Pollution and carbon reduction platform

Abstract

The invention belongs to the technical field of pollution reduction and carbon reduction management, and particularly relates to a pollution reduction and carbon reduction platform which comprises a data acquisition module, a data analysis and prediction module, a data transmission optimization module, a business display module, three major application modules, a heterogeneous data fusion and security sharing module, a management decision support module, a public service module and a transformation and promotion module. According to the method, a mixed mode of edge calculation, double-queue synchronization and opportunistic synchronization is introduced, the idea of a time delay tolerant network is innovatively combined, accurate synchronization and transmission solutions for different real-time requirements are provided, and data guarantee is provided for efficient response and accurate decision of a park management system.

Description

Technical Field

[0001] The present invention relates to the technical field of motors, and specifically to a pollution reduction and carbon emission reduction platform. Background Art

[0002] With the proposal of the country's "dual carbon" goal and the improvement of ecological environment management requirements, industrial parks have become key areas for coordinated management of pollution reduction and carbon emission reduction. Enterprises are concentrated in the park, and the total amount of energy consumption and pollution emissions is large, with diverse types and complex sources. Traditional management methods can no longer meet the needs of modern and digital management. Therefore, it is necessary to effectively monitor and manage the park through a pollution reduction and carbon emission reduction platform; However, the existing monitoring and management platforms still have the following technical problems when in use: With the extensive deployment of intelligent monitoring devices in the park, the multi-source heterogeneous data collected in real time (such as energy consumption, carbon emissions, and pollution monitoring data) shows characteristics such as large data volume, diverse formats, and high transmission frequencies, resulting in serious challenges to network bandwidth and transmission efficiency. Problems such as clock deviation, link quality fluctuations, and network delays of different devices further exacerbate the difficulty of data synchronization, affecting the system's real-time management and decision-making response capabilities; The data types collected by sensors and devices in park management are diverse, including high-dimensional time series data, relational attribute data, etc. There are obvious heterogeneities in the sampling frequency, accuracy, and structure of different data, which makes it difficult to directly fuse the data during unified analysis and management, affecting the accuracy and timeliness of intelligent decision-making. At the same time, park data (such as enterprise energy consumption and emission data) involves the core privacy of enterprises, and sufficient security and privacy protection need to be ensured during data sharing and analysis; In the pollution reduction and carbon emission reduction management of industrial parks, the attribution of pollution sources is complex and changeable. Traditional methods mainly rely on single-modal data analysis and are difficult to effectively integrate unstructured data such as policy texts and enterprise reports, resulting in limited accuracy of carbon emission prediction, lagging decision-making, and lack of generalization ability. Large language models have the ability of cross-modal data fusion and intelligent reasoning, can achieve multi-factor attribution analysis, and can model the long-term policy impact to optimize the carbon emission path.

[0003] Therefore, a pollution reduction and carbon emission reduction platform is proposed to solve the above-mentioned problems; This application focuses on three key technologies of "data synchronization and transmission optimization based on edge computing", "security management mechanism for data fusion and sharing", and "precision decision-making support based on intelligent models" for the intelligent control and decision-making of pollution reduction and carbon emission reduction in industrial parks, respectively solving the three core challenges of data transmission, privacy security, and decision-making optimization, and conducting in-depth research from aspects such as data transmission optimization, heterogeneous data fusion and secure sharing, and intelligent decision-making support; This application solves the core problems in the collaborative management of pollution reduction and carbon emission reduction in industrial parks by introducing intelligent technologies such as advanced edge computing, cloud-edge-end collaboration technology, large language models (LLMs), and deep learning algorithms. The goal of this application is to design and implement an intelligent management and control platform for pollution reduction and carbon emission reduction in industrial parks based on data fusion drive, promote efficient data synchronization and fusion, ensure data privacy, and enhance the reliability of data sharing, provide the accuracy, timeliness, and interpretability of intelligent decision-making for pollution reduction and carbon emission reduction in industrial parks, and ultimately achieve the energy use efficiency of the park, reduce pollution emissions, optimize the decision-making process, and promote the transformation of the park towards green, low-carbon, and efficient management. Summary of the Invention

