Carbon neutralization management method and system based on digital asset circulation

Abstract

The invention relates to the technical field of environment management, in particular to a carbon neutralization management method and system based on digital asset circulation, and the method comprises the following steps: screening associated data of energy consumption and carbon emission based on carbon emission data, energy consumption, a total production value and an industrial production value, and eliminating abnormal values and redundant information. According to the invention, through combination of trend weight and time sequence fitting, the influence range of trend change is determined, the prediction capability of dynamic association of energy consumption and carbon emission is effectively enhanced, and through analysis of quota distribution records and time span parameters, quota adjustment in a transaction path is optimized, the quota distribution efficiency and flexibility are improved, and the prediction efficiency of the dynamic association of energy consumption and carbon emission is improved. Supply and demand fluctuation analysis is combined with quota price and distribution adjustment, a supply and demand matching mechanism is dynamically optimized, distribution accuracy and response efficiency are improved, fluctuation intervals and risk association weights are dynamically calculated, real-time assessment and regulation of risk changes are realized, and adverse effects of risk accumulation on carbon transaction management are reduced.

Description

Technical Field

[0001] The present invention relates to the field of environmental management technology, and in particular to a carbon neutrality management method and system based on the circulation of digital assets. Background Art

[0002] The technical field of carbon neutrality management methods includes digital information processing systems and methods related to environmental management. Its core content is the full-process monitoring, accounting, allocation and management of carbon emissions to achieve optimal utilization of environmental resources and ecological protection. This technical field includes the collection of carbon emission data, the establishment of a carbon emission accounting system, the allocation and trading management of carbon emission quotas, and also covers carbon footprint tracking based on data analysis, carbon emission monitoring and the design of dynamic control mechanisms to ensure the accuracy and traceability of carbon emission data.

[0003] Among them, the carbon neutrality management method refers to the specific implementation method of trading and managing carbon quotas through monitoring and accounting of carbon emission data and based on digital technology. The technical matters targeted by the patent subject include real-time collection and accounting of carbon emission data, calculation of carbon emissions through standardized algorithms, and formulation of quota allocation mechanisms, distributed recording and transaction tracking of carbon emission quotas based on blockchain technology, and automated verification and management of the execution process of carbon emission quota transactions through smart contract technology to ensure the scientificity and controllability of carbon neutrality goals.

[0004] Existing technologies do not adequately handle outliers in carbon emission data, resulting in a decrease in the accuracy of the analysis of the correlation between energy consumption and carbon emissions, and the interaction relationship between multi-dimensional data cannot effectively reflect dynamic changes. Trend change analysis fails to combine time series dynamic weights and parameter fitting, making it difficult to accurately assess the specific impact of trend changes on carbon emissions, affecting forecasting capabilities. There is a lack of in-depth analysis of quota distribution and time span fluctuations in the trading path, resulting in lagging path optimization and insufficient trading flexibility. The allocation adjustment mechanism lacks dynamic optimization during supply and demand fluctuations, making it difficult to achieve accurate allocation and efficient use of resources under fluctuations. Risk assessment fails to accurately calculate the fluctuation range and correlation weights, making it difficult to track risk changes in real time and effectively regulate them. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a carbon neutrality management method and system based on the circulation of digital assets.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a carbon neutrality management method based on the circulation of digital assets, comprising the following steps: S1: Based on carbon emission data, energy consumption, gross domestic product and industrial output value, screen the correlation data between energy consumption and carbon emissions, remove outliers and redundant information, calculate the interactive relationship of multi-dimensional data, establish a dynamic matrix of energy consumption and carbon emissions, and obtain dynamic eigenvalues; S2: Based on the dynamic characteristic value, the dynamic relationship between emission quota and energy consumption in the time series is screened, the weight value of trend change in the differentiated time period is calculated, the correlation relationship between energy consumption and trend parameters is fitted, the influence range of trend change parameters on the time series is calculated, and the trend dynamic coefficient value is generated; S3: Based on the trend dynamic coefficient value, filter the quota distribution records and time span parameters in the transaction path, calculate the dynamic fluctuation range of the path, determine the correlation between the change of the transaction time span in the path and the quota fluctuation, analyze the impact of the path fluctuation on the transaction quota, and generate the path optimization difference value; S4: Based on the path optimization difference value, analyze the distribution change range in the supply and demand fluctuation, adjust the supply and demand data and the distribution coefficient in the quota price, calculate the impact weight of the balanced distribution record and the supply and demand fluctuation, dynamically adjust the supply and demand distribution results, establish a dynamic relationship between the supply and demand matching parameters, and generate the supply and demand balance distribution coefficient; S5: Based on the supply and demand balance allocation coefficient, screen the transaction path and the real-time supply and demand fluctuation records, analyze the cumulative impact of path fluctuations on risk changes, determine the cumulative deviation between the supply and demand fluctuation range and the allocation coefficient, calculate the correlation weight between the fluctuation range and the dynamic risk, and generate the dynamic risk volatility.

[0007] The carbon emission dynamic matrix specifically includes the energy consumption matrix, the carbon emission matrix, and the characteristic matrix value. The trend dynamic coefficient value includes the trend change weight, the trend parameter correlation relationship, and the time series influence range. The path optimization difference value includes the quota distribution record fluctuation range, the transaction time span variation range, and the transaction quota impact range. The supply and demand balance allocation coefficient includes the supply and demand data adjustment coefficient, the quota price allocation coefficient, and the dynamic relationship between the supply and demand matching parameters. The dynamic risk volatility specifically refers to the fluctuation range correlation weight, the dynamic risk cumulative impact, and the supply and demand fluctuation cumulative deviation.

[0008] As a further solution of the present invention, the step of obtaining the characteristic matrix value is specifically as follows: S111: extract carbon emission data, energy consumption, gross domestic product and industrial output value, remove null values, duplicate values ​​and logical outliers in the data, and screen valid data pairs with correlation between energy consumption and carbon emissions by calling the difference data in the time series of energy consumption and carbon emissions, and generate a correlation data matrix of energy consumption and carbon emissions; S112: using the correlation data matrix of energy consumption and carbon emissions, calculating the interaction relationship between data, solving the linear coefficient of energy consumption affecting carbon emissions through the weighted ratio of matrix row and column elements, determining the nonlinear correction coefficient according to the comparison ratio of fluctuation intervals in the dynamic sequence, and using the interactive calculation results to call the matrix optimization logic to eliminate duplicate and inefficient items, and obtain the dynamic matrix of energy consumption and carbon emissions; S113: Using the multi-dimensional data in the energy consumption and carbon emission dynamic matrix, construct a feature matrix using the formula: ; Calculate and generate dynamic eigenvalues; in, Represents the dynamic characteristic value of energy consumption and carbon emissions, Represents energy consumption Item data, Represents carbon emissions Item data, It is a nonlinear correction weight coefficient to adjust the impact ratio of energy consumption and carbon emissions. is the normalization adjustment factor used to balance the differentiated data scales of energy consumption and carbon emissions. Represents the total amount of data.

