, and then log in and view the data through their accounts. In this way, the carbon inventory system platform can achieve centralized carbon data management.

[0068] like Figure 1 and Figure 3 As shown, the carbon inventory system platform of the present invention is used to realize functions such as management of smart carbon meters, carbon inventory quality control, automatic form filling and enterprise carbon emission prediction. All data from smart carbon meters are uploaded to the carbon inventory system platform, and unified data collection and equipment management are carried out through the smart carbon meter management module to realize the collection of enterprise energy consumption data and the storage and comparison of digital images.

[0069] The system of the present invention also includes a carbon inventory quality control module and an automatic form filling module; the carbon inventory quality control module is used to generate and distribute enterprise questionnaires based on the knowledge base, and is also used to perform statistical analysis on the feedback of the questionnaires; the automatic form filling module is connected to the carbon inventory quality control module, and is used to generate enterprise carbon reports based on the statistical analysis results of the feedback of the questionnaires.

[0070] Specifically, the platform generates questionnaires for enterprises based on its knowledge base, and corresponding questionnaires are generated according to enterprise type. The carbon inventory system platform distributes the generated questionnaires to the enterprise's client application, which the enterprise can access by logging in, answering the questionnaires, and submitting feedback. The questionnaires are used to clarify the enterprise's survey scope, determine emission inventory boundaries, organizational boundaries and emission ranges, and calculate greenhouse gas emissions. The carbon inventory quality control module also automatically organizes and completes the inventory quality management system and questionnaire statistical result analysis reports, accurately reflecting the entire carbon inventory process for enterprises. The automatic form filling module fills in the questionnaire statistical results into the form templates according to the templates, ultimately generating a carbon report.

[0071] Compared to existing manual carbon auditing, this invention can greatly improve efficiency and reduce costs.

[0072] In one specific embodiment of the present invention, the system has an energy consumption prediction function, implemented by an energy consumption prediction module. This module includes a prediction model and an algorithm. The prediction model includes a regression model and a dynamic carbon profiling prediction model. The regression module is an energy consumption prediction model combining multinomial regression and linear regression. This energy consumption prediction function provides enterprises with decisions on energy conservation and emission reduction.

[0073] The processing module of this invention is connected to a regression model. This processing module is also used to fit and predict the information data of the collected energy consumption meter using the established regression model in order to obtain the prediction result of basic energy consumption.

[0074] Furthermore, the processing module is also used to perform trend analysis on the collected energy consumption meter information data, and to determine whether the line connecting the collected energy consumption meter information data tends to be a straight line or a curve within a set period. If the determination result is a straight line, then linear regression is used to fit the data to obtain the prediction result; if the determination result is a curve, then multinomial regression is used to fit the data to obtain the prediction result.

[0075] Under ideal operating conditions, a company's energy consumption can generally be predicted using a linear regression model. Within a relatively stable period, using linear regression to fit the data, such as a linear function y=ax+b, based on historical energy consumption data, yields relatively small errors. However, a company's operations are often complex, and various factors can influence production energy consumption. Therefore, multinomial regression curves are introduced to fit the data, such as a quadratic function y=ax+b. 2 +bx+c and the cubic function y=ax 3 +bx 2 The regression model uses a combination of data preprocessing and a multinomial function (+cx+d) for fitting. Generally, it's difficult to transform a regression function into a linear model using a simple function transformation; a common approach is to use a multinomial function to fit the data. In the regression model of this invention, multinomial linear regression uses data preprocessing plus a linear model for fitting, significantly reducing the computational complexity of multinomial regression.

[0076] The regression model of this invention solves the data fitting problem. It simulates future trends based on the company's historical energy consumption data. The regression model does not consider other factors affecting the company's energy consumption. It is for predictive analysis of the company's fixed (or stable) energy consumption.

[0077] The dynamic carbon profile prediction model of this invention also considers other energy consumption data and business data related to enterprise production. The business data includes enterprise development stage data, organizational scale data, product market data, target customer data, operating status data, number of employees data, weather environment data, and energy-saving renovation data. The dynamic carbon profile prediction model of this invention comprehensively incorporates the above-mentioned factors affecting enterprise business data to create a carbon profile of enterprise energy consumption.

[0078] Data on a company's development stage and organizational size are static profiles, which are generally updated over a long period of time; while other business data are dynamic profiles, which are updated over a shorter period of time, are more timely, and can more accurately reflect a company's energy consumption.

[0079] The company's operating data can be obtained by the acquisition module 21 by connecting to the company's production management system and the building information system where the company is located, such as the low-voltage system (access control, gate, elevator, etc.), or the company can fill in the corresponding operating data through the client, such as development stage data, organizational scale data, etc.

