Enterprise carbon footprint efficient tracking and metering system based on artificial intelligence

Abstract

The invention provides an enterprise carbon footprint efficient tracking and metering system based on artificial intelligence, and relates to the technical field of environmental protection, and the system comprises a data collection module which collects energy consumption, logistics transportation, waste treatment and office activity data in the enterprise operation in real time through an Internet of Things device and a sensor; the data transmission module is used for transmitting data to a cloud end by adopting an encrypted communication protocol and a low-power wide area network technology; the data storage module is used for hierarchically storing original data and aggregated data based on a distributed database and a data lake; the data processing module forms a unified data view through data cleaning, integration and quality control; the machine learning module is used for predicting a carbon emission trend and identifying an abnormal value by using a historical data training model; through automatic data acquisition and AI analysis, the accuracy of carbon footprint calculation is remarkably improved, the real-time performance and automation of carbon footprint tracking and metering are achieved, and the requirement for manual intervention is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of environmental protection technology, and more specifically, relates to an artificial intelligence-based system for efficiently tracking and measuring an enterprise's carbon footprint. Background Art

[0002] Tracking and measuring a company's carbon footprint has become crucial. Traditional carbon footprint calculation methods usually rely on manual data collection and manual calculations, which is not only time-consuming but also prone to errors.

[0003] Existing technologies mainly rely on manual data entry or partial automation tools, which have problems such as low efficiency, limited coverage, and lack of intelligent analysis. For example: 1. Traditional Excel spreadsheets or specialized software require manual data entry, which is prone to errors and difficult to cover complex supply chains.

[0004] 2. Some automation tools rely on hardware devices (such as smart meters), which are costly and have poor scalability.

[0005] 3. Existing systems lack AI-driven forecasting and strategy recommendation capabilities and are unable to dynamically optimize carbon management. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides an artificial intelligence-based system for efficiently tracking and measuring corporate carbon footprints to solve the above problems.

[0007] An AI-based system for efficiently tracking and measuring an enterprise's carbon footprint includes: a data acquisition module that uses IoT devices and sensors to collect real-time data on energy consumption, logistics, waste disposal, and office activities in enterprise operations; a data storage module that uses encrypted communication protocols and low-power wide area network technology to transmit data to the cloud; a data storage module that stores raw data and aggregated data in layers based on distributed databases and data lakes; a data processing module that forms a unified data view through data cleaning, integration, and quality control; a machine learning module that uses historical data to train models to predict carbon emission trends and identify outliers; a carbon footprint calculation module that dynamically calculates carbon emissions at the enterprise, department, and production link levels based on international standards; a report generation module that automatically generates visual carbon footprint reports that comply with ISO14064 standards; an optimization suggestion module that combines financial data to generate energy-saving and emission reduction strategies and cost-benefit analysis; and a user interface module that provides visual dashboards, interactive analysis tools, and multi-role permission management capabilities.

[0008] Preferably, the data acquisition module integrates a mobile application to support manual supplementary entry of non-automated data, including travel records, office supply consumption and carbon emission data of temporary activities. The machine learning module adopts a deep learning model (such as LSTM or Transformer) to dynamically optimize the carbon emission prediction accuracy and continuously update the model parameters through cross-validation and real-time feedback. The carbon footprint calculation module supports integration with the enterprise ERP system or production management system, and synchronizes operational data in real time to dynamically update the carbon footprint calculation results.

[0009] Preferably, the optimization suggestion module includes a supply chain carbon footprint analysis unit, which combines supplier carbon emission data to provide enterprises with supply chain optimization solutions. The user interface module supports multilingual report output and is embedded with a compliance check function to ensure that the report complies with the legal and regulatory requirements of the target area.

[0010] An AI-based method for efficiently tracking and measuring an enterprise's carbon footprint includes the following steps: real-time collection of multi-source carbon emission data through IoT devices and sensor networks; preliminary cleaning and compression of the data using edge computing nodes; transmission of the data to cloud storage and integration into a unified data view through an ETL process; use of trained machine learning models to predict carbon emission trends and identify energy conservation and emission reduction opportunities; dynamic calculation of the enterprise's carbon footprint at all levels based on the ISO14064 standard; automatic generation of visual reports, and push of customized emission reduction strategies to user terminals.

[0011] The training process of the machine learning model includes: Select the initial model architecture based on the company's industry characteristics; train the model using historical data and introduce transfer learning to optimize prediction accuracy in small sample scenarios; collect new data in real time to update model parameters and ensure the dynamic adaptability of prediction results.