[0009] As a further solution of the present invention, the step of obtaining the trend dynamic coefficient value is specifically as follows: S211: extracting time series data from the dynamic characteristic value, identifying the trend of energy consumption change by comparing the corresponding time points of energy consumption and emission quota, calculating the energy consumption fluctuation amplitude and direction of the differentiated intervals in the time series based on sliding window analysis, and generating trend change data of the time series; S212: using the trend change data of the time series, calculating the rate of change of the energy consumption fluctuation range and the correlation strength of the emission amount in each time period by counting the fluctuation range of energy consumption in multiple intervals, determining the degree of deviation of the change rate and the mean of the correlation strength in the differentiated intervals, adjusting and calculating the weighted value of each trend change, and generating a trend change weight array; S213: Fitting the correlation between energy consumption and time series trend parameters through the trend change weight array, performing regression analysis based on the time period weight and trend parameter influence, using the formula: ; Calculate the influence range of trend change parameters on time series and generate trend dynamic coefficient value; in, Represents the trend dynamic coefficient value, Representative The weight value of each time period, Represents the trend parameter of the corresponding time period, is the attenuation coefficient, which is used to adjust the influence of trend parameters over time. For the The time span from the time period to the current time, is the total number of time periods.

[0010] As a further solution of the present invention, the step of obtaining the path optimization difference value is specifically as follows: S311: Filter the quota distribution records and time span parameters corresponding to the transaction path from the trend dynamic coefficient value, extract the characteristics of the dynamic distribution of the path based on the quota change rate at the time point, calculate the change direction and rate of the quota distribution in the path, and generate dynamic distribution data of the path by adjusting the fluctuation trend of the distribution data record; S312: using the dynamic distribution data of the path, by comparing the fluctuation range of the quota distribution in the differentiated time periods, calculating the change rate of the path time span, determining the fluctuation characteristics according to the difference between the time span change rate and the path quota distribution, analyzing the correlation strength of the transaction path in combination with the weighted distribution parameters in the time period, and generating the dynamic fluctuation range data of the transaction path; S313: Calculate the impact of time span changes on quota volatility through the transaction path dynamic fluctuation range data, and build a path fluctuation optimization model using the formula: ; Generate path optimization difference value; in, represents the path optimization difference value, For the The quota fluctuation amount of the path interval, For the The time span of the path interval, is the fluctuation attenuation coefficient, which adjusts the influence of quota fluctuation on the time span. is the time weight coefficient, balancing the relationship between time span and volatility, is the total number of path intervals.

[0011] As a further solution of the present invention, the step of obtaining the supply-demand balance allocation coefficient is specifically as follows: S411: extracting the distribution change amplitude of supply and demand fluctuation from the path optimization difference value, calculating multiple-segment change rates based on the distribution ratios of multiple time periods in the supply and demand fluctuation, analyzing the relationship between the quota price and the distribution record in the supply and demand data, screening the significant intervals of dynamic distribution changes, and generating supply and demand fluctuation distribution records; S412: Calculate the change range of the allocation coefficient in the supply and demand data through the supply and demand fluctuation allocation record, adjust the allocation record based on the proportional weight of the quota price difference and the time span within the supply and demand fluctuation range, analyze the correlation strength between multiple supply and demand data and price allocation, and generate the supply and demand allocation adjustment weight; S413: Dynamically adjust the supply and demand allocation result through the supply and demand allocation adjustment weight, and calculate the balance relationship between the allocation dynamics and price changes, using the formula: ; Calculate the supply and demand allocation balance value and generate the supply and demand balance allocation coefficient; in, represents the supply-demand equilibrium allocation coefficient, For the The amount of change in the allocation of time periods, is the quota price for the corresponding time period, For the The price adjustment weight of the time period, and the impact of adjusting the quota price on the allocation change, It is the square correction parameter of the change in supply and demand within the time period, which is used to balance the impact of the change in supply and demand on the time span. For the The time span of the time period, For the The price fluctuation weight of the time period is used to correct the dynamic impact of price changes in the two time periods before and after. is the total number of allocated intervals, The quota price for the previous time period.

[0012] As a further solution of the present invention, the step of obtaining the dynamic risk volatility is specifically as follows: S511: Based on the supply and demand balance allocation coefficient, screen the real-time supply and demand fluctuation records that affect the transaction path, extract key time points and quota allocation data from the fluctuation records, calculate the correlation between supply and demand changes and quota allocation at each time point, and generate preliminary analysis data of supply and demand fluctuations; S512: Analyze the cumulative impact of path fluctuations on risk changes through the preliminary analysis data of supply and demand fluctuations, calculate the cumulative risk value of each point, determine the correlation between the fluctuation range and risk changes based on the data of the supply and demand fluctuation range and the allocation coefficient, and generate cumulative risk deviation data; S513: Using the cumulative risk deviation data, evaluate the impact of the cumulative deviation of the supply and demand fluctuation range and the allocation coefficient on the transaction path, using the formula: ; Calculate the correlation weight between the volatility range and dynamic risk to generate the dynamic risk volatility; in, represents the dynamic risk volatility, For the The risk deviation value of each data point measures the actual risk deviation of each data point. Represents the average value of the risk deviation of all data points, which is used to calculate the risk gap between multiple data points and the average value. For the The weight of each data point is used to adjust the impact of each risk point on the overall risk assessment. is the total number of data points.

