Intelligent city carbon emission real-time monitoring and trading platform based on block chain

Abstract

The invention relates to the technical field of carbon emission, in particular to an intelligent city carbon emission real-time monitoring and trading platform based on a block chain, which comprises an environment monitoring module, a data acquisition module, a data verification module, a block chain network module and a trading module, the environment monitoring module is responsible for analyzing market requirements and environment conditions in real time and dynamically adjusting a monitoring strategy and resource allocation; the data acquisition module is used for collecting carbon emission related data from various data sources in real time; the data verification module is used for verifying and verifying the collected data; the block chain network module provides a safe, transparent and tamperproof data recording environment for the transaction platform; and the transaction module ensures the security and transparency of the transaction by using a block chain technology. According to the invention, the accuracy and timeliness of the monitoring data are ensured, and data support is provided for the transaction of the carbon emission permit, so that the transparency and efficiency of carbon emission management are promoted.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a blockchain-based smart city carbon emission real-time monitoring and trading platform. Background Art

[0002] As the problems of global warming and environmental degradation become increasingly serious, carbon emission management and reduction have become the focus of common concern of the international community. In order to effectively control and reduce greenhouse gas emissions, a carbon emission trading market has been established to allow the buying and selling of carbon emission rights to incentivize emission reductions. However, the existing carbon emission monitoring and trading system faces problems such as difficulty in data collection, information opacity and low transaction security. These problems seriously hinder the healthy development of the carbon emission trading market and the realization of carbon reduction goals.

[0003] The real-time monitoring technology of carbon emissions is still not mature enough. The existing system often finds it difficult to accurately and real-time monitor and record carbon emissions data, resulting in the reliability and timeliness of the data being unable to meet market and regulatory needs. In addition, the rapid changes in environmental conditions and the diversity of monitoring needs in different regions and industries have put forward higher requirements on the flexibility and adaptability of the monitoring system. Secondly, the traditional carbon emissions data processing and verification process lacks effective technical support, and the accuracy and consistency of the data are difficult to guarantee. Errors in data collection, inconsistent information, or tampering may lead to erroneous carbon emissions information being used in transactions and decision-making, affecting the fairness of transactions and the effectiveness of decisions. In addition, the transaction security and transparency issues in the carbon emissions trading market are also very prominent. In the absence of a reliable data recording and verification mechanism, both parties to the transaction face high risks. Information asymmetry and potential fraud in the transaction process further weaken the vitality of the market and the confidence of participants. Summary of the invention

[0004] Based on the above objectives, the present invention provides a blockchain-based smart city carbon emissions real-time monitoring and trading platform.

[0005] The blockchain-based smart city carbon emission real-time monitoring and trading platform includes an environmental monitoring module, a data collection module, a data verification module, a blockchain network module, and a trading module, among which;

[0006] The environmental monitoring module is responsible for real-time analysis of market demand and environmental conditions, and dynamic adjustment of monitoring strategies and resource allocation;

[0007] The data acquisition module collects carbon emission related data from multiple data sources in real time under the guidance of the environmental monitoring module, and the data sources include intelligent transportation networks, industrial production lines, energy use equipment and related carbon emission sources;

[0008] The data verification module verifies and verifies the collected data;

[0009] The blockchain network module provides a secure, transparent and tamper-proof data recording environment for the transaction platform;

[0010] The trading module supports the buying and selling of carbon emission rights and uses blockchain technology to ensure the security and transparency of transactions.

[0011] Furthermore, the environmental monitoring module includes:

[0012] Real-time market demand analysis: The environmental monitoring module monitors the trading activities of carbon emission rights in real time, including trading volume and price fluctuations, by accessing the market transaction data interface to assess the current market demand for carbon emission data;

[0013] Environmental condition monitoring: The environmental monitoring module synchronously receives data from the environmental monitoring sensor network, including environmental indicators, pollutant concentrations in the area, and weather change information. Environmental indicators include temperature, humidity, and air pressure;

[0014] Dynamic monitoring strategy adjustment: Based on comprehensive analysis of market demand and environmental conditions, the environmental monitoring module automatically adjusts the work priority and resource allocation of the data acquisition module;

[0015] Resource allocation optimization: Dynamically allocate monitoring resources, including data collection personnel, equipment, and computing and storage resources, based on the importance and timeliness of monitoring tasks;

[0016] Adaptive adjustment feedback mechanism: The environmental monitoring module includes a feedback mechanism to continuously optimize monitoring strategies and resource allocation plans based on monitoring results and market feedback.