[0012] An AI-based device for efficiently tracking and measuring a company's carbon footprint includes: a sensor network deployed in a company's production facilities, logistics vehicles, and office areas to monitor energy consumption, transportation mileage, and waste emissions; an edge computing gateway for data preprocessing and local caching; a cloud server cluster that performs data storage, machine learning analysis, and carbon footprint calculation; and a user terminal that provides a visual interface, real-time alarms, and report download capabilities.

[0013] The sensor network includes multimodal sensors that support the fusion collection of temperature and humidity, gas composition, vibration and energy consumption data, and improves data accuracy through an adaptive calibration algorithm.

[0014] Compared with the prior art, the present invention has the following beneficial effects: Improved accuracy: Through automated data collection and AI analysis, the accuracy of carbon footprint calculations has been significantly improved.

[0015] Cost reduction: It reduces the overall cost of carbon footprint tracking and measurement for enterprises. It reduces the manpower and material costs of carbon footprint tracking and measurement for enterprises, and reduces the additional expenses caused by data errors.

[0016] Enhanced compliance: Automatically generate reports that comply with international standards, helping companies meet regulatory requirements and avoid potential legal risks.

[0017] By providing energy-saving and emission-reduction strategy recommendations, we help companies develop more scientific and reasonable sustainable development strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the enterprise carbon footprint tracking system of the present invention; Figure 2 It is a flow chart of the data acquisition system of the present invention; Figure 3 It is a flow chart of the data processing system of the present invention; Figure 4 It is a flow chart of the report generating system of the present invention. DETAILED DESCRIPTION

[0019] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0020] See also Figures 1-4 The present invention provides an artificial intelligence-based system for efficiently tracking and measuring the carbon footprint of enterprises, including: a data acquisition module, which uses Internet of Things devices and sensors to collect real-time data on energy consumption, logistics transportation, waste disposal and office activities in enterprise operations; a data storage module, which uses encrypted communication protocols and low-power wide area network technology to transmit data to the cloud; a data storage module, which stores raw data and aggregated data in layers based on distributed databases and data lakes; a data processing module, which forms a unified data view through data cleaning, integration and quality control; a machine learning module, which uses historical data to train models to predict carbon emission trends and identify outliers; a carbon footprint calculation module, which dynamically calculates carbon emissions at the enterprise, department and production link levels based on international standards; a report generation module, which automatically generates a visual carbon footprint report that complies with the ISO14064 standard; an optimization suggestion module, which combines financial data to generate energy-saving and emission reduction strategies and cost-benefit analysis; and a user interface module, which provides a visual dashboard, interactive analysis tools and multi-role permission management functions.

[0021] The data collection module integrates a mobile application and supports manual entry of non-automated data, including travel records, office supply consumption, and carbon emissions data from temporary activities. The machine learning module uses deep learning models (such as LSTM or Transformer) to dynamically optimize carbon emissions prediction accuracy and continuously update model parameters through cross-validation and real-time feedback. The carbon footprint calculation module supports integration with the enterprise ERP system or production management system, synchronizing operational data in real time to dynamically update carbon footprint calculation results.

[0022] The optimization suggestion module includes a supply chain carbon footprint analysis unit, which combines supplier carbon emission data to provide companies with supply chain optimization solutions. The user interface module supports multilingual report output and embeds compliance checking functions to ensure that the report complies with the legal and regulatory requirements of the target region.

[0023] An AI-based method for efficiently tracking and measuring an enterprise's carbon footprint includes the following steps: real-time collection of multi-source carbon emission data through IoT devices and sensor networks; preliminary cleaning and compression of the data using edge computing nodes; transmission of the data to cloud storage and integration into a unified data view through an ETL process; use of trained machine learning models to predict carbon emission trends and identify energy conservation and emission reduction opportunities; dynamic calculation of the enterprise's carbon footprint at all levels based on the ISO14064 standard; automatic generation of visual reports, and push of customized emission reduction strategies to user terminals.

[0024] The training process of a machine learning model includes: Select the initial model architecture based on the company's industry characteristics; train the model using historical data and introduce transfer learning to optimize prediction accuracy in small sample scenarios; collect new data in real time to update model parameters and ensure the dynamic adaptability of prediction results.

[0025] An AI-based device for efficiently tracking and measuring a company's carbon footprint includes: a sensor network deployed in a company's production facilities, logistics vehicles, and office areas to monitor energy consumption, transportation mileage, and waste emissions; an edge computing gateway for data preprocessing and local caching; a cloud server cluster that performs data storage, machine learning analysis, and carbon footprint calculation; and a user terminal that provides a visual interface, real-time alarms, and report download capabilities.