[0013] A carbon neutrality management system based on digital asset circulation, the carbon neutrality management system based on digital asset circulation is used to execute the above-mentioned carbon neutrality management method based on digital asset circulation, the system includes: The energy dynamic matrix module is based on carbon emission data, energy consumption, gross domestic product and industrial output value. It screens the associated data of energy consumption and carbon emissions, removes outliers and redundant information, calculates the interactive relationship between energy consumption and gross domestic product, establishes a dynamic matrix of energy consumption and carbon emissions, and generates dynamic matrix values ​​of energy consumption and carbon emissions. The trend fitting calculation module is based on the energy consumption and carbon emission dynamic matrix values, screens the dynamic relationship between emission quotas and energy consumption in the time series, calculates the weight value of trend changes within the time period, fits the correlation between energy consumption and trend change parameters, calculates the influence range of trend change parameters on the time series, and generates trend dynamic coefficient values; The path fluctuation analysis module screens the quota distribution records and time spans in the transaction path based on the trend dynamic coefficient value, calculates the dynamic fluctuation range of the path, determines the correlation between the time span change in the path and the quota fluctuation, analyzes the impact of the path fluctuation on the transaction quota, and generates a path optimization difference value; The supply and demand balance adjustment module analyzes the distribution change range in the supply and demand fluctuation based on the path optimization difference value, adjusts the supply and demand data and the distribution coefficient in the quota price, calculates the influence weight of the balance distribution record and the fluctuation range of the supply and demand data, dynamically adjusts the supply and demand distribution results, establishes a dynamic relationship between the supply and demand matching parameters, and generates the supply and demand balance distribution coefficient; The dynamic risk assessment module is based on the supply and demand balance allocation coefficient, screens transaction paths and real-time supply and demand fluctuation records, analyzes the cumulative impact of path fluctuations on risk changes, determines the cumulative deviation between the supply and demand fluctuation range and the allocation coefficient, calculates the correlation weight between the fluctuation range and the dynamic risk change, and generates a dynamic risk volatility.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by eliminating outliers and redundant information in carbon emission data, a dynamic matrix of energy consumption and carbon emissions is accurately established, the interactive analysis capability of multi-dimensional data is strengthened, and the accuracy and application value of data are improved. Combining trend weights with time series fitting, the scope of influence of trend changes is clarified, and the predictive ability of the dynamic association between energy consumption and carbon emissions is effectively enhanced. By analyzing quota distribution records and time span parameters, quota adjustments in trading paths are optimized to improve quota allocation efficiency and flexibility. Supply and demand fluctuation analysis combines quota prices with allocation adjustments to dynamically optimize the supply and demand matching mechanism and improve allocation accuracy and response efficiency. Dynamically calculate fluctuation ranges and risk association weights to achieve real-time assessment and regulation of risk changes and reduce the adverse effects of risk accumulation on carbon trading management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 It is a flow chart of the steps of obtaining the characteristic matrix value of the present invention; Figure 3 A flow chart of the steps for obtaining the trend dynamic coefficient value of the present invention; Figure 4 A flow chart of the steps for obtaining the path optimization difference value of the present invention; Figure 5 A flow chart of the steps for obtaining the supply-demand balance allocation coefficient of the present invention; Figure 6 The figure is a flow chart of the steps for obtaining the dynamic risk volatility of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the 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 therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] Example 1: Please refer to Figure 1The present invention provides a technical solution: a carbon neutrality management method based on the circulation of digital assets, comprising the following steps: S1: Based on carbon emission data, energy consumption, gross domestic product and industrial output value, screen the correlation data between energy consumption and carbon emissions, remove outliers and redundant information, calculate the interactive relationship of multi-dimensional data, establish a dynamic matrix of energy consumption and carbon emissions, and obtain dynamic eigenvalues; S2: Based on the dynamic characteristic value, the dynamic relationship between emission quota and energy consumption in the time series is screened, the weight value of trend change in the differentiated time period is calculated, the correlation between energy consumption and trend parameters is fitted, the influence range of trend change parameters on the time series is calculated, and the trend dynamic coefficient value is generated; S3: Based on the trend dynamic coefficient value, filter the quota distribution records and time span parameters in the transaction path, calculate the dynamic fluctuation range of the path, determine the correlation between the change of the transaction time span in the path and the quota fluctuation, analyze the impact of the path fluctuation on the transaction quota, and generate the path optimization difference value; S4: Based on the path optimization difference value, analyze the distribution change range in the supply and demand fluctuation, adjust the distribution coefficient in the supply and demand data and quota price, calculate the impact weight of the balanced distribution record and the supply and demand fluctuation, dynamically adjust the supply and demand distribution results, establish the dynamic relationship between the supply and demand matching parameters, and generate the supply and demand balance distribution coefficient; S5: Based on the supply and demand balance allocation coefficient, screen the transaction paths and real-time supply and demand fluctuation records, analyze the cumulative impact of path fluctuations on risk changes, determine the cumulative deviation between the supply and demand fluctuation range and the allocation coefficient, calculate the correlation weight between the fluctuation range and dynamic risk, and generate the dynamic risk volatility.

[0019] The carbon emission dynamic matrix specifically includes the energy consumption matrix, the carbon emission matrix, and the characteristic matrix value. The trend dynamic coefficient value includes the trend change weight, the trend parameter correlation relationship, and the time series impact range. The path optimization difference value includes the quota distribution record fluctuation range, the transaction time span variation range, and the transaction quota impact range. The supply and demand balance allocation coefficient includes the supply and demand data adjustment coefficient, the quota price allocation coefficient, and the dynamic relationship between the supply and demand matching parameters. The dynamic risk volatility specifically refers to the fluctuation range correlation weight, the dynamic risk cumulative impact, and the supply and demand fluctuation cumulative deviation.

[0020] See also Figure 2 , the specific steps for obtaining the characteristic matrix value are: S111: extract carbon emission data, energy consumption, gross domestic product and industrial output value, remove null values, duplicate values ​​and logical outliers in the data, and screen valid data pairs with correlation between energy consumption and carbon emissions by calling the difference data in the time series of energy consumption and carbon emissions, and generate a correlation data matrix of energy consumption and carbon emissions; The data is processed step by step, and the null values ​​are eliminated by calculating the missing data rate to determine the elimination range. Columns with missing rates higher than 50% are directly deleted, and columns with missing rates lower than 50% are filled with null values ​​by linear interpolation. Then, duplicate values ​​are eliminated and the data row content is verified and marked by the hash algorithm. Data rows with the same hash value are deleted, and logical outliers are eliminated based on the ratio of energy consumption to gross domestic product. Data rows with a ratio less than 0.1 or greater than 10 are judged as logical outliers and deleted. By comparing the time series of energy consumption and carbon emissions, data segments with consistent time axes and positively correlated fluctuations are screened, and data segments that do not meet the fluctuation consistency conditions are eliminated. The remaining energy consumption and carbon emission data are sorted pair by pair to form a correlation matrix, and a correlation data matrix of energy consumption and carbon emissions is generated.