[0017] Furthermore, the dynamic monitoring strategy adjustment includes:

[0018] Market demand factor M i : reflects the demand intensity for carbon emission data of a region or monitoring type. The calculation formula is: M i =k1·log(V+1)+k2·ΔP, where V is the trading volume in the selected time period, ΔP is the rate of change of price in the same time period, and k1 and k2 are weight parameters;

[0019] Environmental sensitivity factor E i : Considering the environmental conditions of the region and its sensitivity to change, the calculation formula is: Among them, C is the current average concentration of pollutants, ΔR / Δt is the environmental change rate, and h1 and h2 are weight parameters;

[0020] Real-time adjustment factor: market demand factor M i and environmental sensitivity factor E iMake adjustments, the adjustment formula is:

[0021] M i ′=M i ×(1+δ·S m );

[0022] E i ′=E i +λ·I e ;

[0023] Among them, M i ′ is the market demand sensitivity adjustment, E i ′ is the environmental emergency response factor, S m is the rate of change of market demand, I e is a supplementary indicator based on sudden environmental events, δ and λ are adjustment coefficients;

[0024] Monitoring priority and resource allocation: Taking into account market demand and environmental sensitivity factors, calculate the overall priority P of each monitoring task. i , and calculate the resource allocation ratio R i , the calculation formula is:

[0025] P i =α·M i ′+β·E i ′;

[0026]

[0027] Where N is the total number of monitoring tasks, α, β are the weights of adjusting the market demand factor and environmental sensitivity factor in the priority calculation, and P n It is the sum of the priorities of all monitoring tasks;

[0028] Resource allocation flexibility adjustment: Taking into account the resource allocation ratio R i The impact on monitoring task allocation is introduced to adjust the resource allocation ratio. The adjusted resource allocation ratio R i The calculation formula of ′ is:

[0029] R i ′=R i ×(1+·V r );

[0030] Where, is the adjustment coefficient, V r is the rate of change of resource availability;

[0031] Dynamic monitoring strategy adjustment: using the adjusted resource allocation ratio R i ′, the environmental monitoring module dynamically adjusts the work priority and resource allocation of the data acquisition module to optimize the collection of carbon emission data.

[0032] Furthermore, the data acquisition module includes:

[0033] Receiving instructions from the environment monitoring module: The data acquisition module receives instructions from the environment monitoring module. The instructions indicate the priority, frequency and target data source of data acquisition based on the comprehensive analysis of market demand and environmental conditions and the dynamic adjustment of resource allocation ratio;

[0034] Access to multiple data sources: The data acquisition module is equipped with interfaces that connect to multiple data sources;

[0035] Real-time data collection: For intelligent transportation networks, the collected data include vehicle mileage, fuel type and consumption. The carbon emissions of intelligent transportation networks are calculated through on-board sensors and GPS data. For industrial production lines, the emission data of industrial production lines are collected by monitoring the energy consumption and operation status of emission control equipment during the production process. For energy-using equipment, the total carbon emissions of energy-using equipment are calculated by collecting energy consumption data and emission coefficients. Energy-using equipment includes power plants, heating equipment and air-conditioning equipment. For related carbon emission sources, the carbon emissions of related carbon emission sources are calculated based on the type and scale of activities.

[0036] Data preprocessing: preprocess the collected data, including data cleaning, formatting and preliminary analysis;

[0037] Data on-chain: The pre-processed carbon emission data is recorded through the blockchain network to ensure the data is tamper-proof and traceable for subsequent analysis and transactions.

[0038] Furthermore, the calculation formula for the carbon emissions of the intelligent transportation network is:

[0039] E vehicle =D×F×C f ;

[0040] Among them, E vehicle is the carbon emission of the vehicle (kg CO2), D is the distance traveled by the vehicle (km), F is the fuel consumption per unit distance (L / km), C f is the carbon emission factor of the fuel (kg CO2 / L);

[0041] The calculation formula for the emission data of the industrial production line is:

[0042] E industrial =∑(E n ×C n );

[0043] Among them, E industrialis the total carbon emissions from industrial production (kg CO2), E n is the consumption of the nth energy source, C n is the carbon emission coefficient of the nth energy source (kg CO2 / unit of energy);

[0044] The calculation formula for the total carbon emissions of the energy-using equipment is:

[0045] E total =∑(E energy ×C energy );

[0046] Among them, E total is the total carbon emissions of the facility or equipment (kg CO2), E energy is the amount of each energy consumed, C energy is the carbon emission factor of each energy source (kg CO2 / energy unit);

[0047] The calculation formula for the carbon emissions of the relevant carbon emission sources is:

[0048] E other =A×C a ;

[0049] Among them, E other is the carbon emissions generated by the activity (kg CO2), A is the scale of the activity (e.g. area, number of people, etc.), C a is the average carbon emission factor for the activity type (kg CO2 / activity unit).

[0050] Furthermore, the data verification module includes:

[0051] Data integrity check: The data verification module performs integrity checks on the collected data;

[0052] Data consistency verification: The data verification module verifies the consistency of data, including comparing relevant data items between data sources to detect potential contradictions or mismatches;

[0053] Historical data comparison: The data validation module compares and analyzes newly collected data with historical data to identify abnormal fluctuations or changes that do not meet expectations;

[0054] Algorithm verification: Multi-factor weighted mean square error (MFW-MSE) is used to verify the data to ensure the scientific nature of the data and the accuracy of the calculation;

[0055] Blockchain record comparison: The data verification module compares the collected data with the relevant records on the blockchain;

[0056] Report generation: After completing the above verification process, the data verification module generates a verification report, including the verification results, problems found, and recommended improvement measures.