[0004] The purpose of the present invention is to provide a pollution reduction and carbon emission reduction platform to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A pollution reduction and carbon emission reduction platform includes a data collection module, a data analysis and prediction module, a data transmission optimization module, a business display module, three major application modules, a heterogeneous data fusion and secure sharing module, a management decision support module, a public service module, and a transformation and improvement module; The data collection module is used to collect atmospheric pollutants, emission gases, and water quality in the park; The data analysis and prediction module is used to estimate the carbon footprint of the park and predict future emission trends; The data transmission optimization module, by introducing edge computing, the storage and processing capabilities of edge nodes, through regional division and collaborative communication of cluster head nodes, realizes efficient synchronization and transmission between nodes. For the different real-time requirements of different detection services, a differential service scheme is designed using a hybrid method of dual-queue synchronization and opportunistic synchronization. Using the "store-carry-forward" strategy, it reduces network overhead and ensures that the data synchronization success rate of relay nodes is improved in the case of unstable networks, providing more accurate decision support for the intelligent management platform of the park; The business display module is used to provide a visual interface to display the progress of carbon peak work, key indicator tasks, and current status in the region, achieving an overview in one graph, facilitating managers to quickly understand the overall situation; The three major application modules include carbon monitoring, carbon accounts, and carbon portraits, accurately monitoring carbon emissions from different sources in the park, establishing carbon emission accounts for each enterprise or project, recording their emissions and emission reduction efforts, analyzing the energy consumption of enterprises in detail, and proposing targeted improvement measures; The heterogeneous data fusion and security sharing module is used to establish a relational-temporal fusion database by regarding the dimensional relationship of time-series data as a tree structure. Combining the powerful processing capabilities of cloud computing and adopting deep learning technology, it uses a CNN-RNN multi-layer network structure to achieve the deep fusion and association of relational data and time-series data. In terms of data sharing and privacy protection, it introduces blockchain technology and practical Byzantine fault tolerance algorithms to establish an efficient and reliable data sharing consensus mechanism, realizing efficient and reliable data sharing and fusion while ensuring data privacy, providing stronger data support for intelligent park management; The management decision support module utilizes the cross-modal data processing ability of the LLM to analyze the causal relationships among pollution emissions, production, meteorology, and energy consumption, improving the accuracy of attribution analysis. It uses an optimized LSTM for short-term emission prediction and simulates the impact of policy adjustments on long-term emissions through causal reasoning to dynamically optimize the carbon emission path. It introduces deep reinforcement learning to optimize strategies, combines mixed-integer linear programming to ensure economy and feasibility, and uses natural language generation technology to improve decision-making efficiency and response capabilities. It will build a modular management system to support the refined management of energy, carbon emissions, and environmental protection, promote the coordinated management of pollution reduction and carbon reduction, and combine means such as information push, SMS, and voice broadcast. It will also build a digital large-screen display system to display key pollution discharge and high-energy consumption links in the park, and track the progress and development status of pollution reduction and carbon reduction targets in real time; The public service module is used to encourage the park to actively announce the main practices and typical cases of pollution reduction and carbon reduction, accept social supervision, and stimulate enterprises to actively reduce pollution and carbon emissions through policy linkages such as carbon credits, energy use rights trading, and co-creation vouchers; The transformation and improvement module is used to promote the development of the park's infrastructure in a more environmentally friendly and efficient direction.

[0006] Preferably, the data acquisition module includes: environmental monitoring equipment, greenhouse gas monitoring equipment, water quality monitoring equipment, energy consumption monitoring equipment, satellite remote sensing technology, drones, and mobile monitoring equipment; The environmental monitoring equipment includes a continuous emission monitoring system and an air quality monitoring station. The continuous emission monitoring system is used to monitor the pollutant concentration and emissions in industrial waste gas in real time, such as sulfur dioxide, nitrogen oxides, and particulate matter; the air quality monitoring station is used to measure a series of parameters in the atmosphere, including but not limited to PM2.5, PM10, ozone, and carbon monoxide; The greenhouse gas monitoring equipment includes a carbon dioxide monitor and greenhouse gas monitoring equipment; the carbon dioxide monitor is used to measure the amount of carbon dioxide emitted in the park or by specific enterprises, and the greenhouse gas monitoring equipment is used to accurately measure greenhouse gases; The water quality monitoring equipment monitors indicators such as chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, and total phosphorus in water bodies to evaluate the water quality status; The energy consumption monitoring device records the electricity, water resources, and gas usage of enterprises, helping to analyze energy efficiency and potential energy-saving space; The satellite remote sensing technology provides large-scale environmental monitoring data, such as changes in vegetation coverage, surface temperature, and atmospheric composition, which helps to macroscopically understand the environmental status of the park and its surrounding areas; The mobile monitoring device is used for precise monitoring in inaccessible places, supplementing the deficiencies of fixed monitoring points, facilitating flexible deployment at different locations within the park, and quickly obtaining pollution information of local areas.

[0007] Preferably, the data analysis and prediction module includes a data preprocessing and cleaning model, a carbon emission calculation model, a carbon emission prediction model, and a key indicator monitoring model; The data preprocessing and cleaning model integrates data from different sources, ensures consistent data formats, removes outliers, fills in missing values, and ensures the integrity and accuracy of the data; The carbon emission calculation model calculates the carbon footprint of each enterprise by collecting the energy consumption data of enterprises in the park and combining the emission factors of specific industries, accurately identifying the main carbon emission sources and their contribution ratios, and helping managers to take targeted emission reduction measures; The carbon emission prediction model uses historical data and adopts time series models such as ARIMA and LSTM to predict future carbon emission trends, simulates the impact of different policies or technical improvement plans on carbon emissions, and evaluates the emission reduction effects under various scenarios; The key indicator monitoring model sets and monitors a series of key indicators, such as energy consumption per unit output value and carbon intensity, evaluates the overall energy conservation and emission reduction effectiveness of the park, and when certain indicators exceed the preset thresholds, the system automatically issues an alarm to remind managers to take corresponding measures.

[0008] Preferably, the data transmission optimization module includes a multi-source heterogeneous data synchronization technology model based on edge computing and a low-latency and high-efficiency data synchronization transmission optimization technology model; The multi-source heterogeneous data synchronization technology model based on edge computing divides nodes into regions by analyzing the hierarchical and attribution logical relationships between nodes. The cluster head node realizes inter-region communication to complete one-to-many synchronization, and the nodes within the region perform parallel synchronization to achieve synchronous transmission of nodes. To solve the clock differences of different sensor nodes, a hybrid synchronization mechanism of network time protocol and precision time protocol is designed to provide microsecond-level time synchronization accuracy, ensuring the time synchronization and data consistency of energy consumption monitoring and carbon emission data, and providing a data basis for the real-time response ability and decision-making accuracy of the intelligent pollution reduction and carbon emission reduction control platform for industrial parks; The low-latency and high-efficiency data synchronization and transmission optimization technology model differentiates the real-time requirements of services in data synchronization, adopts a dual-queue synchronization design, sends high-real-time and low-real-time service data in different queues, realizes differential services for different real-time services, and uses the link bandwidth more reasonably. In case of data synchronization failure caused by network link quality deterioration or interruption, the idea of the delay-tolerant network architecture is adopted to improve the synchronization success rate, and an opportunistic synchronization method of "store-carry-forward" is proposed for the data synchronization service in the low-real-time queue, and appropriate relay nodes are selected to improve the data synchronization efficiency; in addition, by adopting an adaptive routing algorithm based on load and network status, real-time monitoring of the load of network nodes and link quality is realized, and the optimal transmission path is dynamically selected to reduce network overhead and improve the data transmission efficiency.