[0021] S112: Using the correlation data matrix of energy consumption and carbon emissions, calculate the interactive relationship between the data, solve the linear coefficient of the impact of energy consumption on carbon emissions through the weighted ratio of the matrix row and column elements, determine the nonlinear correction coefficient according to the comparison ratio of the fluctuation range in the dynamic sequence, and use the interactive calculation results to call the matrix optimization logic to eliminate duplicate and inefficient items, and obtain the dynamic matrix of energy consumption and carbon emissions; The ratio of energy consumption and carbon emissions is calculated item by item, and the mean and variance of the ratio are calculated. The columns with ratios less than the mean minus two times the variance or greater than the mean plus two times the variance are eliminated. The nonlinear correction coefficient is calculated based on the carbon emission fluctuation range. The rate of change within the range is extracted by the sliding window method and compared with the energy consumption fluctuation rate to determine the nonlinear adjustment ratio. The matrix is ​​optimized and scaled by normalizing the data weights column by column to eliminate the influence of unit differences and numerical fluctuations. The weights of each element in the matrix are readjusted by calling the linear coefficient and nonlinear correction coefficient results to finally generate a dynamic matrix of energy consumption and carbon emissions.

[0022] S113: Use the multi-dimensional data in the dynamic matrix of energy consumption and carbon emissions to construct a feature matrix using the formula: ; Calculate and generate dynamic eigenvalues; in, Represents the dynamic characteristic value of energy consumption and carbon emissions, Represents energy consumption Item data, Represents carbon emissions Item data, It is a nonlinear correction weight coefficient to adjust the impact ratio of energy consumption and carbon emissions. is the normalization adjustment factor used to balance the differentiated data scales of energy consumption and carbon emissions. Represents the total amount of data.

[0023] formula: ; The benefit of the formula is that by introducing nonlinear correction weight coefficients and the normalized adjustment factor , so that the dynamic characteristic values ​​of energy consumption and carbon emissions are more adapted to the complex relationship of actual data, and the accuracy of calculation and the adaptability of data are improved.

[0024] Detailed explanation of the formula and the process of formula calculation and derivation: , based on the data adjustment, , through weight optimization adjustment range setting, assuming that the energy consumption data is , the carbon emission data is , put it into the formula: ; Calculate the numerator first: ; Numerator sum: ; Then calculate the denominator: ; ; Substituting the numerator and denominator into the formula: ; The results show that the dynamic characteristic value between energy consumption and carbon emissions is 38.07, which reflects the comprehensive influence between the two in the system and can be further used to optimize the energy structure or predict future carbon emission levels.

[0025] See also Figure 3 , the steps to obtain the trend dynamic coefficient value are as follows: S211: extracting time series data from the dynamic characteristic values, identifying the trend of energy consumption changes by comparing the corresponding time points of energy consumption and emission quotas, calculating the energy consumption fluctuation amplitude and direction of the differentiated intervals in the time series based on sliding window analysis, and generating trend change data of the time series; First, based on the fluctuation range of energy consumption data and emission quota data, the corresponding ratio of the change rate of energy consumption to the emission quota is calculated respectively. The data in each time period is grouped and analyzed by setting a sliding window. The difference of energy consumption is calculated in the sliding window and the fluctuation range of energy consumption is measured by combining the ratio of the minimum value to the maximum value of each group of data. Then, according to the corresponding ratio of emission quota and energy consumption, the change trend in different time periods is quantified. The direction of the trend is indicated by the mean deviation of the ratio in each section. At the same time, the growth or decrease rate of energy consumption in each time period is recorded. A set of trend change sequences is formed through the segment-by-segment analysis of the overall time series. Finally, the fluctuation results of each segment are combined according to the time series to generate the trend change data of the time series.

[0026] S212: using the trend change data of the time series, by statistically analyzing the correlation strength between the fluctuation range of energy consumption and the emission in multiple intervals in each time period, calculating the change rate of the fluctuation range of energy consumption, determining the degree of deviation between the mean of the change rate and the correlation strength in the differentiated intervals, adjusting and calculating the weighted value of each trend change, and generating a trend change weight array; First, by calculating the impact of each fluctuation amplitude on the change in emissions, the impact ratios in different time periods are obtained in turn, and the change rate of energy consumption is weighted in sections using the weighted calculation method. The deviation between the volatility and intensity in the time period is calculated by combining the ratio of the trend change direction and the fluctuation amplitude. The proportion of the weight value is corrected by combining the standardized processing of the data, and a complete set of trend change weight value arrays are generated through segment-by-segment calculations to generate a trend change weight array.

[0027] S213: Fit the correlation between energy consumption and time series trend parameters through the trend change weight array, and perform regression analysis based on the time period weight and trend parameter influence, using the formula: ; Calculate the influence range of trend change parameters on time series and generate trend dynamic coefficient value; in, Represents the trend dynamic coefficient value, Representative The weight value of each time period, Represents the trend parameter of the corresponding time period, is the attenuation coefficient, which is used to adjust the influence of trend parameters over time. For the The time span from the time period to the current time, is the total number of time periods.

[0028] formula: ; The benefit of the formula is that by introducing the attenuation coefficient and weight value The combined operation makes the influence of trend parameters on time span show a nonlinear decreasing characteristic, which improves the adaptability and flexibility of the model to long-term trend changes.

[0029] Detailed explanation of the formula and the process of formula calculation and derivation: : Divide the time series into 5 time periods; Set the weight value of the first time period , weight value of the second time period , weight value of the third time period , weight value of the fourth time period , weight value of the fifth time period ; Assume that the corresponding trend parameters are , , , , ; Assume that the time decay coefficient , the time spans are , , , , .

[0030] calculate: ; ; ; ; This result shows that the trend dynamic coefficient value It is 1.8335, indicating that the correlation intensity of the trend change in the current time series to energy consumption has a certain nonlinear attenuation characteristic, and this value can be used for the subsequent dynamic adjustment process of energy consumption.