[0026] The sensor network includes multimodal sensors that support the fusion collection of temperature and humidity, gas composition, vibration and energy consumption data, and improves data accuracy through adaptive calibration algorithms.

[0027] The present invention provides an efficient enterprise carbon footprint tracking and measurement system based on artificial intelligence to overcome the shortcomings of the existing technology and achieve real-time, accurate and comprehensive tracking and measurement of the enterprise carbon footprint, thereby helping enterprises better fulfill their social responsibilities and enhance their sustainable development capabilities.

[0028] Automated data collection: Automatically collect various carbon emission-related data during corporate operations through IoT technology and sensors.

[0029] Advanced Data Analytics: Use machine learning algorithms to clean, organize, and analyze collected data to identify outliers and trends.

[0030] Intelligent assessment: Based on the results of big data analysis, AI algorithms are used to calculate the company's carbon footprint and predict future carbon emission trends.

[0031] Automatic report generation: Automatically generate carbon footprint reports that meet international standards and support companies in disclosing information externally.

[0032] Strategic recommendations: Provide enterprises with energy-saving and emission reduction strategic recommendations based on carbon footprint analysis results.

[0033] Easy to expand: The system is designed with scalability in mind and can adapt to the needs of enterprises of different sizes.

[0034] User-friendly interface: Provides an intuitive and easy-to-use user interface to facilitate management personnel to operate and view data.

[0035] The following are the core components and their detailed descriptions: 1. Data acquisition module IoT technology integration: Real-time monitoring and data collection can be achieved through the deployment of IoT devices, such as smart meters, gas sensors, and temperature sensors. These devices can be installed in key locations across the enterprise, such as power generation facilities, heating / cooling systems, factory production lines, warehouses, and logistics centers.

[0036] Sensor network: Build a network of various types of sensors to monitor energy consumption (electricity, natural gas, fuel, etc.), logistics and transportation (mileage, load, transportation method, etc.), waste disposal (type, amount, disposal method, etc.), and energy usage in office areas.

[0037] Mobile application integration: Develop mobile applications that enable employees to easily record and upload non-automated data, such as travel activities and office supply consumption, to provide more comprehensive coverage of the company's carbon emission sources.

[0038] 2. Data transmission module Secure communication protocol: Use encrypted secure communication protocols (such as TLS / SSL) to protect information security during data transmission and prevent data tampering or leakage.

[0039] Low-power wide-area network technology: Utilizing LPWAN (Low-power wide-area network) technologies, such as LoRaWAN or NB-IoT, ensures that remote devices can stably transmit data under low-power conditions.

[0040] Edge computing nodes: Deploy edge computing nodes to reduce the pressure on central servers and perform preliminary processing before data reaches the central server.

[0041] 3. Data storage module Distributed database system: Use a distributed database management system (such as Cassandra or HBase) to support large-scale concurrent access and massive data storage requirements.

[0042] Data lake and data warehouse: Build a data lake to store raw data, and a data warehouse to store processed and aggregated data for subsequent analysis.

[0043] Backup and recovery mechanism: Implement regular backup strategies and have rapid recovery capabilities to ensure data integrity and high system availability.

[0044] 4. Data processing module Data cleaning: Use advanced data cleaning techniques, such as anomaly detection algorithms, missing value filling, and data normalization, to ensure data quality and consistency.

[0045] Data integration: Data from different sources are integrated through the ETL (Extract, Transform, Load) process to form a unified data view.

[0046] Data quality control: Establish a data quality control system, including data validation rules and audit processes, to ensure data accuracy.

[0047] 5. Machine Learning Module Model selection: Select appropriate machine learning models based on data characteristics and business needs, such as linear regression, decision tree, support vector machine, deep learning model, etc.

[0048] Model training: Use historical data to train models to identify carbon emission patterns, predict future carbon emission trends, and identify energy conservation and emission reduction opportunities.

[0049] Model evaluation and optimization: Evaluate model performance through cross-validation and other evaluation indicators (such as accuracy and recall), and continuously adjust and optimize the model based on feedback.

[0050] 6. Carbon footprint calculation module Compatible with international standards: Carbon footprint calculations are performed in accordance with international standards (such as the ISO 14064 series) to ensure the accuracy and comparability of the results.

[0051] Multi-dimensional calculation: It can not only calculate the overall carbon footprint of the enterprise, but also break it down to the department level or even specific production links, facilitating refined management and responsibility allocation.

[0052] Dynamic Update: Supports real-time update of carbon footprint data so that corporate management can promptly understand carbon emissions and take corresponding measures.

[0053] 7. Report generation module Automatic report generation: The system can automatically generate carbon footprint reports that meet international standards, including charts, key indicators and trend analysis.