[0057] Furthermore, the multi-factor weighted mean square error MFW-MSE includes:

[0058] Predict carbon emissions: Predict through a linear model, the model is expressed as:

[0059]

[0060] in, is the model-predicted carbon emissions for the ith data point, X 1i ,X 2i ,…,X ni are the n variables that affect the carbon emissions of the i-th data point (such as energy consumption, activity type indicators, etc.), β0, β1, …, β n is a model parameter, which indicates the weight of each variable on carbon emissions. i is the error term, capturing the variability not explained by the model;

[0061] Multi-factor weighted mean square error MFW-MSE calculation: By comprehensively considering multiple influencing factors to adjust the weight of each data point and evaluate the prediction performance of the model, the calculation formula is:

[0062]

[0063] Where M is the total number of samples, w i is the basic weight of the ith data point, are the environmental adjustment factor, activity type factor and time factor of the i-th data point, respectively, and Y i is the actual carbon emission of the ith data point, is the predicted carbon emissions of the ith data point calculated by the carbon emissions prediction model.

[0064] Furthermore, the calculation formula of the environmental adjustment factor is:

[0065]

[0066] Among them, C e is the current environmental condition indicator, C e0 is the reference value of environmental conditions, σ e is the standard deviation of the effect of changes in environmental conditions on carbon emissions;

[0067] The calculation formula of the activity type factor is:

[0068] A act =w act ;

[0069] Among them, w act It is a predefined weight value based on the activity type (such as transportation, industry, etc.);

[0070] The calculation formula of the time factor is:

[0071] T period =1+α p ×δ p ;

[0072] Among them, α p is the adjustment coefficient of the time factor, δ p It is based on seasonal changes, working days and non-working days, and time deviation values ​​between day and night.

[0073] Furthermore, the transaction module includes:

[0074] Transaction initiation and matching: Transaction participants (buyers or sellers) submit orders for the sale of carbon emission rights through the transaction module, including transaction type (buy / sell), quantity, and price. The transaction module automatically matches the buy and sell orders and finds a suitable counterparty.

[0075] Transaction confirmation and recording: When the order is successfully matched, the transaction module sends transaction confirmation information to both parties and requires both parties to confirm the transaction. After both parties confirm, the transaction details (including the identities of both parties, transaction quantity, price, etc.) are recorded on the blockchain;

[0076] Transaction execution and carbon emission rights transfer: After confirming that the transaction is correct, the transaction module automatically executes the transaction, including deducting the corresponding carbon emission rights from the seller's account and transferring them to the buyer's account;

[0077] Security and transparency guarantee: During the transaction process, all transaction information and participant identities are encrypted to ensure information security and privacy.

[0078] Beneficial effects of the present invention:

[0079] The present invention, by integrating the environmental monitoring module and the data acquisition module, can analyze market demand and environmental conditions in real time, and dynamically adjust monitoring strategies and resource allocation. This real-time monitoring and dynamic adjustment mechanism optimizes the frequency and accuracy of data acquisition, allowing the platform to adapt to different monitoring needs and environmental changes. In this way, the platform ensures the accuracy and timeliness of monitoring data, provides solid data support for the trading of carbon emission rights, and thus promotes the transparency and efficiency of carbon emission management.

[0080] The present invention ensures the accuracy and consistency of the data by deeply checking and verifying the collected data. Combined with the blockchain network module, the platform provides a safe, transparent and tamper-proof recording environment for all verified carbon emission data and transaction records. The integration of data verification and blockchain technology not only greatly improves the security and transparency of data and transactions, but also enhances the trust and credibility of the platform, laying the foundation for the healthy development of the carbon emission trading market.

[0081] The present invention uses blockchain technology to ensure the security and transparency of transactions, facilitates buyers and sellers to conduct efficient and fair transactions based on real-time and accurate carbon emission data, and automates and digitizes processes such as automatic order matching, transaction confirmation and recording, and transfer of carbon emission rights. It not only improves transaction efficiency, but also ensures the fairness and traceability of the transaction process. This efficient and fair carbon emission rights trading mechanism encourages market participants to actively participate in carbon emission reduction activities, thereby promoting the sustainable development and environmental protection of smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0083] Figure 1 A schematic diagram of a platform function module according to an embodiment of the present invention;

[0084] Figure 2 Schematic diagram of a data verification module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0085] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0086] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0087] like Figure 1-2 As shown, the blockchain-based smart city carbon emission real-time monitoring and trading platform includes an environmental monitoring module, a data collection module, a data verification module, a blockchain network module and a trading module, among which;

[0088] The environmental monitoring module is responsible for real-time analysis of market demand and environmental conditions, dynamically adjusting monitoring strategies and resource allocation, optimizing the frequency and accuracy of data collection, and providing pre-guidance for the data collection module to adapt to different monitoring needs and environmental changes;

[0089] The data collection module collects carbon emission-related data from multiple data sources in real time under the guidance of the environmental monitoring module. The data sources include intelligent transportation networks, industrial production lines, energy-using equipment, and related carbon emission sources.