[0080] The dynamic carbon profiling prediction model of this invention is constructed in the following manner:

[0081] First, data from the enterprise's energy consumption meters, other energy consumption data related to production, and operational data are collected daily. This data is stored in a time-series database, forming a data source. Figure 4 As shown, big data technology is used to store, query, and process enterprise data. Through big data technology, massive enterprise energy consumption datasets are effectively stored and managed, and rapid query and analysis functions are provided. This allows the platform to quickly obtain and understand the dynamic energy consumption of the enterprise.

[0082] Next, this invention employs deep learning technology (such as deep neural network models) to construct a dynamic carbon profile prediction model (or prediction algorithm) for enterprises, analyzing the collected enterprise data. For example, a deep learning model (or deep neural network model) is trained using data sources to obtain the dynamic carbon profile prediction model. In constructing the dynamic carbon profile of an enterprise, a purely data-driven approach is used to dynamically analyze and learn time series data—that is, a deep learning method for time series prediction—to process the enterprise's static energy consumption data and dynamic operational data, extracting more comprehensive, reliable, and complete time-series correlation information. Using the dynamic carbon profile construction designed in this invention, a unified enterprise carbon profile representation can be generated. Based on these unified profile representations, managers can further apply data mining or machine learning techniques for deeper analysis, establishing a prediction algorithm for the enterprise's dynamic carbon profile of energy consumption, thereby more accurately predicting the energy consumption level when the enterprise's future operating conditions change.

[0083] This invention collects basic energy consumption data and operational data from enterprises daily, storing it in a time-series database to form a data source for big data learning and analysis. The enterprise dynamic carbon profile construction of this invention relies on IoT and database technologies to establish an enterprise carbon inventory system platform for collecting enterprise energy consumption data. By collecting basic operational data and dynamic energy consumption data, and utilizing big data technology for storage and analysis, managers can better understand and grasp the dynamic situation of enterprise energy consumption, providing strong support for energy conservation and emission reduction.

[0084] This invention also provides a data-based carbon profiling method for enterprises, which is described below.

[0085] like Figure 6 As shown, the enterprise carbon profiling method with data-provable capabilities of the present invention includes the following steps:

[0086] Execute step S11 to collect information data from the enterprise's energy consumption meter and simultaneously photograph the meter's dial to form image data; then execute step S12.

[0087] Execute step S12 to collect other energy consumption data related to enterprise production; then execute step S13.

[0088] Execute step S13 to convert the collected energy consumption meter data and other energy consumption data related to enterprise production into carbon emission data, and display the carbon emission data through the carbon meter; then execute step S14.

[0089] Execute step S14, add timestamp and address stamp to the collected energy consumption meter information data, and upload it to the blockchain platform for storage along with the corresponding image data; then execute step S15;

[0090] Perform step S15 to construct a dynamic carbon profiling prediction model based on deep learning technology; then perform step S16.

[0091] Execute step S16 to collect the company's operating data, input the company's operating data, energy consumption meter information data and other energy consumption data related to the company's production into the dynamic carbon profile prediction model, and give the company's carbon profile prediction results through the dynamic carbon profile prediction model.

[0092] In one specific embodiment of the present invention, when constructing a dynamic carbon profile prediction model, information data from the enterprise's energy consumption meter, other energy consumption data related to the enterprise's production, and the enterprise's operating data are collected daily and stored in the form of a time series to form a data source.

[0093] A deep learning model is trained using data sources to obtain a dynamic carbon profiling prediction model.

[0094] In one specific embodiment of the present invention, the enterprise carbon profiling method with data-provable evidence further includes:

[0095] Establish a regression model;

[0096] The collected energy consumption meter data is fitted and predicted using the established regression model to obtain the predicted basic energy consumption.

[0097] In one specific embodiment of the present invention, the enterprise carbon profiling method with data-provable evidence further includes:

[0098] Generate and distribute enterprise questionnaires based on the knowledge base;

[0099] Collect feedback from the questionnaires, conduct statistical analysis on the feedback, and then generate a carbon report for the company based on the statistical analysis results.

[0100] In one specific embodiment of the present invention, the enterprise carbon profiling method with data-provable evidence further includes:

[0101] The collected energy consumption meter data is compared with the corresponding image data. If the comparison results are inconsistent, the collected energy consumption meter data and the corresponding image data are deleted.

[0102] The present invention also provides a storage medium storing a program for a data-provable enterprise carbon profiling method, wherein the steps of the data-provable enterprise carbon profiling method are implemented when the program is executed by a processor.