[0031] See also Figure 4 , the specific steps for obtaining the path optimization difference value are: S311: Filter the quota distribution records and time span parameters corresponding to the transaction path from the trend dynamic coefficient value, extract the dynamic distribution characteristics of the path based on the quota change rate at the time point, calculate the change direction and rate of the quota distribution in the path, and generate dynamic distribution data of the path by adjusting the fluctuation trend of the distribution data record; By extracting the quota change rate at each time point, calling the transaction time point data in the quota distribution record, dividing the time span into multiple sub-segments according to the interval, based on the change characteristics of each transaction node in the time span interval, by weighted accumulation of the absolute value of the quota change, calculate the correlation data between the quota change rate and the time span, and at the same time mark the fluctuation direction of each quota distribution, calculate the ratio of the change amplitude between the time points, mark the quota volatility above the threshold as a significant fluctuation value, and compare the significant fluctuation value with the quota volatility by calling the associated interval range in the time span distribution. Combined with the cumulative value within the quota and time distribution range, the interval with volatility lower than the average is eliminated, and the dynamic trend change rate is obtained through the gradually adjusted data analysis to generate the dynamic distribution data of the path.

[0032] S312: using the dynamic distribution data of the path, by comparing the fluctuation range of the quota distribution in the differentiated time periods, calculating the change rate of the path time span, determining the fluctuation characteristics according to the difference between the time span change rate and the path quota distribution, analyzing the correlation strength of the transaction path in combination with the weighted distribution parameters in the time period, and generating the dynamic fluctuation range data of the transaction path; By extracting the time span and quota fluctuation range of each interval of the path, comparing the fluctuation range proportion in each time period, calling the significant fluctuation value marked in the path dynamic data, and statistically analyzing the intervals with significant fluctuations, the distribution mean and standard deviation of the fluctuation value are calculated, and the interval with a lower distribution mean is determined as a low fluctuation area, and the interval with a larger standard deviation is determined as a high fluctuation area. The ratio formula of volatility and significant value is used for calculation. At the same time, the time span of the path interval fluctuation is cumulatively analyzed, and the adjustment coefficient of the time span is used to correct the dynamic impact of the interval fluctuation on the path as a whole. The final dynamic fluctuation range is generated through the dynamic distribution trend to obtain the dynamic fluctuation range data of the transaction path.

[0033] S313: Calculate the impact of time span changes on quota volatility through transaction path dynamic fluctuation range data, and build a path fluctuation optimization model using the formula: ; Generate path optimization difference value; in, represents the path optimization difference value, For the The quota fluctuation amount of the path interval, For the The time span of the path interval, is the fluctuation attenuation coefficient, which adjusts the influence of quota fluctuation on the time span. is the time weight coefficient, balancing the relationship between time span and volatility, is the total number of path intervals.

[0034] formula: ; The benefit of the formula is that by introducing the time span attenuation factor and the balance time weight coefficient , which can dynamically adjust the impact weights of quota fluctuations in different time intervals in the path, effectively reduce the error accumulation caused by long time spans, and at the same time enhance the sensitivity to significant fluctuations in a short time.

[0035] Detailed explanation of the formula and the process of formula calculation and derivation: Parameter introduction: represents the path optimization difference value, For the The quota fluctuation amount of the path interval, For the The time span of the path interval, is the fluctuation attenuation coefficient, which adjusts the influence of quota fluctuation on the time span. is the time weight coefficient, balancing the relationship between time span and volatility, is the total number of path intervals, and the summation symbol Indicates the cumulative sum of all path intervals.

[0036] Parameter acquisition method: It is obtained by calculating the difference between the initial value and the end value of the quota in each interval of the path, for example, extracting the difference between the two end points from the monitored quota data record; It is obtained directly by recording the time difference between two time nodes in the transaction path. For example, if the path interval is from January 1, 2023 to January 7, 2023, the time span is 6 days; and Adjustments are made based on the dynamic characteristics of the path data. The correction coefficient is set by the overall trend change within the path fluctuation range. The specific value is determined by the distribution characteristics of the path data. For example, the time weight coefficient of the high fluctuation interval is set to a larger value than that of the low fluctuation interval.

[0037] Calculation formula: Assume the path has 3 intervals: ; ; .

[0038] The calculation formula is as follows: ; First interval: ; Second interval: ; The third interval: ; sum: ; The result shows that the path optimization difference value is 16.33, which indicates that the volatility characteristics of the transaction path can effectively measure the dynamic distribution changes of the overall path under the correction of the time span, which can be used for subsequent path optimization and trading strategy adjustment.

[0039] See also Figure 5 , the specific steps for obtaining the supply and demand balance allocation coefficient are: S411: extracting the distribution change range of supply and demand fluctuations from the path optimization difference value, calculating the multi-segment change rate based on the distribution ratio of multiple time periods in the supply and demand fluctuations, analyzing the relationship between the quota price and the distribution record in the supply and demand data, screening the significant interval of dynamic distribution change, and generating the supply and demand fluctuation distribution record; Based on the allocation data within the time period of the path optimization difference value, the allocation ratio change rate of each time period is calculated one by one. The absolute value function is used to calculate the allocation difference between the time point and the previous time point and the average is taken to obtain the allocation change rate. By setting the change rate threshold, the significant allocation fluctuation interval is screened out. For each fluctuation interval, the proportion of time points whose change rate exceeds the set threshold is calculated to determine whether it meets the requirements of the significant fluctuation interval. Assuming that the number of points in an interval where the allocation difference is greater than the average difference reaches more than 50%, the interval is determined to be a significant fluctuation interval. For the screened significant fluctuation interval, the maximum, minimum and average values ​​of the allocation change rate are further extracted, and these values ​​are compared to confirm the fluctuation characteristics in each significant interval. For a certain time interval, the maximum value of the allocation change rate is 0.15, the minimum value is 0.02, and the average value is 0.08. Through the determination of these characteristic values, the complete significant interval characteristics are obtained, and the supply and demand fluctuation allocation record is generated.

[0040] S412: Calculate the change range of the allocation coefficient in the supply and demand data through the supply and demand fluctuation allocation record, adjust the allocation record based on the proportional weight of the quota price difference and the time span within the supply and demand fluctuation range, analyze the correlation strength between multiple supply and demand data and price allocation, and generate the supply and demand allocation adjustment weight; The overall fluctuation range value is calculated by the allocation change rate of each fluctuation range. Combined with the quota price difference data of the fluctuation range, the allocation change value is adjusted using the time span proportion weight. For example, for the fluctuation range of a certain range, the weight value is calculated proportionally in combination with the price difference data. For each segment of data, the fluctuation range is multiplied by the weight value to obtain the adjusted allocation range value. For a fluctuation range of 0.12, the weight value is 1.5, and the adjusted value is 0.18. Repeat this process to calculate the adjusted allocation values ​​of all fluctuation ranges, and calculate the overall correlation strength value by weighted summation of all adjusted values. For the weight value, it is allocated according to the time span ratio of each range, and the weight value is calculated according to the time span ratio. For range 1, the time span ratio is 30%, and the weight value is 0.3. The supply and demand allocation adjustment weight is generated by the value calculated by all weights and allocation changes.