[0090] The data verification module verifies and validates the collected data to ensure the accuracy and consistency of the data. The data verification module conducts in-depth analysis of the data, identifies possible anomalies or errors, and makes necessary corrections;

[0091] The blockchain network module provides a secure, transparent and tamper-proof data recording environment for the trading platform. All verified carbon emission data and transaction records are recorded on the blockchain to ensure the authenticity of the data and the traceability of the transactions.

[0092] The trading module supports the buying and selling of carbon emission rights, and uses blockchain technology to ensure the security and transparency of transactions. The trading module facilitates buyers and sellers to trade based on real-time and accurate carbon emission data, thus improving the efficiency and fairness of transactions.

[0093] By integrating these modules, the platform can achieve real-time monitoring of carbon emissions, accurate verification of data and efficient trading of carbon emission rights, providing a reliable carbon management and trading solution for smart cities and promoting carbon emission reduction and sustainable development.

[0094] Environmental monitoring modules include:

[0095] Real-time market demand analysis: The environmental monitoring module monitors the trading activities of carbon emission rights in real time, including trading volume and price fluctuations, by accessing the market transaction data interface to assess the current market demand for carbon emission data;

[0096] Environmental condition monitoring: The environmental monitoring module synchronously receives data from the environmental monitoring sensor network, including environmental indicators, pollutant concentrations in the area, and weather change information. Environmental indicators include temperature, humidity, and air pressure;

[0097] Dynamic monitoring strategy adjustment: Based on a comprehensive analysis of market demand and environmental conditions, the environmental monitoring module automatically adjusts the work priority and resource allocation of the data collection module. For example, for regions or industries with active carbon markets and large carbon emission rights trading volumes, the monitoring frequency and density of data collection points are increased;

[0098] Resource allocation optimization: Dynamically allocate monitoring resources, including data collection personnel, equipment, and computing and storage resources, based on the importance and timeliness of monitoring tasks, to ensure that high-priority monitoring tasks receive sufficient resource support;

[0099] Adaptive adjustment feedback mechanism: The environmental monitoring module includes a feedback mechanism to continuously optimize monitoring strategies and resource allocation plans based on monitoring results and market feedback to respond to changes in market and environmental conditions and ensure the accuracy and timeliness of monitoring data;

[0100] In this way, the environmental monitoring module can ensure that the real-time carbon emissions monitoring and trading platform always maintains efficient and sensitive monitoring capabilities when facing changes in the environment and market demand, providing solid data support for the accurate trading and effective management of carbon emission rights.

[0101] Dynamic monitoring strategy adjustments include:

[0102] Market demand factor M i : reflects the demand intensity for carbon emission data of a region or monitoring type. The calculation formula is: M i =k1·log(V+1)+k2·ΔP, where V is the trading volume in the selected time period, ΔP is the rate of change of price in the same time period, and k1 and k2 are weight parameters;

[0103] Environmental sensitivity factor E i : Considering the environmental conditions of the region and its sensitivity to change, the calculation formula is: Among them, C is the current average concentration of pollutants, ΔR / Δt is the environmental change rate, and h1 and h2 are weight parameters;

[0104] Real-time adjustment factor: market demand factor M i and environmental sensitivity factor E i Make adjustments, the adjustment formula is:

[0105] M i ′=M i ×(1+δ·S m );

[0106] E i ′=E i +λ·I e ;

[0107] Among them, M i ′ is the market demand sensitivity adjustment, E i ′ is the environmental emergency response factor, S m is the rate of change of market demand, I e is a supplementary indicator based on sudden environmental events, δ and λ are adjustment coefficients;

[0108] Monitoring priority and resource allocation: Taking into account market demand and environmental sensitivity factors, calculate the overall priority P of each monitoring task. i , and calculate the resource allocation ratio R i , the calculation formula is:

[0109] P i =α·M i ′+β·E i ′;

[0110]

[0111] Where N is the total number of monitoring tasks, α, β are the weights of adjusting the market demand factor and environmental sensitivity factor in the priority calculation, and P n It is the sum of the priorities of all monitoring tasks;

[0112] Resource allocation flexibility adjustment: Taking into account the resource allocation ratio R i The impact on monitoring task allocation is introduced to adjust the resource allocation ratio. The adjusted resource allocation ratio R i The calculation formula of ′ is:

[0113] R i ′=R i ×(1+·V r );

[0114] Where, is the adjustment coefficient, V r is the rate of change in resource availability, such as the number of new or reduced monitoring devices, changes in human resources, etc.;

[0115] Dynamic monitoring strategy adjustment: using the adjusted resource allocation ratio R i ′, the environmental monitoring module dynamically adjusts the work priority and resource allocation of the data acquisition module to optimize the collection of carbon emission data;

[0116] In the blockchain-based smart city carbon emissions real-time monitoring and trading platform, by directly introducing elastic adjustments in the resource allocation ratio, the environmental monitoring module can respond to resource changes more flexibly, which not only simplifies the algorithm, but also improves the flexibility and efficiency of resource allocation, ensuring that monitoring resources can be timely and effectively reallocated according to actual needs and resource changes. This is especially important for maintaining the continuity and accuracy of monitoring tasks when resources are limited or demand changes dramatically. Combined with the immutability and transparency of blockchain technology, this dynamic adjustment mechanism further enhances the reliability of carbon emissions monitoring and trading data, providing strong support for the sustainable development of smart cities.