[0009] Preferably, the service display module includes a business one-map model, a carbon emission visualization model, and a performance evaluation and tracking model; The business one-map model displays the key indicators and current status of the entire park through a comprehensive chart, such as the total carbon emission, pollutant concentration, and energy consumption distribution, integrates the real-time data update function, and dynamically displays the latest environmental monitoring data and energy consumption situation; The carbon emission visualization model uses the geographic information system to mark the positions of each monitoring point and its corresponding carbon emission data on the map, provides an intuitive display in space, represents the carbon emission intensity of different regions by the depth of color, facilitates the discovery of high-emission regions, and draws a trend chart of carbon emission changes over time to help identify peak emission periods and long-term change trends; The performance evaluation and tracking model displays key performance indicators, such as energy consumption per unit output value and carbon intensity, and clearly shows the comparison between the actual value and the target value of each indicator in the form of a dashboard.

[0010] Preferably, the heterogeneous data fusion and security sharing module includes a multi-source heterogeneous data fusion technology model based on a relational-temporal database and a data sharing and privacy protection technology model based on blockchain; The multi-source heterogeneous data fusion technology model based on a relational-temporal database regards the dimensional relationship of temporal data as a tree structure. Using high-dimensional temporal tuples as the root nodes, which point to other tuples to form non-leaf nodes, thus realizing data transmission and organization. The data in the relational database is regularly uploaded to the cloud data warehouse according to business requirements and kept consistent with the temporal database through fields such as device ID. The preliminarily integrated data will be transmitted to the cloud, where feature extraction is performed. The extracted features will jointly form a feature vector set with the attribute features in the relational data for subsequent modeling and analysis. To further improve the data fusion and analysis effect, the cloud will combine deep learning technologies, use convolutional neural networks to extract the structured features in the relational data, and at the same time process the sequential features in the temporal data through recurrent neural networks. By constructing a multi-layer network structure of CNN-RNN, deep fusion and association of multi-source heterogeneous relational data and temporal data are achieved, providing more comprehensive feature information for decision-making. The data sharing and privacy protection technology model based on blockchain uses the Practical Byzantine Fault Tolerance algorithm to establish an efficient and reliable consensus mechanism for data sharing, combines blockchain technology with cosine similarity analysis to ensure consistency and security in the data sharing process. By embedding the Neighborhood Consensus-Frequency Modulation Variable Scale Node Classification algorithm and using cosine similarity analysis, nodes are effectively distinguished and processed to ensure the optimization of the data integration process through classification and frequency adjustment. By constructing a vector space model, this algorithm converts complex multi-dimensional data summaries into concise vectors for representation, thus simplifying the data processing flow and enhancing the comparability between data.

[0011] Preferably, the management decision support module includes an intelligent analysis and prediction model for park carbon emissions and pollutant data based on the AI large language model and a collaborative optimization technology model for pollution reduction and carbon emission reduction based on intelligent decision support. The intelligent analysis and prediction model for park carbon emissions and pollutant data based on the AI large language model combines the cross-modal data processing ability of the LLM to construct an intelligent analysis and prediction framework. For emission pattern mining, a BERT model with time window attention is used to extract the key environmental factors of the data, and the relationship between emission data and production, meteorology, and energy consumption is analyzed by combining with a knowledge graph to establish a pollution causal relationship network, and knowledge distillation technology is used for attribution analysis. For emission trend prediction, an intelligent prediction system of "short-term dynamic prediction + long-term policy-driven simulation" is constructed. The LSTM optimized by Bayesian is used to predict the short-term emission change trend, and at the same time, a GAN based on causal reasoning is used to model the impact of policies and production adjustments on long-term emissions, and the carbon emission path is dynamically adjusted through multi-objective optimization, providing accurate, efficient, and interpretable governance decisions for the park. The collaborative optimization technology model for pollution reduction and carbon emission reduction based on intelligent decision support combines the cross-modal data processing capabilities of the LLM to construct an AI-based intelligent decision support framework. It uses deep reinforcement learning to optimize pollution reduction and carbon emission reduction strategies, and combines mixed-integer linear programming to ensure the economy and feasibility of the optimization plan. At the same time, it uses natural language generation technology to transform complex decision-making processes into intuitive operation suggestions, enhancing the manager's response ability to sudden pollution events and long-term carbon emission reduction strategies. Through LLM-driven intelligent reasoning and adaptive optimization, it improves the collaborative management level of pollution reduction and carbon emission reduction in the park, providing data support and decision support for the construction of green and low-carbon parks.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) Transmission optimization technology based on intelligent data synchronization: This application introduces a hybrid method of edge computing, dual-queue synchronization, and opportunistic synchronization, innovatively combines the idea of delay-tolerant networks, and provides precise synchronization and transmission solutions for different real-time requirements, providing data guarantee for the efficient response and accurate decision-making of the park management system; (2) Data fusion security technology based on cloud-edge-end collaboration: This application proposes a multi-source heterogeneous data fusion technology based on relational-temporal databases, and combines deep learning technologies (CNN and RNN) for data feature extraction and fusion. At the same time, by introducing blockchain technology and Byzantine fault-tolerant algorithms, it provides a reliable and secure consensus mechanism for data sharing; (3) Collaborative management and control platform for pollution reduction and carbon emission reduction in the park based on intelligent decision-making: By integrating large language models into the management process of intelligent analysis and decision-making for pollution control, it solves problems such as scattered data, complex attribution, and insufficient prediction accuracy. It can not only achieve short-term prediction and long-term policy simulation, improve the prediction accuracy of emission trends, but also allow operators to obtain detailed information and suggestions for pollution control through natural language queries, realizing dynamic supervision and real-time decision-making for pollution reduction and carbon emission reduction in the park. Description of the Drawings