[0103] The beneficial effects of the present invention's enterprise carbon profiling method, system, and storage medium with data-based evidence storage include:

[0104] 1) Enhanced data authenticity:

[0105] Cross-validation of data from multiple sources: Cross-validation using data from multiple sources can significantly reduce the risk of data errors and misreporting, and improve the authenticity and credibility of the data. This multi-source verification method is more reliable than a single data source and can reduce the impact of human factors on data quality.

[0106] Blockchain technology ensures data integrity: Blockchain technology is immutable; once data is recorded on the chain, it cannot be easily modified or deleted. This further ensures the authenticity and integrity of the data, preventing the possibility of data tampering during transmission and storage.

[0107] 2) Improved data utilization efficiency:

[0108] Real-time data monitoring: By cross-verifying and uploading multi-source data to the blockchain, real-time monitoring and updating of carbon emission data can be achieved.

[0109] Data sharing and collaboration: After multi-source data is cross-verified and uploaded to the blockchain, data sharing and collaboration between different sources can be achieved. This helps to break down data silos and improve the efficiency and value of data utilization.

[0110] 3) Develop more precise energy-saving and carbon-reduction strategies for enterprises through corporate carbon profiling:

[0111] The enterprise dynamic carbon profiling technology of this invention can more accurately realize the future energy consumption of enterprises, help enterprises formulate energy-saving and carbon-reduction strategies, cope with unexpected energy use situations, and better serve enterprises.

[0112] The main innovations of this invention regarding the enterprise carbon profiling method, system, and storage medium with data-based evidence storage include:

[0113] 1. The data of the smart carbon meter is encrypted, stored, and cannot be tampered with; it is also monitorable.

[0114] The data collected by the smart carbon meter undergoes multiple encryption and verification processes, is stored on the blockchain, and is also preserved using digital image encryption, making it accurate, reliable, and tamper-proof.

[0115] Carbon meters can visually display carbon emissions.

[0116] The energy consumption data collected and measured by the carbon meter system can be directly converted into carbon based on local methodologies and displayed on a physical device similar to an electricity meter.

[0117] Second, the carbon inventory system can automatically generate carbon inventory reports for enterprises.

[0118] By digitizing energy consumption data and fully digitizing the enterprise carbon inventory process, combined with big data models, verifiable enterprise carbon inventory reports that meet industry standards are automatically generated, saving a significant amount of manpower and achieving accuracy, reliability, and verifiability.

[0119] III. Energy consumption prediction model combining polynomial regression and linear regression.

[0120] This innovative method combines multinomial regression with linear regression to predict enterprise energy consumption trends. It overcomes the prediction error of linear models for multi-element changes and also takes into account the non-linear changes in energy consumption in the prediction model, making it more accurate and practical than the commonly used single linear regression model.

[0121] IV. Enterprise Dynamic Carbon Profile Prediction Algorithm.

[0122] Traditional energy consumption data collection and metering methods cannot accurately and timely depict the changes in a company's energy consumption over time and in its production and operations. A company's energy consumption during the production process varies over time, along with its operational status, development strategy, number of employees, and operating efficiency. This invention employs time-series deep learning technology to continuously collect energy consumption-related data from company operations. Based on different time periods, it generates dynamic energy consumption profiles for the company, predicts energy consumption trends, and generates a carbon profile, thereby more accurately predicting the future carbon emissions generated by the company and providing managers with energy conservation and emission reduction decision-making support.

[0123] The present invention has been described in detail above with reference to the accompanying drawings and embodiments. Those skilled in the art can make various modifications to the present invention based on the above description.

Claims

1. A method for creating a carbon profile of an enterprise with verifiable data, characterized in that, Includes the following steps: Collect information data from the enterprise's energy consumption meters, and simultaneously photograph the meter dials to generate image data; Collect other energy consumption data related to enterprise production; The collected energy consumption meter data and other energy consumption data related to enterprise production are converted into carbon emission data, and the carbon emission data is displayed through a carbon meter. The collected energy consumption meter data is added with timestamps and address stamps, and uploaded to the blockchain platform for storage along with the corresponding image data; A dynamic carbon profiling prediction model was built based on deep learning technology; The system collects the enterprise's operational data, inputs the enterprise's operational data, energy consumption meter information, and other energy consumption data related to the enterprise's production into the dynamic carbon profile prediction model, and provides the enterprise's carbon profile prediction results through the dynamic carbon profile prediction model; wherein the enterprise's operational data includes the enterprise's development stage data, organizational scale data, product market data, target customer data, operating status data, number of employees data, weather environment data, and energy-saving renovation data. This also includes: building a regression model; The collected energy consumption meter data is fitted and predicted using the established regression model to obtain the prediction results of basic energy consumption. The established regression model is an energy consumption prediction model that combines multinomial regression and linear regression. Trend analysis is performed on the collected energy consumption meter data to determine whether the line connecting the collected energy consumption meter data tends to be a straight line or a curve within a set period. If the result is a straight line, linear regression is used to fit the data to obtain the prediction result; if the result is a curve, multinomial regression is used to fit the data to obtain the prediction result. It also includes: generating and distributing enterprise questionnaires based on the knowledge base; the generated questionnaires are used to clarify the scope of the enterprise survey, determine the boundaries of the emission inventory, organizational boundaries and emission range, and calculate greenhouse gas emissions; Collect feedback from the questionnaires, conduct statistical analysis on the feedback, and then generate a carbon report for the company based on the statistical analysis results.