[0117] The data acquisition module includes:

[0118] Receiving instructions from the environment monitoring module: The data acquisition module receives instructions from the environment monitoring module. The instructions indicate the priority, frequency and target data source of data acquisition based on the comprehensive analysis of market demand and environmental conditions and the dynamic adjustment of resource allocation ratio;

[0119] Access to multiple data sources: The data acquisition module is equipped with interfaces to connect to multiple data sources. Each data source transmits data through a specific protocol and format to ensure data accuracy and consistency.

[0120] Real-time data collection: For intelligent transportation networks, the collected data include vehicle mileage, fuel type and consumption. The carbon emissions of intelligent transportation networks are calculated through on-board sensors and GPS data. For industrial production lines, the emission data of industrial production lines are collected by monitoring the energy consumption and operation status of emission control equipment during the production process. For energy-using equipment, the total carbon emissions of energy-using equipment are calculated by collecting energy consumption data and emission coefficients. Energy-using equipment includes power plants, heating equipment and air-conditioning equipment. For related carbon emission sources, the carbon emissions of related carbon emission sources are calculated according to the type and scale of activities, such as agricultural activities, building operations, etc.

[0121] Data preprocessing: preprocess the collected data, including data cleaning, formatting and preliminary analysis to remove noise and ensure data quality;

[0122] Data on-chain: The pre-processed carbon emission data is recorded through the blockchain network to ensure the data is immutable and traceable for subsequent analysis and transactions;

[0123] Through the above implementation method, the data acquisition module can efficiently and accurately collect carbon emission related data from multiple data sources, meet the real-time and accuracy requirements of the blockchain-based smart city carbon emission real-time monitoring and trading platform, and provide solid data support for the trading of carbon emission rights.

[0124] The calculation formula for the carbon emissions of intelligent transportation networks is:

[0125] E vehicle =D×F×C f ;

[0126] Among them, E vehicle is the carbon emission of the vehicle (kg CO2), D is the distance traveled by the vehicle (km), F is the fuel consumption per unit distance (L / km), C f is the carbon emission factor of the fuel (kg CO2 / L);

[0127] The calculation formula for emission data of industrial production lines is:

[0128] E industrial =∑(E n ×C n );

[0129] Among them, E industrial is the total carbon emissions from industrial production (kg CO2), E n is the consumption of the nth energy source, C n is the carbon emission coefficient of the nth energy source (kg CO2 / unit of energy);

[0130] The total carbon emissions of energy-using equipment are calculated as:

[0131] E total =∑(E energy ×C energy );

[0132] Among them, E total is the total carbon emissions of the facility or equipment (kg CO2), E energy is the amount of each energy consumed, C energy is the carbon emission factor of each energy source (kg CO2 / energy unit);

[0133] The calculation formula for carbon emissions from relevant carbon emission sources is:

[0134] E other =A×C a ;

[0135] Among them, E other is the carbon emissions generated by the activity (kg CO2), A is the scale of the activity (e.g. area, number of people, etc.), C a is the average carbon emission factor of the activity type (kg CO2 / activity unit);

[0136] In the blockchain-based smart city carbon emissions real-time monitoring and trading platform, the above calculation method provides a complete carbon emissions calculation framework for the data acquisition module. By collecting data from multiple data sources in real time and applying the corresponding calculation formula, the platform can accurately estimate carbon emissions from different sources. After preprocessing and verification, these data can be recorded on the blockchain to ensure their immutability and traceability. Such data supports the environmental monitoring module (EMM) to make more accurate monitoring strategy adjustments and provide an accurate data basis for the trading of carbon emission rights, thereby promoting the transparency of carbon emission management.

[0137] The data validation module includes:

[0138] Data integrity check: The data validation module performs integrity checks on the collected data to ensure that all necessary data fields are filled and there is no missing information, including checks on basic attributes such as timestamps, data source identifiers, and value ranges;

[0139] Data consistency verification: The data verification module verifies the consistency of data, including comparing relevant data items between data sources to detect potential contradictions or mismatches. For example, comparing records of the same carbon emission source from different data sources to ensure their consistency;

[0140] Historical data comparison: The data verification module compares and analyzes newly collected data with historical data to identify abnormal fluctuations or changes that do not meet expectations. Using historical data can help identify data entry errors or potential data tampering;

[0141] Algorithm verification: Multi-factor weighted mean square error (MFW-MSE) is used to verify the data to ensure the scientific nature of the data and the accuracy of the calculation;

[0142] Blockchain record comparison: The data verification module compares the collected data with the relevant records on the blockchain. Any inconsistencies are marked and reported for further investigation.