[0013] Figure 1 It is a module composition diagram of the pollution reduction and carbon emission reduction platform; Figure 2 It is a composition diagram of the data acquisition module; Figure 3 It is a composition diagram of the data analysis and prediction module; Figure 4 It is a composition diagram of the data transmission optimization module; Figure 5 It is a composition diagram of the business display module; Figure 6 It is a composition diagram of the heterogeneous data fusion and secure sharing module; Figure 7 It is a composition diagram of the management decision support module. Detailed implementation manners

[0014] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0015] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention.

[0016] Embodiment: Please refer to Figure 1-7 , the present invention provides a technical solution: A pollution reduction and carbon emission reduction platform, including a data acquisition module, a data analysis and prediction module, a data transmission optimization module, a business display module, three major application modules, a heterogeneous data fusion and secure sharing module, a management decision support module, a public service module, and a transformation and improvement module; The data acquisition module is used to collect atmospheric pollutants, emission gases, and water quality in the park; The data analysis and prediction module is used to estimate the carbon footprint of the park and predict future emission trends; The data transmission optimization module realizes efficient synchronization and transmission between nodes by introducing edge computing, the storage and processing capabilities of edge nodes, and through regional division and collaborative communication of cluster head nodes. For the different real-time requirements of different detection services, a differential service scheme is designed using a hybrid method of dual-queue synchronization and opportunistic synchronization. Using the "store-carry-forward" strategy, it reduces network overhead and ensures that the data synchronization success rate of relay nodes is improved in the case of unstable networks, providing more accurate decision support for the intelligent management platform of the park; The business display module is used to provide a visual interface to display the progress of carbon peak work, key indicator tasks, and current status in the area, achieving an overview in one graph, facilitating managers to quickly understand the overall situation; The three major application modules include carbon monitoring, carbon accounts, and carbon portraits, accurately monitoring carbon emissions from different sources in the park, establishing carbon emission accounts for each enterprise or project, recording their emissions and emission reduction efforts, analyzing the energy consumption of enterprises in detail, and proposing targeted improvement measures; The heterogeneous data fusion and security sharing module is used to establish a relational-temporal fusion database by treating the dimensional relationship of time-series data as a tree structure. Combining the powerful processing capabilities of cloud computing and adopting deep learning techniques, a CNN-RNN multi-layer network structure is used to achieve the deep fusion and association of relational data and time-series data. In terms of data sharing and privacy protection, blockchain technology and practical Byzantine fault tolerance algorithms are introduced to establish an efficient and reliable data sharing consensus mechanism, realizing efficient and reliable data sharing and fusion while ensuring data privacy, providing stronger data support for park intelligent management; The management decision support module utilizes the cross-modal data processing ability of the LLM to analyze the causal relationships among pollution emissions, production, meteorology, and energy consumption, improving the accuracy of attribution analysis. An optimized LSTM is used for short-term emission prediction, and causal reasoning is used to simulate the impact of policy adjustments on long-term emissions, realizing the dynamic optimization of the carbon emission path. Deep reinforcement learning optimization strategies are introduced, combined with mixed-integer linear programming to ensure economy and feasibility. Using natural language generation technology to improve decision-making efficiency and response capabilities, a modular management system will be built to support the refined management of energy, carbon emissions, and environmental protection, promoting the coordinated management of pollution reduction and carbon emission reduction. Combining means such as information push, SMS, and voice broadcast, a digital large-screen display system will also be built to display key pollution discharge and high-energy consumption links in the park, and to track the progress and development status of pollution reduction and carbon emission reduction targets in real time; The public service module is used to encourage the park to actively announce the main practices and typical cases of pollution reduction and carbon emission reduction, accept social supervision, and stimulate enterprises to actively reduce pollution and carbon emissions through policy linkages such as carbon credits, energy use rights trading, and co-creation vouchers; The transformation and improvement module is used to promote the development of the park's infrastructure towards a more environmentally friendly and efficient direction.