[0054] Report customization: allows users to customize report templates as needed to meet different reporting needs and format requirements.

[0055] Compliance Check: Built-in compliance check function ensures that the report complies with relevant legal and regulatory requirements.

[0056] 8. Optimization suggestion module Energy conservation and emission reduction strategies: Based on the carbon footprint analysis results, provide suggestions for enterprises' energy conservation and emission reduction work, such as improving production processes, optimizing supply chain management, and adopting clean energy.

[0057] Cost-benefit analysis: Combined with financial data, this tool evaluates the cost-effectiveness of different emission reduction measures to help companies make the best decisions.

[0058] Performance tracking: Provides an ongoing tracking and evaluation mechanism to help companies monitor their progress towards their carbon reduction targets.

[0059] 9. User Interface Visual dashboard: Design intuitive dashboards that display key indicators and trend charts to help management quickly understand the carbon footprint status.

[0060] Interactive Analysis Tools: Provide interactive analysis tools that allow users to explore data and drill down to relevant information.

[0061] Multi-role support: Provides customized views and permission settings based on different user roles (such as senior managers, environment managers, IT administrators, etc.).

[0062] 10. System integration and scalability Interface design: The system design takes into account the integration with other business systems and provides a standardized API interface to facilitate data exchange and access to external applications.

[0063] Modular architecture: The microservice architecture makes the system easy to maintain and expand, and new functional modules can be flexibly added according to business needs.

[0064] 11. Security and Privacy Protection Data encryption: Use industry-standard data encryption technology to ensure the security of sensitive data.

[0065] Access control (continued): Ensures that only authorized users can access specific data and functions. This includes authentication, authorization, and audit logging to monitor who accessed what data and when.

[0066] Privacy protection: Comply with relevant laws and regulations, such as GDPR (General Data Protection Regulation) or other regional privacy protection regulations, to ensure that personal data is properly handled and will not be abused or leaked.

[0067] Data Lifecycle Management: Implement a data lifecycle management strategy to ensure that data is protected by appropriate security measures from creation to destruction.

[0068] 12. System operation, maintenance and support Monitoring and Alarm: Establish a system monitoring mechanism to continuously monitor key components and services, and automatically send alarms to notify relevant personnel when anomalies occur.

[0069] Troubleshooting: Develop detailed troubleshooting guides and emergency response plans to quickly resolve system failures and minimize downtime and impact.

[0070] Technical Support: We provide 24 / 7 technical support services to resolve problems and questions encountered by users, and perform regular system maintenance and upgrades.

[0071] 13.Continuous improvement and update Iterative development: Adopt agile development methods, continuously iterate system functions, and regularly release new versions based on user feedback and technological progress.

[0072] Technology Update: As new technologies develop, we continuously evaluate and introduce new solutions to keep the system advanced and competitive.

[0073] Training and Education: Regular training and support are provided to users to help them better understand and use the system, while also raising awareness of carbon footprint management within the organization.

[0074] 14. Social Responsibility and Sustainable Development Social responsibility: Demonstrate the company's carbon footprint management results in an open and transparent manner to enhance public trust and social responsibility.

[0075] Partnerships: Collaborate with suppliers, customers, and other stakeholders to drive carbon reduction actions in the supply chain.

[0076] Community Engagement: Encourage employees and community members to participate in environmental protection activities and promote a culture and practice of sustainable development.

[0077] In summary, this AI-based efficient carbon footprint tracking and measurement system for enterprises can not only provide enterprises with comprehensive carbon footprint management solutions, but also help enterprises fulfill their social responsibilities and achieve sustainable development goals.

[0078] Example: Imagine a large manufacturing company looking to improve its carbon footprint tracking and measurement system. The company has factories across multiple locations, involved in a complex supply chain and logistics network. Implementing the system of the present invention could help the company achieve more efficient carbon footprint management.

[0079] 1. System deployment: Data collection: Smart meters and sensors are installed in each factory to collect data on electricity consumption, gas usage, wastewater discharge, etc. At the same time, GPS trackers and fuel consumption monitors are installed on logistics vehicles.

[0080] Data transmission: Data is transmitted to the cloud server via a secure wireless network.

[0081] Data processing and storage: The data processing module on the cloud server cleans and integrates the data, and then stores it in a high-performance database.

[0082] Machine Learning Modeling: Use historical data to train machine learning models to identify carbon emission patterns and predict future trends.

[0083] Carbon footprint calculation: Based on the ISO 14064 standard and machine learning models, the carbon footprint of each factory and the entire company is calculated.