[0143] Report generation: After completing the above verification process, the data verification module generates a verification report, including verification results, problems found, and recommended improvement measures. These reports are crucial to improving data quality and supporting decision making;

[0144] Through the above implementation method, the data verification module ensures that the data used in the blockchain-based smart city carbon emissions real-time monitoring and trading platform is accurate, consistent and reliable. This strict data verification and validation mechanism not only enhances the credibility of the data, but also provides a solid data foundation for the trading of carbon emission rights and the formulation of carbon reduction measures.

[0145] The multi-factor weighted mean square error MFW-MSE includes:

[0146] Predict carbon emissions: Predict through a linear model, the model is expressed as:

[0147]

[0148] in, is the model-predicted carbon emissions for the ith data point, X 1i ,X 2i ,…,X ni are the n variables that affect the carbon emissions of the i-th data point (such as energy consumption, activity type indicators, etc.), β0, β1, …, β n is a model parameter, which indicates the weight of each variable on carbon emissions. i is the error term, capturing the variability not explained by the model;

[0149] Multi-factor weighted mean square error MFW-MSE calculation: By comprehensively considering multiple influencing factors to adjust the weight of each data point and evaluate the prediction performance of the model, the calculation formula is:

[0150]

[0151] Where M is the total number of samples, w i is the basic weight of the ith data point, are the environmental adjustment factor, activity type factor and time factor of the i-th data point, respectively, and Y i is the actual carbon emission of the ith data point, is the predicted carbon emissions of the ith data point calculated by the carbon emissions prediction model;

[0152] In the blockchain-based smart city carbon emissions real-time monitoring and trading platform, the accuracy of carbon emissions data can be accurately verified by using the improved MFW-MSE algorithm and the above-mentioned carbon emissions prediction model. This method takes into account a variety of influencing factors in carbon emissions prediction, such as environmental conditions, activity types, and time changes. By comprehensively considering the impact of these factors on prediction accuracy, the scientificity and rationality of data verification are improved.

[0153] The multi-factor weighted mean square error MFW-MSE not only strengthens the logic and comprehensiveness of the data verification process, but also provides a flexible framework for the platform to adapt to the complexity and variability of carbon emission monitoring. Such a data verification mechanism ensures the accuracy and consistency of carbon emission data on the platform, supports data-based decision-making, such as carbon trading and emission reduction strategy planning, and further promotes the sustainable development goals of smart cities.

[0154] The calculation formula of the environmental adjustment factor is:

[0155]

[0156] Among them, C e is the current environmental condition indicator, C e0 is the reference value of environmental conditions, σ e is the standard deviation of the effect of changes in environmental conditions on carbon emissions;

[0157] The activity type factor is calculated as:

[0158] A act =w act ;

[0159] Among them, w act It is a predefined weight value based on the activity type (such as transportation, industry, etc.);

[0160] The calculation formula of time factor is:

[0161] T period =1+α p ×δ p ;

[0162] Among them, α p is the adjustment coefficient of the time factor, δ p It is a time deviation value based on seasonal changes, working days and non-working days, and daytime and nighttime;

[0163] In the blockchain-based smart city carbon emissions real-time monitoring and trading platform, this meticulous approach takes into account the impact of different environmental conditions, activity types and time periods on carbon emissions. It helps ensure that the calculation and prediction of carbon emissions are more accurate, thereby providing solid data support for the trading and management of carbon emission rights. Through this method, the reliability and transparency of the smart city carbon emissions monitoring system can be effectively improved, and the realization of sustainable development goals can be promoted.

[0164] The trading modules include:

[0165] Transaction initiation and matching: Transaction participants (buyers or sellers) submit orders for the sale of carbon emission rights through the transaction module, including transaction type (buy / sell), quantity, price and other information. The transaction module automatically matches the buy and sell orders and finds a suitable counterparty.

[0166] Transaction confirmation and recording: When the order is successfully matched, the transaction module sends transaction confirmation information to both parties and requires both parties to confirm the transaction. After both parties confirm, the transaction details (including the identities of both parties, transaction quantity, price, etc.) are recorded on the blockchain. This step uses the immutability of the blockchain to ensure the security and permanence of the transaction record;

[0167] Transaction execution and carbon emission rights transfer: After confirming that the transaction is correct, the transaction module automatically executes the transaction, including deducting the corresponding carbon emission rights from the seller's account and transferring them to the buyer's account. The ownership change information of the carbon emission rights is also recorded on the blockchain to ensure the transparency and traceability of the rights transfer;

[0168] Security and transparency guarantee: During the transaction process, all transaction information and participant identities are encrypted to ensure information security and privacy. Since transaction records are stored on the blockchain, all participants can verify the authenticity of the transaction records, but cannot modify them, thus ensuring the transparency and fairness of the transaction;

[0169] In the blockchain-based smart city carbon emissions real-time monitoring and trading platform, the design and implementation of the trading module fully utilizes the advantages of blockchain technology, such as immutability, encryption security and decentralized auditing, to support the buying and selling of carbon emission rights. This not only makes the carbon emission rights trading process more secure and transparent, but also improves market efficiency and the trust of participants.