[0017] This application has good feasibility in three main technical fields. First, in the transmission optimization technology based on intelligent data synchronization, the application of edge computing enables multi-source heterogeneous data in the park to be stored and processed locally, effectively reducing network transmission latency and improving data synchronization accuracy. This technology has been verified in other fields and has a strong practical foundation. Second, the data fusion security technology based on cloud-edge-end collaboration can ensure the secure sharing and efficient fusion of different types of data in the park by combining deep learning and blockchain technology. Deep learning and blockchain technology have been widely used in industrial data processing and privacy protection, and existing platforms provide sufficient computing power and security guarantees. In addition, the park pollution reduction and carbon emission reduction collaborative control platform based on intelligent decision-making can provide accurate and real-time decision support for park pollution reduction and carbon emission reduction by introducing large language models and reinforcement learning technology, combined with traditional optimization algorithms. These technologies have been successfully applied in other fields and can meet the park's needs, ensuring the feasibility of system implementation; The technical solutions adopted in this application demonstrate remarkable innovation and advancement. Firstly, the transmission optimization technology based on intelligent data synchronization, by combining edge computing with the Delay Tolerant Network (DTN) architecture, innovatively solves the problems of low latency and network fluctuations in large-scale heterogeneous data synchronization and transmission. Through the dual-queue synchronization and the "store-carry-forward" strategy, this application provides a brand-new optimization solution for real-time data synchronization, improving the synchronization accuracy and data transmission efficiency. Secondly, in terms of data fusion security technology, this application combines time-series data with relational data and conducts in-depth feature extraction through deep learning (CNN and RNN), breaking through the limitations of traditional data fusion. In addition, the combination of blockchain technology and the Byzantine fault tolerance algorithm innovatively provides a solution that can not only ensure data security but also achieve efficient sharing, with strong forward-looking. Finally, the park pollution reduction and carbon emission reduction collaborative management and control platform based on intelligent decision-making introduces advanced technologies such as large language models (LLMs) and causal inference GANs. It can not only process multi-source heterogeneous data but also extract useful information from unstructured texts, providing accurate and interpretable emission reduction decision support for the park through intelligent reasoning and optimization strategies. The application of this method in cross-modal data analysis and multi-factor optimization decision-making has high innovation and practical significance.

[0018] The data acquisition module includes: environmental monitoring equipment, greenhouse gas monitoring equipment, water quality monitoring equipment, energy consumption monitoring equipment, satellite remote sensing technology, drones, and mobile monitoring equipment; The environmental monitoring equipment includes a continuous emission monitoring system and an air quality monitoring station. The continuous emission monitoring system is used to monitor in real time the pollutant concentration and emissions in industrial waste gases, such as sulfur dioxide, nitrogen oxides, and particulate matter; the air quality monitoring station is used to measure a series of parameters in the atmosphere, including but not limited to PM2.5, PM10, ozone, and carbon monoxide; The greenhouse gas monitoring equipment includes a carbon dioxide monitor and greenhouse gas monitoring equipment; the carbon dioxide monitor is used to measure the amount of carbon dioxide emitted within the park or by specific enterprises, and the greenhouse gas monitoring equipment is used to accurately measure greenhouse gases; The water quality monitoring equipment monitors indicators such as chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, and total phosphorus in water bodies to evaluate the water quality status; The energy consumption monitoring equipment records the usage of electricity, water resources, and gas by enterprises to help analyze energy efficiency and potential energy-saving space; The satellite remote sensing technology provides large-scale environmental monitoring data, such as changes in vegetation coverage, surface temperature, and atmospheric composition, which helps to macroscopically understand the environmental status of the park and its surrounding areas; The mobile monitoring device is used for precise monitoring in inaccessible areas, compensating for the deficiencies of fixed monitoring points, facilitating flexible deployment at different locations within the park, and quickly obtaining pollution information of local areas.

[0019] The data analysis and prediction module includes a data preprocessing and cleaning model, a carbon emission calculation model, a carbon emission prediction model, and a key indicator monitoring model; The data preprocessing and cleaning model integrates data from different sources, ensures consistent data formats, removes outliers, fills in missing values, and ensures the integrity and accuracy of the data; The carbon emission calculation model calculates the carbon footprint of each enterprise in the park by collecting energy consumption data of each enterprise and combining emission factors specific to the industry, accurately identifies the main carbon emission sources and their contribution ratios, and helps managers take targeted emission reduction measures; The carbon emission prediction model uses historical data and adopts time series models such as ARIMA and LSTM to predict future carbon emission trends, simulates the impact on carbon emissions according to different policy or technology improvement scenarios, and evaluates the emission reduction effects under various scenarios; The key indicator monitoring model sets and monitors a series of key indicators, such as energy consumption per unit output value and carbon intensity, evaluates the overall energy conservation and emission reduction effectiveness of the park, and when certain indicators exceed the preset thresholds, the system automatically issues an alarm to remind managers to take corresponding measures.

[0020] The data transmission optimization module includes a multi-source heterogeneous data synchronization technology model based on edge computing and a low-latency and high-efficiency data synchronization transmission optimization technology model; The multi-source heterogeneous data synchronization technology model based on edge computing divides nodes into regions by analyzing the hierarchical and attribution logical relationships between nodes. The cluster head nodes achieve inter-region communication to complete one-to-many synchronization, and the nodes within the region perform parallel synchronization to achieve synchronous transmission of nodes. To solve the clock differences of different sensor nodes, a hybrid synchronization mechanism of network time protocol and precision time protocol is designed to provide microsecond-level time synchronization accuracy, ensuring the time synchronization and data consistency of energy consumption monitoring and carbon emission data, and providing a data basis for the real-time response ability and decision-making accuracy of the intelligent control platform for pollution reduction and carbon emission reduction in industrial parks; The low-latency and high-efficiency data synchronization and transmission optimization technology model differentiates the real-time requirements of services in data synchronization. It adopts a dual-queue synchronization design, sending high-real-time and low-real-time service data in different queues to achieve differential services for different real-time services and use the link bandwidth more reasonably. In case of data synchronization failure caused by network link quality deterioration or interruption, it adopts the idea of the delay-tolerant network architecture to improve the synchronization success rate, and proposes an opportunistic synchronization method of "store-carry-forward" for the data synchronization services in the low-real-time queue, selecting appropriate relay nodes to improve the data synchronization efficiency. In addition, by adopting an adaptive routing algorithm based on load and network status, it realizes real-time monitoring of the load of network nodes and link quality, dynamically selects the optimal transmission path, and reduces network overhead to improve the data transmission efficiency.