[0041] S413: Dynamically adjust the supply and demand allocation results by adjusting the weights of supply and demand allocation, and calculate the balance between allocation dynamics and price changes using the formula: ; Calculate the supply and demand allocation balance value and generate the supply and demand balance allocation coefficient; in, represents the supply-demand equilibrium allocation coefficient, For the The amount of change in the allocation of time periods, is the quota price for the corresponding time period, For the The price adjustment weight of the time period, and the impact of adjusting the quota price on the allocation change, It is the square correction parameter of the change in supply and demand within the time period, which is used to balance the impact of the change in supply and demand on the time span. For the The time span of the time period, For the The price fluctuation weight of the time period is used to correct the dynamic impact of price changes in the two time periods before and after. is the total number of allocated intervals, The quota price for the previous time period.

[0042] formula: ; The formula is useful in that by adding the distribution change , Price volatility weight , price adjustment weight Parameters such as and time span correction parameters , which can dynamically balance the impact of quota price and time span on the supply and demand allocation coefficient.

[0043] Detailed explanation of the formula and the process of formula calculation and derivation: First obtain the Time period allocation change , obtained by the segmented change value of the supply and demand allocation record, assuming ; Quota Price By collecting the transaction prices in each time period, assuming , corresponding to the price in the previous period ; Weight Parameters Calculated based on time span and price fluctuation respectively, assuming ; Substitute the formula term by term: ; ; Take all the allocated intervals Values ​​and sum: ; This result shows that the supply-demand equilibrium allocation coefficient calculated by the formula It is 330.28, which indicates the combined impact of factors such as time span, price fluctuations and distribution changes on the overall supply and demand balance.

[0044] See also Figure 6 , the specific steps for obtaining dynamic risk volatility are: S511: Based on the supply and demand balance allocation coefficient, screen the real-time supply and demand fluctuation records that affect the transaction path, extract key time points and quota allocation data from the fluctuation records, calculate the correlation between supply and demand changes and quota allocation at each time point, and generate preliminary analysis data of supply and demand fluctuations; By comparing the supply and demand change data and the time series of quota allocation records at different time points, the change rate of quota allocation is analyzed from the supply and demand change at each time point, and its distribution at different time points is further calculated based on the fluctuation value of the allocation record. By defining the relationship formula between allocation change and supply and demand change, the change amplitude of the allocation value and the time series are standardized. The formula is: in, represents the rate of change of supply and demand, Indicates the change value of supply and demand data, It represents the time span of the change. The rate of change in each time period is calculated through the supply and demand fluctuations and the time interval. After calculating the rate of change in each time period, the standardized data of the allocation record is normalized to the range of [0,1] to facilitate the subsequent fluctuation analysis and calculation of the cumulative deviation. Through this process, the preliminary analysis results of supply and demand changes and quota allocation are formed, and the preliminary analysis data of supply and demand fluctuations are generated.

[0045] S512: Analyze the cumulative impact of path fluctuations on risk changes through preliminary analysis data of supply and demand fluctuations, calculate the cumulative risk value of each point, determine the correlation between fluctuation amplitude and risk changes based on the data of supply and demand fluctuation amplitude and allocation coefficient, and generate cumulative risk deviation data; By extracting the cumulative change value of key supply and demand fluctuation points in the preliminary analysis data, combined with the supply and demand change rate and fluctuation distribution data at each time point, the following calculation formula is used: in, represents the cumulative fluctuation impact value, For the The rate of change of supply and demand in the time period, Indicates The weight of quota allocation within a time period is calculated in segments. The changing rate of supply and demand fluctuations in each time period is matched with the quota allocation weight to obtain the fluctuation impact value of each time period. The total fluctuation impact is then obtained by adding them up item by item. This process clarifies the correlation between the fluctuation range of supply and demand and the fluctuation of allocation records, thereby generating cumulative risk deviation data.

[0046] S513: Using the cumulative risk deviation data, evaluate the impact of the cumulative deviation of supply and demand fluctuations and the allocation coefficient on the transaction path, using the formula: ; Calculate the correlation weight between the volatility range and dynamic risk to generate the dynamic risk volatility; in, represents the dynamic risk volatility, For the The risk deviation value of each data point measures the actual risk deviation of each data point. Represents the average value of the risk deviation of all data points, which is used to calculate the risk gap between multiple data points and the average value. For the The weight of each data point is used to adjust the impact of each risk point on the overall risk assessment. is the total number of data points.

[0047] formula: ; The benefit of the formula is that by introducing the product of the square root of the deviation value and the weight, the formula can better highlight the contribution of data points with larger risk deviations in the overall risk volatility, thereby more accurately reflecting the imbalance of risk distribution.

[0048] Detailed explanation of the formula and the process of formula calculation and derivation: Assume that the risk bias data extracted from the supply and demand fluctuation records is , its mean By formula: ; Calculated ; Get weight data , substitute each term into the formula to calculate the numerator: ; Calculate item by item: ; ; ; ; Numerator sum: ; Denominator calculation: ; The final formula is: ; The results show that the value of the dynamic risk volatility is 1.572, which directly reflects the relationship between the risk deviation level in the supply and demand volatility data and the cumulative risk after weight adjustment. The result can be further used as a key parameter input for the dynamic risk management strategy to optimize risk distribution and management methods.