[0170] In this way, the platform can provide a reliable carbon emission rights trading environment, promote the rational flow of carbon emission rights and the effective control of total carbon emissions, which is of great significance for encouraging carbon emission reduction, promoting the use of clean energy and achieving the sustainable development goals of smart cities.

[0171] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0172] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A blockchain-based smart city carbon emissions real-time monitoring and trading platform, characterized by: It includes an environmental monitoring module, a data collection module, a data verification module, a blockchain network module and a transaction module, among which; The environmental monitoring module is responsible for real-time analysis of market demand and environmental conditions, and dynamic adjustment of monitoring strategies and resource allocation; The data acquisition module collects carbon emission related data from multiple data sources in real time under the guidance of the environmental monitoring module, and the data sources include intelligent transportation networks, industrial production lines, energy use equipment and related carbon emission sources; The data verification module verifies and verifies the collected data; The blockchain network module provides a secure, transparent and tamper-proof data recording environment for the transaction platform; The trading module supports the buying and selling of carbon emission rights and uses blockchain technology to ensure the security and transparency of transactions.

2. According to the blockchain-based smart city carbon emission real-time monitoring and trading platform of claim 1, it is characterized in that: The environmental monitoring module comprises: Real-time market demand analysis: The environmental monitoring module monitors the trading activities of carbon emission rights in real time, including trading volume and price fluctuations, by accessing the market transaction data interface to assess the current market demand for carbon emission data; Environmental condition monitoring: The environmental monitoring module synchronously receives data from the environmental monitoring sensor network, including environmental indicators, pollutant concentrations in the area, and weather change information. Environmental indicators include temperature, humidity, and air pressure; Dynamic monitoring strategy adjustment: Based on comprehensive analysis of market demand and environmental conditions, the environmental monitoring module automatically adjusts the work priority and resource allocation of the data acquisition module; Resource allocation optimization: Dynamically allocate monitoring resources, including data collection personnel, equipment, and computing and storage resources, based on the importance and timeliness of monitoring tasks; Adaptive adjustment feedback mechanism: The environmental monitoring module includes a feedback mechanism to continuously optimize monitoring strategies and resource allocation plans based on monitoring results and market feedback.

3. The blockchain-based smart city carbon emissions real-time monitoring and trading platform according to claim 2 is characterized in that: The dynamic monitoring strategy adjustment includes: Market demand factor M i : reflects the demand intensity for carbon emission data of a region or monitoring type. The calculation formula is: M i =k1·log(V+1)+k2·ΔP, where V is the trading volume in the selected time period, ΔP is the rate of change of price in the same time period, and k1 and k2 are weight parameters; Environmental sensitivity factor E i : Considering the environmental conditions of the region and its sensitivity to change, the calculation formula is: Among them, C is the current average concentration of pollutants, ΔR / Δt is the environmental change rate, and h1 and h2 are weight parameters; Real-time adjustment factor: market demand factor M i and environmental sensitivity factor E i Make adjustments, the adjustment formula is: M i ′=M i ×(1+δ·S m ); AND i ′=And i +λ·I e ; Among them, M i ′ is the market demand sensitivity adjustment, E i ′ is the environmental emergency response factor, S m is the rate of change of market demand, I e is a supplementary indicator based on sudden environmental events, δ and λ are adjustment coefficients; Monitoring priority and resource allocation: Taking into account market demand and environmental sensitivity factors, calculate the comprehensive priority P of each monitoring task. i , and calculate the resource allocation ratio R i , the calculation formula is: P i =α·M i ′+β·E i ′; Where N is the total number of monitoring tasks, α, β are the weights of adjusting the market demand factor and environmental sensitivity factor in the priority calculation, and P n It is the sum of the priorities of all monitoring tasks; Resource allocation flexibility adjustment: Taking into account the resource allocation ratio R i The impact on monitoring task allocation is introduced to adjust the resource allocation ratio. The adjusted resource allocation ratio R i The calculation formula of ′ is: R i ′=R i ×(1+·V r ); Where, is the adjustment coefficient, V r is the rate of change of resource availability; Dynamic monitoring strategy adjustment: using the adjusted resource allocation ratio R i ′, the environmental monitoring module dynamically adjusts the work priority and resource allocation of the data acquisition module to optimize the collection of carbon emission data.