[0021] The service display module includes a service overview model, a carbon emission visualization model, and a performance evaluation and tracking model; The service overview model displays the key indicators and current status of the entire park through a comprehensive chart, such as the total carbon emissions, pollutant concentration, and energy consumption distribution, integrating a real-time data update function to dynamically display the latest environmental monitoring data and energy consumption; The carbon emission visualization model uses geographic information systems to mark the locations of each monitoring point and their corresponding carbon emission data on the map, providing an intuitive spatial display. The carbon emission intensity of different regions is represented by the depth of color, facilitating the discovery of high-emission regions, and drawing a trend chart of carbon emissions over time to help identify peak emission periods and long-term change trends; The performance evaluation and tracking model displays key performance indicators, such as energy consumption per unit output value and carbon intensity, clearly showing the comparison between the actual value and the target value of each indicator in the form of a dashboard.

[0022] The heterogeneous data fusion and secure sharing module includes a multi-source heterogeneous data fusion technology model based on a relational-temporal database and a data sharing and privacy protection technology model based on blockchain; The multi-source heterogeneous data fusion technology model based on a relational-temporal database regards the dimensional relationship of temporal data as a tree structure. Using high-dimensional temporal tuples as the root nodes, which point to other tuples to form non-leaf nodes, thus realizing data transmission and collation. The data in the relational database is regularly uploaded to the cloud data warehouse according to business requirements and is kept consistent with the temporal database through fields such as device ID. The preliminarily integrated data will be transmitted to the cloud, where feature extraction is performed. The extracted features will jointly form a feature vector set with the attribute features in the relational data for subsequent modeling and analysis. To further improve the data fusion and analysis effect, the cloud will combine deep learning technologies, use convolutional neural networks to extract the structured features in the relational data, and at the same time process the sequence features in the temporal data through recurrent neural networks. By constructing a multi-layer network structure of CNN-RNN, deep fusion and association of multi-source heterogeneous relational data and temporal data are achieved, providing more comprehensive feature information for decision-making. The data sharing and privacy protection technology model based on blockchain uses the Practical Byzantine Fault Tolerance algorithm to establish an efficient and reliable consensus mechanism for data sharing, combines blockchain technology with cosine similarity analysis to ensure consistency and security in the data sharing process. By embedding the Neighborhood Consensus-Frequency Modulation Variable Scale Node Classification algorithm and using cosine similarity analysis, nodes are effectively distinguished and processed, ensuring the optimization of the data integration process through classification and frequency adjustment. By constructing a vector space model, this algorithm transforms complex multi-dimensional data summaries into concise vectors for representation, thus simplifying the data processing flow and enhancing the comparability between data.

[0023] The management decision support module includes an intelligent analysis and prediction model for park carbon emissions and pollutant data based on the AI large language model and a collaborative optimization technology model for pollution reduction and carbon emission reduction based on intelligent decision support. The intelligent analysis and prediction model for park carbon emissions and pollutant data based on the AI large language model combines the cross-modal data processing ability of the LLM to construct an intelligent analysis and prediction framework. For emission pattern mining, a BERT model with time window attention is used to extract the key environmental factors of the data, and the relationship between emission data and production, meteorology, and energy consumption is analyzed by combining a knowledge graph to establish a pollution causal relationship network, and knowledge distillation technology is used for attribution analysis. For emission trend prediction, an intelligent prediction system of "short-term dynamic prediction + long-term policy-driven simulation" is constructed. The LSTM optimized by Bayesian is used to predict the short-term emission change trend, and at the same time, a GAN based on causal reasoning is used to model the impact of policies and production adjustments on long-term emissions, and the carbon emission path is dynamically adjusted through multi-objective optimization, providing accurate, efficient, and interpretable governance decisions for the park. The collaborative optimization technology model for pollution reduction and carbon emission reduction based on intelligent decision support combines the cross-modal data processing capabilities of LLM to construct an AI-based intelligent decision support framework; uses deep reinforcement learning to optimize pollution reduction and carbon emission reduction strategies, and combines mixed-integer linear programming to ensure the economy and feasibility of the optimization plan. At the same time, natural language generation technology is used to transform complex decision-making processes into intuitive operation suggestions, improving the response capabilities of managers to sudden pollution incidents and long-term carbon emission reduction strategies. Through LLM-driven intelligent reasoning and adaptive optimization, the collaborative management level of pollution reduction and carbon emission reduction in the park is improved, providing data support and decision support for the construction of green and low-carbon parks.

[0024] The foregoing has shown and described the basic principles, main features and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic features of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes within the meaning and scope of the equivalent elements of the claims in the present invention, and any reference signs in the claims should not be regarded as limiting the claims involved.