[0049] A carbon neutrality management system based on digital asset circulation, the carbon neutrality management system based on digital asset circulation is used to execute the above-mentioned carbon neutrality management method based on digital asset circulation, the system includes: The energy dynamic matrix module is based on carbon emission data, energy consumption, gross domestic product and industrial output value. It screens the associated data of energy consumption and carbon emissions, removes outliers and redundant information, calculates the interactive relationship between energy consumption and gross domestic product, establishes a dynamic matrix of energy consumption and carbon emissions, and generates dynamic matrix values ​​of energy consumption and carbon emissions. The trend fitting calculation module is based on the dynamic matrix values ​​of energy consumption and carbon emissions, screens the dynamic relationship between emission quotas and energy consumption in the time series, calculates the weight value of trend changes within the time period, fits the correlation between energy consumption and trend change parameters, calculates the influence range of trend change parameters on the time series, and generates trend dynamic coefficient values; The path fluctuation analysis module screens the quota distribution records and time spans in the transaction path based on the trend dynamic coefficient value, calculates the dynamic fluctuation range of the path, determines the correlation between the time span change in the path and the quota fluctuation, analyzes the impact of the path fluctuation on the transaction quota, and generates the path optimization difference value; The supply and demand balance adjustment module analyzes the distribution change range in supply and demand fluctuations based on the path optimization difference value, adjusts the supply and demand data and the distribution coefficient in the quota price, calculates the influence weight of the balance distribution record and fluctuation range of the supply and demand data, dynamically adjusts the supply and demand distribution results, establishes the dynamic relationship between the supply and demand matching parameters, and generates the supply and demand balance distribution coefficient; The dynamic risk assessment module is based on the supply and demand balance allocation coefficient, screens transaction paths and real-time supply and demand fluctuation records, analyzes the cumulative impact of path fluctuations on risk changes, determines the cumulative deviation between the supply and demand fluctuation range and the allocation coefficient, calculates the correlation weight between the fluctuation range and dynamic risk changes, and generates dynamic risk volatility.

[0050] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A carbon neutrality management method based on digital asset circulation, characterized in that: The following steps are involved: S1: Based on carbon emission data, energy consumption, gross domestic product and industrial output value, screen the correlation data between energy consumption and carbon emissions, remove outliers and redundant information, calculate the interactive relationship of multi-dimensional data, establish a dynamic matrix of energy consumption and carbon emissions, and obtain dynamic eigenvalues; S2: Based on the dynamic characteristic value, the dynamic relationship between emission quota and energy consumption in the time series is screened, the weight value of trend change in the differentiated time period is calculated, the correlation relationship between energy consumption and trend parameters is fitted, the influence range of trend change parameters on the time series is calculated, and the trend dynamic coefficient value is generated; S3: Based on the trend dynamic coefficient value, filter the quota distribution records and time span parameters in the transaction path, calculate the dynamic fluctuation range of the path, determine the correlation between the change of the transaction time span in the path and the quota fluctuation, analyze the impact of the path fluctuation on the transaction quota, and generate the path optimization difference value; S4: Based on the path optimization difference value, analyze the distribution change range in the supply and demand fluctuation, adjust the supply and demand data and the distribution coefficient in the quota price, calculate the impact weight of the balanced distribution record and the supply and demand fluctuation, dynamically adjust the supply and demand distribution results, establish a dynamic relationship between the supply and demand matching parameters, and generate the supply and demand balance distribution coefficient; S5: Based on the supply and demand balance allocation coefficient, screen the transaction path and the real-time supply and demand fluctuation records, analyze the cumulative impact of path fluctuations on risk changes, determine the cumulative deviation between the supply and demand fluctuation range and the allocation coefficient, calculate the correlation weight between the fluctuation range and the dynamic risk, and generate the dynamic risk volatility.

2. The carbon neutrality management method based on digital asset circulation according to claim 1 is characterized in that: The carbon emission dynamic matrix specifically includes the energy consumption matrix, the carbon emission matrix, and the characteristic matrix value. The trend dynamic coefficient value includes the trend change weight, the trend parameter correlation relationship, and the time series influence range. The path optimization difference value includes the quota distribution record fluctuation range, the transaction time span variation range, and the transaction quota impact range. The supply and demand balance allocation coefficient includes the supply and demand data adjustment coefficient, the quota price allocation coefficient, and the dynamic relationship between the supply and demand matching parameters. The dynamic risk volatility specifically refers to the fluctuation range correlation weight, the dynamic risk cumulative impact, and the supply and demand fluctuation cumulative deviation.

3. The carbon neutrality management method based on digital asset circulation according to claim 2 is characterized in that: The steps for obtaining the characteristic matrix value are specifically as follows: S111: extract carbon emission data, energy consumption, gross domestic product and industrial output value, remove null values, duplicate values ​​and logical outliers in the data, and screen valid data pairs with correlation between energy consumption and carbon emissions by calling the difference data in the time series of energy consumption and carbon emissions, and generate a correlation data matrix of energy consumption and carbon emissions; S112: using the correlation data matrix of energy consumption and carbon emissions, calculating the interaction relationship between data, solving the linear coefficient of energy consumption affecting carbon emissions through the weighted ratio of matrix row and column elements, determining the nonlinear correction coefficient according to the comparison ratio of fluctuation intervals in the dynamic sequence, and using the interactive calculation results to call the matrix optimization logic to eliminate duplicate and inefficient items, and obtain the dynamic matrix of energy consumption and carbon emissions; S113: Using the multi-dimensional data in the energy consumption and carbon emission dynamic matrix, construct a feature matrix using the formula: ； Calculate and generate dynamic eigenvalues; in, Represents the dynamic characteristic value of energy consumption and carbon emissions, Represents energy consumption Item data, Represents carbon emissions Item data, It is a nonlinear correction weight coefficient to adjust the impact ratio of energy consumption and carbon emissions. is the normalization adjustment factor used to balance the differentiated data scales of energy consumption and carbon emissions. Represents the total amount of data.

4. The carbon neutrality management method based on digital asset circulation according to claim 3 is characterized in that: The steps for obtaining the trend dynamic coefficient value are specifically as follows: S211: extracting time series data from the dynamic characteristic value, identifying the trend of energy consumption change by comparing the corresponding time points of energy consumption and emission quota, calculating the energy consumption fluctuation amplitude and direction of the differentiated intervals in the time series based on sliding window analysis, and generating trend change data of the time series; S212: using the trend change data of the time series, calculating the rate of change of the energy consumption fluctuation range and the correlation strength of the emission amount in each time period by counting the fluctuation range of energy consumption in multiple intervals, determining the degree of deviation of the change rate and the mean of the correlation strength in the differentiated intervals, adjusting and calculating the weighted value of each trend change, and generating a trend change weight array; S213: Fitting the correlation between energy consumption and time series trend parameters through the trend change weight array, performing regression analysis based on the time period weight and trend parameter influence, using the formula: ； Calculate the influence range of trend change parameters on time series and generate trend dynamic coefficient value; in, Represents the trend dynamic coefficient value, Representative The weight value of each time period, Represents the trend parameter of the corresponding time period, is the attenuation coefficient, which is used to adjust the influence of trend parameters over time. For the The time span from the time period to the current time, is the total number of time periods.