4. The blockchain-based smart city carbon emissions real-time monitoring and trading platform according to claim 3 is characterized in that: The data acquisition module comprises: Receiving instructions from the environment monitoring module: The data acquisition module receives instructions from the environment monitoring module. The instructions indicate the priority, frequency and target data source of data acquisition based on the comprehensive analysis of market demand and environmental conditions and the dynamic adjustment of resource allocation ratio; Access to multiple data sources: The data acquisition module is equipped with interfaces that connect to multiple data sources; Real-time data collection: For intelligent transportation networks, the collected data include vehicle mileage, fuel type and consumption. The carbon emissions of intelligent transportation networks are calculated through on-board sensors and GPS data. For industrial production lines, the emission data of industrial production lines are collected by monitoring the energy consumption and operation status of emission control equipment during the production process. For energy-using equipment, the total carbon emissions of energy-using equipment are calculated by collecting energy consumption data and emission coefficients. Energy-using equipment includes power plants, heating equipment and air-conditioning equipment. For related carbon emission sources, the carbon emissions of related carbon emission sources are calculated based on the type and scale of activities. Data preprocessing: preprocess the collected data, including data cleaning, formatting and preliminary analysis; Data on-chain: The pre-processed carbon emission data is recorded through the blockchain network to ensure the data is tamper-proof and traceable for subsequent analysis and transactions.

5. According to the blockchain-based smart city carbon emission real-time monitoring and trading platform of claim 4, it is characterized in that: The calculation formula of carbon emissions of the intelligent transportation network is: in, is the carbon emission of the vehicle, D is the distance traveled by the vehicle, F is the fuel consumption per unit distance, C f is the carbon emission coefficient of the fuel; The calculation formula for the emission data of the industrial production line is: AND industrial =∑(E n ×C n ); Among them, E industrial is the total carbon emissions from industrial production, E n is the consumption of the nth energy source, C n is the carbon emission coefficient of the nth energy source; The calculation formula for the total carbon emissions of the energy-using equipment is: AND total =∑(E energy ×C energy ); Among them, E total is the total carbon emissions of a facility or equipment, E energy is the amount of each energy consumed, C energy is the carbon emission coefficient of each energy source; The calculation formula for the carbon emissions of the relevant carbon emission sources is: AND other =A×C a ; Among them, E other is the carbon emissions generated by the activity, A is the scale of the activity, C a is the average carbon emission factor for the activity type.

6. The blockchain-based smart city carbon emissions real-time monitoring and trading platform according to claim 5 is characterized in that: The data verification module comprises: Data integrity check: The data verification module performs integrity checks on the collected data; Data consistency verification: The data verification module verifies the consistency of data, including comparing relevant data items between data sources to detect potential contradictions or mismatches; Historical data comparison: The data validation module compares and analyzes newly collected data with historical data to identify abnormal fluctuations or changes that do not meet expectations; Algorithm verification: Multi-factor weighted mean square error MFW-MSE is used to verify the data; Blockchain record comparison: The data verification module compares the collected data with the relevant records on the blockchain; Report generation: After completing the above verification process, the data verification module generates a verification report, including the verification results, problems found, and recommended improvement measures.

7. The blockchain-based smart city carbon emissions real-time monitoring and trading platform according to claim 6 is characterized in that: The multi-factor weighted mean square error MFW-MSE includes: Predict carbon emissions: Predict through a linear model, the model is expressed as: in, is the model-predicted carbon emissions for the ith data point, X 1i ,X 2i ,…,X ni are n variables that affect the carbon emissions of the i-th data point, β0, β1, …, β n is a model parameter, which indicates the weight of each variable on carbon emissions. i is the error term, capturing the variability not explained by the model; Multi-factor weighted mean square error MFW-MSE calculation: By comprehensively considering multiple influencing factors to adjust the weight of each data point and evaluate the prediction performance of the model, the calculation formula is: Where M is the total number of samples, w i is the basic weight of the ith data point, are the environmental adjustment factor, activity type factor and time factor of the i-th data point, respectively, and Y i is the actual carbon emission of the ith data point, is the predicted carbon emissions of the ith data point calculated by the carbon emissions prediction model.

8. The blockchain-based smart city carbon emissions real-time monitoring and trading platform according to claim 7 is characterized in that: The calculation formula of the environmental adjustment factor is: Among them, C e is the current environmental condition indicator, C e0 is the reference value of environmental conditions, σ e is the standard deviation of the effect of changes in environmental conditions on carbon emissions; The calculation formula of the activity type factor is: A act =w act ; Among them, w act It is a predefined weight value based on the activity type; The calculation formula of the time factor is: T period =1+a p ×d p ; Among them, α p is the adjustment coefficient of the time factor, δ p It is based on seasonal changes, working days and non-working days, and time deviation values ​​between day and night.

9. The blockchain-based smart city carbon emissions real-time monitoring and trading platform according to claim 8 is characterized in that: The transaction module includes: Transaction initiation and matching: Transaction participants submit orders for buying and selling carbon emission rights through the transaction module, including transaction type, quantity, and price. The transaction module automatically matches the buying and selling orders and finds a suitable counterparty; Transaction confirmation and recording: When the order is successfully matched, the transaction module sends transaction confirmation information to both parties and requires both parties to confirm the transaction. After both parties confirm, the transaction details are recorded on the blockchain; Transaction execution and carbon emission rights transfer: After confirming that the transaction is correct, the transaction module automatically executes the transaction, including deducting the corresponding carbon emission rights from the seller's account and transferring them to the buyer's account; Security and transparency guarantee: During the transaction process, all transaction information and participant identities are encrypted to ensure information security and privacy.

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