[0025] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made therein without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A pollution reduction and carbon reduction platform, characterized by: It includes data collection module, data analysis and prediction module, data transmission optimization module, business display module, three major application modules, heterogeneous data fusion and security sharing module, management decision support module, public service module and transformation and improvement module; The data acquisition module is used to collect atmospheric pollutants, exhaust gases and water quality in the park; The data analysis and prediction module is used to estimate the carbon footprint of the park and predict future emission trends; The data transmission optimization module introduces edge computing, the storage and processing capabilities of edge nodes, and realizes efficient synchronization and transmission between nodes through regional division and collaborative communication of cluster head nodes. In view of the different real-time requirements of different detection services, a hybrid method of dual-queue synchronization and opportunistic synchronization is used to design differentiated service solutions. The "store-carry-forward" strategy is used to reduce network overhead and ensure that the transit nodes improve the data synchronization success rate when the network is unstable, providing more accurate decision-making support for the park's intelligent management platform. The business display module is used to provide a visual interface to display the progress of carbon peak work in the region, key indicator tasks and status, and achieve a one-picture overview, so that managers can quickly understand the overall situation; The three application modules include carbon monitoring, carbon accounting and carbon profiling, which accurately monitor carbon emissions from different sources in the park, establish carbon emission accounts for each enterprise or project, record their emissions and emission reduction efforts, conduct detailed analysis of the energy consumption of enterprises, and propose targeted improvement measures; The heterogeneous data fusion and security sharing module is used to establish a relation-time series fusion database by treating the dimensional relationship of time series data as a tree structure, combining the powerful processing power of cloud computing, using deep learning technology, and using the CNN-RNN multi-layer network structure to achieve deep fusion and association of relational data and time series data. In terms of data sharing and privacy protection, blockchain technology and practical Byzantine fault-tolerant algorithms are introduced to establish an efficient and reliable data sharing consensus mechanism, achieving efficient and reliable data sharing and fusion while ensuring data privacy, providing more powerful data support for park intelligent management; The management decision support module uses the cross-modal data processing capability of LLM to analyze the causal relationship between pollution emissions and production, meteorology, and energy consumption, improve the accuracy of attribution analysis, use optimized LSTM for short-term emission forecasting, and use causal reasoning to simulate the impact of policy adjustments on long-term emissions to achieve dynamic optimization of carbon emission paths. It introduces deep reinforcement learning optimization strategies, combines mixed integer linear programming to ensure economy and feasibility, and uses natural language generation technology to improve decision-making efficiency and responsiveness. It will build a modular management system to support the refined management of energy, carbon emissions, and environmental protection, and promote the coordinated management of pollution reduction and carbon reduction. In combination with information push, text messages, voice broadcasts, and other means, it will also build a digital large-screen display system to display key pollution-discharging and high-energy consumption links in the park, and track the progress and development of pollution reduction and carbon reduction goals in real time. The public service module is used to encourage the industrial park to proactively publish its main practices and typical cases of pollution reduction and carbon reduction, accept social supervision, and encourage enterprises to actively reduce pollution and carbon reduction through the linkage of carbon credits, energy rights trading, and co-creation coupons. The transformation and upgrading module is used to promote the development of the park infrastructure in a more environmentally friendly and efficient direction.

2. A pollution reduction and carbon reduction platform according to claim 1, characterized in that: The data acquisition module includes: environmental monitoring equipment, greenhouse gas monitoring equipment, water quality monitoring equipment, energy consumption monitoring equipment, satellite remote sensing technology and drones and mobile monitoring equipment; The environmental monitoring equipment includes a continuous emission monitoring system and an air quality monitoring station. The continuous emission monitoring system is used to monitor the concentration and emission of pollutants in industrial waste gas in real time, such as sulfur dioxide, nitrogen oxides, and particulate matter; the air quality monitoring station is used to measure a series of parameters in the atmosphere, including but not limited to PM2.5, PM10, ozone, and carbon monoxide; The greenhouse gas monitoring equipment includes a carbon dioxide monitor and a greenhouse gas monitoring device; the carbon dioxide monitor is used to measure the amount of carbon dioxide emitted in the park or a specific enterprise, and the greenhouse gas monitoring device is used to accurately measure greenhouse gases; The water quality monitoring equipment monitors indicators such as chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, total phosphorus, etc. in the water body to evaluate the water quality; The energy consumption monitoring equipment records the use of electricity, water resources and gas by the enterprise, and helps analyze energy efficiency and potential energy saving space; The satellite remote sensing technology provides a wide range of environmental monitoring data, such as vegetation coverage changes, surface temperature, and atmospheric composition, which helps to understand the environmental status of the park and its surrounding areas at a macro level; The mobile monitoring equipment is used for accurate monitoring in hard-to-reach places, supplementing the deficiencies of fixed monitoring points, facilitating flexible deployment at different locations within the park, and quickly acquiring pollution information in local areas.

3. A pollution reduction and carbon reduction platform according to claim 1, characterized in that: The data analysis and prediction module includes a data preprocessing and cleaning model, a carbon emission calculation model, a carbon emission prediction model and a key indicator monitoring model; The data preprocessing and cleaning model integrates data from different sources to ensure consistent data formats, remove outliers, fill in missing values, and ensure data integrity and accuracy; The carbon emission calculation model collects energy consumption data of enterprises in the park and combines it with emission factors of specific industries to calculate the carbon footprint of each enterprise, accurately identify the main carbon emission sources and their contribution ratios, and help managers take targeted emission reduction measures; The carbon emission prediction model uses historical data and time series models such as ARIMA and LSTM to predict future carbon emission trends, simulates the impact of different policies or technical improvement plans on carbon emissions, and evaluates the emission reduction effects under various scenarios; The key indicator monitoring model sets and monitors a series of key indicators, such as energy consumption per unit of output value and carbon intensity, to evaluate the overall energy conservation and emission reduction effectiveness of the park. When certain indicators exceed the preset threshold, the system automatically issues an alarm to remind managers to take corresponding measures.