2. The enterprise carbon profiling method with data-based evidence storage as described in claim 1, characterized in that, When constructing a dynamic carbon profile prediction model, information data from the enterprise's energy consumption meter, other energy consumption data related to the enterprise's production, and the enterprise's operating data are collected daily. The collected data is stored in the form of a time series, thus forming a data source. The aforementioned data source is used to train a deep learning model, thereby obtaining a dynamic carbon profiling prediction model.

3. The enterprise carbon profiling method with data-based evidence storage as described in claim 1, characterized in that, Also includes: The collected energy consumption meter data is compared with the corresponding image data. If the comparison results are inconsistent, the collected energy consumption meter data and the corresponding image data are deleted.

4. A storage medium, characterized in that, The storage medium stores a program for a data-provable corporate carbon profiling method. When the program of the data-provable corporate carbon profiling method is executed by a processor, it implements the steps of the data-provable corporate carbon profiling method as described in any one of claims 1 to 3.

5. A corporate carbon profiling system with data storage capability, characterized in that, include: The data acquisition module is used to collect information data from the company's energy consumption meters, capture image data of the meter dials, and collect other energy consumption data related to the company's production. It is also used to collect enterprise operating data; enterprise operating data includes enterprise development stage data, organizational scale data, product market data, target customer data, operating status data, number of employees data, weather and environmental data, and energy-saving renovation data; A carbon conversion module is connected to the acquisition module. The carbon conversion module is used to convert the information data collected from the energy consumption meter and other energy consumption data related to enterprise production into carbon emission data. A carbon meter, connected to the carbon conversion module, is used to display the carbon emission data; The data upload module is connected to the acquisition module. The data upload module is used to add timestamps and address stamps to the information data of the collected energy consumption meter, and upload it together with the corresponding image data to the blockchain platform for storage. The dynamic carbon profiling prediction model is built based on deep learning technology. The processing module is connected to the acquisition module and the dynamic carbon profile prediction model. The processing module is used to input the enterprise's operating data, energy consumption meter information data and other energy consumption data related to the enterprise's production into the dynamic carbon profile prediction model, and to give the enterprise's carbon profile prediction result through the dynamic carbon profile prediction model. It also includes a regression model; the established regression model is an energy consumption prediction model that combines multinomial regression and linear regression. The processing module is connected to the regression model. The processing module is also used to fit and predict the collected energy consumption meter data using the established regression model to obtain a prediction result for basic energy consumption. The processing module is also used to perform trend analysis on the collected energy consumption meter data, determining whether the line connecting the collected energy consumption meter data tends to be a straight line or a curve within a set period. If the result is a straight line, linear regression is used to fit the data to obtain a prediction result; if the result is a curve, multinomial regression is used to fit the data to obtain a prediction result. It also includes a carbon inventory quality control module and an automatic form filling module; The carbon inventory quality control module is used to generate and distribute enterprise questionnaires based on the knowledge base, and also to perform statistical analysis on the feedback from the questionnaires. The generated questionnaires are used to clarify the scope of the enterprise survey, determine the boundaries of the emission inventory, organizational boundaries and emission range, and calculate greenhouse gas emissions. The automatic form filling module is connected to the carbon inventory quality control module, and the automatic form filling module is used to generate a carbon report for the enterprise based on the feedback statistical analysis results of the questionnaire.

6. The enterprise carbon profiling system with data storage capability as described in claim 5, characterized in that, It also includes a data filtering module connected to the acquisition module. The data filtering module is used to compare the information data of the acquired energy consumption meter with the corresponding image data. If the comparison results are inconsistent, the acquired information data of the energy consumption meter and the corresponding image data are deleted.

Images