5. The carbon neutrality management method based on digital asset circulation according to claim 4 is characterized in that: The steps for obtaining the path optimization difference value are specifically as follows: S311: Filter the quota distribution records and time span parameters corresponding to the transaction path from the trend dynamic coefficient value, extract the characteristics of the dynamic distribution of the path based on the quota change rate at the time point, calculate the change direction and rate of the quota distribution in the path, and generate dynamic distribution data of the path by adjusting the fluctuation trend of the distribution data record; S312: using the dynamic distribution data of the path, by comparing the fluctuation range of the quota distribution in the differentiated time periods, calculating the change rate of the path time span, determining the fluctuation characteristics according to the difference between the time span change rate and the path quota distribution, analyzing the correlation strength of the transaction path in combination with the weighted distribution parameters in the time period, and generating the dynamic fluctuation range data of the transaction path; S313: Calculate the impact of time span changes on quota volatility through the transaction path dynamic fluctuation range data, and build a path fluctuation optimization model using the formula: ； Generate path optimization difference value; in, represents the path optimization difference value, For the The quota fluctuation amount of the path interval, For the The time span of the path interval, is the fluctuation attenuation coefficient, which adjusts the influence of quota fluctuation on the time span. is the time weight coefficient, balancing the relationship between time span and volatility, is the total number of path intervals.

6. The carbon neutrality management method based on digital asset circulation according to claim 5 is characterized in that: The steps for obtaining the supply-demand balance allocation coefficient are specifically as follows: S411: extracting the distribution change amplitude of supply and demand fluctuation from the path optimization difference value, calculating multiple-segment change rates based on the distribution ratios of multiple time periods in the supply and demand fluctuation, analyzing the relationship between the quota price and the distribution record in the supply and demand data, screening the significant intervals of dynamic distribution changes, and generating supply and demand fluctuation distribution records; S412: Calculate the change range of the allocation coefficient in the supply and demand data through the supply and demand fluctuation allocation record, adjust the allocation record based on the proportional weight of the quota price difference and the time span within the supply and demand fluctuation range, analyze the correlation strength between multiple supply and demand data and price allocation, and generate the supply and demand allocation adjustment weight; S413: Dynamically adjust the supply and demand allocation result through the supply and demand allocation adjustment weight, and calculate the balance relationship between the allocation dynamics and price changes, using the formula: ； Calculate the supply and demand allocation balance value and generate the supply and demand balance allocation coefficient; in, represents the supply-demand equilibrium allocation coefficient, For the The amount of change in the allocation of time periods, is the quota price for the corresponding time period, For the The price adjustment weight of the time period, and the impact of adjusting the quota price on the allocation change, It is the square correction parameter of the change in supply and demand within the time period, which is used to balance the impact of the change in supply and demand on the time span. For the The time span of the time period, For the The price fluctuation weight of the time period is used to correct the dynamic impact of price changes in the two time periods before and after. is the total number of allocated intervals, The quota price for the previous time period.

7. The carbon neutrality management method based on digital asset circulation according to claim 6 is characterized in that: The steps for obtaining the dynamic risk volatility are specifically as follows: S511: Based on the supply and demand balance allocation coefficient, screen the real-time supply and demand fluctuation records that affect the transaction path, extract key time points and quota allocation data from the fluctuation records, calculate the correlation between supply and demand changes and quota allocation at each time point, and generate preliminary analysis data of supply and demand fluctuations; S512: Analyze the cumulative impact of path fluctuations on risk changes through the preliminary analysis data of supply and demand fluctuations, calculate the cumulative risk value of each point, determine the correlation between the fluctuation range and risk changes based on the data of the supply and demand fluctuation range and the allocation coefficient, and generate cumulative risk deviation data; S513: Using the cumulative risk deviation data, evaluate the impact of the cumulative deviation of the supply and demand fluctuation range and the allocation coefficient on the transaction path, using the formula: ； Calculate the correlation weight between the volatility range and dynamic risk to generate the dynamic risk volatility; in, represents the dynamic risk volatility, For the The risk deviation value of each data point measures the actual risk deviation of each data point. Represents the average value of the risk deviation of all data points, which is used to calculate the risk gap between multiple data points and the average value. For the The weight of each data point is used to adjust the impact of each risk point on the overall risk assessment. is the total number of data points.

8. A carbon neutrality management system based on digital asset circulation, characterized in that: According to the carbon neutrality management method based on digital asset circulation according to any one of claims 1 to 7, the system comprises: The energy dynamic matrix module is based on carbon emission data, energy consumption, gross domestic product and industrial output value. It screens the associated data of energy consumption and carbon emissions, removes outliers and redundant information, calculates the interactive relationship between energy consumption and gross domestic product, establishes a dynamic matrix of energy consumption and carbon emissions, and generates dynamic matrix values ​​of energy consumption and carbon emissions. The trend fitting calculation module is based on the energy consumption and carbon emission dynamic matrix values, screens the dynamic relationship between emission quotas and energy consumption in the time series, calculates the weight value of trend changes within the time period, fits the correlation between energy consumption and trend change parameters, calculates the influence range of trend change parameters on the time series, and generates trend dynamic coefficient values; The path fluctuation analysis module screens the quota distribution records and time spans in the transaction path based on the trend dynamic coefficient value, calculates the dynamic fluctuation range of the path, determines the correlation between the time span change in the path and the quota fluctuation, analyzes the impact of the path fluctuation on the transaction quota, and generates a path optimization difference value; The supply and demand balance adjustment module analyzes the distribution change range in the supply and demand fluctuation based on the path optimization difference value, adjusts the supply and demand data and the distribution coefficient in the quota price, calculates the influence weight of the balance distribution record and the fluctuation range of the supply and demand data, dynamically adjusts the supply and demand distribution results, establishes a dynamic relationship between the supply and demand matching parameters, and generates the supply and demand balance distribution coefficient; The dynamic risk assessment module is based on the supply and demand balance allocation coefficient, screens transaction paths and real-time supply and demand fluctuation records, analyzes the cumulative impact of path fluctuations on risk changes, determines the cumulative deviation between the supply and demand fluctuation range and the allocation coefficient, calculates the correlation weight between the fluctuation range and the dynamic risk change, and generates a dynamic risk volatility.

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