[0084] Report generation: The system automatically generates a detailed carbon footprint report and sends it to relevant personnel via email.

[0085] Energy conservation and emission reduction recommendations: Based on the carbon footprint analysis results, the system provides enterprises with specific measures and recommendations for energy conservation and emission reduction.

[0086] 2. System advantages: Real-time monitoring: The system can monitor energy consumption and carbon emissions in real time, identify problems promptly and take action.

[0087] Decision support: Through in-depth analysis of data, the system can provide management with data-driven decision-making basis.

[0088] Compliance assurance: Automatically generated reports ensure data transparency and compliance, helping companies cope with government scrutiny and social oversight.

[0089] 3. User experience: User Interface: The system provides a user-friendly interface that enables non-technical personnel to easily access and understand carbon footprint data.

[0090] Mobile App: A mobile app has also been developed to enable managers to view key metrics and receive alert notifications from anywhere.

[0091] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

Claims

1. An AI-based system for efficiently tracking and measuring corporate carbon footprints, characterized by: include: The data collection module collects real-time data on energy consumption, logistics and transportation, waste disposal, and office activities in enterprise operations through IoT devices and sensors; The data transmission module uses encrypted communication protocols and low-power wide area network technology to transmit data to the cloud; Data storage module, which stores raw data and aggregated data in layers based on distributed databases and data lakes; Data processing module, which forms a unified data view through data cleaning, integration and quality control; A machine learning module that uses historical data to train models to predict carbon emission trends and identify outliers; Carbon footprint calculation module, which dynamically calculates carbon emissions at the enterprise, department and production level based on international standards; Report generation module, automatically generates visual carbon footprint reports that comply with ISO14064 standards; Optimization suggestion module, which combines financial data to generate energy conservation and emission reduction strategies and cost-benefit analysis; The user interface module provides visual dashboards, interactive analysis tools, and multi-role permission management capabilities.

2. The system for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence as claimed in claim 1, characterized in that: The data collection module is integrated with a mobile application and supports manual entry of non-automated data, including travel records, office supplies consumption and carbon emission data of temporary activities.

3. The system for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence as claimed in claim 1, characterized in that: The machine learning module uses a deep learning model (such as LSTM or Transformer) to dynamically optimize the accuracy of carbon emission predictions and continuously update model parameters through cross-validation and real-time feedback.

4. The system for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence as claimed in claim 1, characterized in that: The carbon footprint calculation module supports integration with the enterprise ERP system or production management system, and synchronizes operational data in real time to dynamically update the carbon footprint calculation results.

5. The system for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence as claimed in claim 1, characterized in that: The optimization suggestion module includes a supply chain carbon footprint analysis unit, which combines supplier carbon emission data to provide enterprises with supply chain optimization solutions.

6. The system for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence as claimed in claim 1, characterized in that: The user interface module supports multi-language report output and is embedded with compliance checking functions to ensure that the report complies with the legal and regulatory requirements of the target region.

7. A method for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence, characterized in that: The following steps are involved: Collect carbon emission data from multiple sources in real time through IoT devices and sensor networks; Use edge computing nodes to perform preliminary cleaning and compression of data; Transfer data to cloud storage and integrate it into a unified data view through ETL processes; Use trained machine learning models to predict carbon emission trends and identify energy conservation and emission reduction opportunities; Dynamically calculate the carbon footprint of each level of the enterprise based on the ISO14064 standard; Automatically generate visual reports and push customized emission reduction strategies to user terminals.

8. The method for efficiently tracking and measuring an enterprise's carbon footprint based on artificial intelligence as claimed in claim 7, characterized in that: The training process of the machine learning model includes: Select the initial model architecture based on the company's industry characteristics; The model is trained using historical data and transfer learning is introduced to optimize prediction accuracy in small sample scenarios. New data is collected in real time to update model parameters to ensure the dynamic adaptability of prediction results.

9. An artificial intelligence-based device for efficiently tracking and measuring corporate carbon footprints, characterized in that: include: Sensor networks are deployed in enterprise production facilities, logistics vehicles, and office areas to monitor energy consumption, transportation mileage, and waste emissions; Edge computing gateway, used for data preprocessing and local caching; A cloud server cluster that performs data storage, machine learning analysis, and carbon footprint calculations; User terminal provides visual interface, real-time alarm and report download functions.

10. The device for efficiently tracking and measuring corporate carbon footprint based on artificial intelligence as claimed in claim 9, characterized in that: The sensor network includes multimodal sensors that support the fusion collection of temperature and humidity, gas composition, vibration and energy consumption data, and improves data accuracy through an adaptive calibration algorithm.

Images