4. A pollution reduction and carbon reduction platform according to claim 1, characterized in that: The data transmission optimization module includes a multi-source heterogeneous data synchronization technology model based on edge computing and a low-latency and high-efficiency data synchronization transmission optimization technology model; The multi-source heterogeneous data synchronization technology model based on edge computing divides nodes into regions by analyzing the hierarchical and logical relationships between nodes. The cluster head node realizes inter-regional communication to complete one-to-many synchronization, and the nodes in the region are synchronized in parallel to achieve synchronous transmission of nodes. In order to solve the clock differences of different sensor nodes, a hybrid synchronization mechanism of the network time protocol and the precision time protocol is designed to provide microsecond-level time synchronization accuracy, ensure the time synchronization and data consistency of energy consumption monitoring and carbon emission data, and provide a data basis for the real-time response capability and decision-making accuracy of the intelligent management and control platform for pollution reduction and carbon reduction in industrial parks; The low-latency and high-efficiency data synchronization transmission optimization technology model distinguishes the real-time requirements of services in data synchronization, adopts a dual-queue synchronization design, sends high-real-time and low-real-time service data in different queues, implements differentiated services for different real-time services, and uses link bandwidth more reasonably. In the case where data synchronization fails due to deterioration or interruption of network link quality, the delay-tolerant network architecture concept is adopted to improve the synchronization success rate, and an opportunistic synchronization method of "store-carry-forward" is proposed for data synchronization services in low-real-time queues, and appropriate transit nodes are selected to improve data synchronization efficiency. In addition, by adopting an adaptive routing algorithm based on load and network status, real-time monitoring of network node load and link quality is achieved, the optimal transmission path is dynamically selected, and network overhead is reduced to improve data transmission efficiency.

5. The pollution reduction and carbon reduction platform according to claim 1, characterized in that: The business display module includes a business one-picture model, a carbon emission visualization model and a performance evaluation and tracking model; The business one-picture model displays the key indicators and status of the entire park through a comprehensive chart, such as total carbon emissions, pollutant concentrations, and energy consumption distribution, and integrates real-time data update functions to dynamically display the latest environmental monitoring data and energy consumption; The carbon emission visualization model uses a geographic information system to mark the location of each monitoring point and its corresponding carbon emission data on a map, providing an intuitive spatial display, indicating the carbon emission intensity of different regions by color depth, making it easier to find high-emission areas, and drawing a trend chart of carbon emissions over time to help identify peak emission periods and long-term change trends; The performance evaluation and tracking model displays key performance indicators, such as energy consumption per unit of output value and carbon intensity, and clearly shows the comparison between the actual value and the target value of each indicator in the form of a dashboard.

6. A pollution reduction and carbon reduction platform according to claim 1, characterized in that: The heterogeneous data fusion and security sharing module includes a multi-source heterogeneous data fusion technology model based on a relational-time series database and a data sharing and privacy protection technology model based on blockchain; The multi-source heterogeneous data fusion technology model based on relational-time series database regards the dimensional relationship of time series data as a tree structure, uses high-dimensional time series tuples as root nodes, points to other tuples to form non-leaf nodes, thereby realizing data transmission and organization. The data in the relational database is regularly uploaded to the cloud data warehouse according to business needs, and maintains consistency with the time series database through fields such as device ID; the initially integrated data will be transmitted to the cloud, and feature extraction will be performed in the cloud. The extracted features will be combined with the attribute features in the relational data to form a feature vector set for subsequent modeling and analysis; in order to further improve the data fusion and analysis effects, the cloud will combine deep learning technology, use convolutional neural networks to extract structured features in relational data, and process sequence features in time series data through recurrent neural networks; By building a multi-layer network structure of CNN-RNN, the deep fusion and association of multi-source heterogeneous relational data and time series data can be achieved, providing more comprehensive feature information for decision-making; The blockchain-based data sharing and privacy protection technology model adopts a practical Byzantine fault-tolerant algorithm to establish an efficient and reliable consensus mechanism for data sharing, and combines blockchain technology with cosine similarity analysis to ensure consistency and security in the data sharing process; by embedding a neighborhood consensus-frequency modulation and variable-scaling node classification algorithm and using cosine similarity analysis, the nodes are effectively distinguished and processed, ensuring that the data integration process is optimized through classification and frequency adjustment. By constructing a vector space model, the algorithm converts complex and multi-dimensional data summaries into concise vectors to represent them, thereby simplifying the data processing process and enhancing the comparability of data.

7. The pollution reduction and carbon reduction platform according to claim 1, characterized in that: The management decision support module includes an intelligent analysis and prediction model of park carbon emissions and pollutant data based on an AI large language model and a collaborative optimization technology model for pollution reduction and carbon reduction based on intelligent decision support; The intelligent analysis and prediction model of the park's carbon emissions and pollutant data based on the AI ​​large language model combines the cross-modal data processing capabilities of LLM to build an intelligent analysis and prediction framework. For emission pattern mining, the BERT model with time window attention is used to extract key environmental factors of the data, and the relationship between emission data and production, meteorology, and energy consumption is analyzed in combination with the knowledge graph. A pollution causal relationship network is established, and knowledge distillation technology is used for attribution analysis. For emission trend prediction, an intelligent prediction system of "short-term dynamic prediction + long-term policy-driven simulation" is constructed, and Bayesian optimized LSTM is used to predict short-term emission change trends. At the same time, GAN based on causal reasoning is used to model the impact of policy and production adjustments on long-term emissions, and the carbon emission path is dynamically adjusted through multi-objective optimization to provide the park with accurate, efficient, and explainable governance decisions. The pollution reduction and carbon reduction collaborative optimization technology model based on intelligent decision support is combined with the cross-modal data processing capability of LLM to construct an AI-based intelligent decision support framework; deep reinforcement learning is used to optimize pollution reduction and carbon reduction strategies, and mixed integer linear programming is combined to ensure the economy and feasibility of the optimization scheme. At the same time, natural language generation technology is used to transform complex decision-making processes into intuitive operational suggestions, thereby improving managers' response capabilities to sudden pollution incidents and long-term carbon reduction strategies. Through LLM-driven intelligent reasoning and adaptive optimization, the collaborative management level of pollution reduction and carbon reduction in the park is improved, providing data support and decision-making support for the construction of green and low-carbon parks.

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