A carbon emission real-time monitoring method based on internet of things and edge computing

Abstract

The application relates to the technical field of carbon emission monitoring, and discloses a carbon emission real-time monitoring method based on an Internet of Things and edge computing, which comprises the following steps: calculating a delay time according to fluid physical properties, translating a gas concentration time axis to obtain synchronous reference carbon emission mass flow after alignment; combining an electric power change rate to extract features, calculating transient compensation carbon emission mass flow; evaluating a processor thermal saturation index according to an energy consumption constant, measuring a communication packet loss ratio and reconstructing and recovering real-time carbon emission mass flow; finally, introducing a topological carbon balance difference between multi-node accounting data and main exhaust port measured data, and distributing errors according to proportions.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, specifically a real-time carbon emission monitoring method based on the Internet of Things and edge computing. Background Technology

[0002] Industrial production lines often acquire heterogeneous physical data from multiple sources by deploying gas flow sensors, concentration sensors, and power meters. Distributed edge computing nodes aggregate this data for regional carbon quota management. Existing real-time monitoring systems mostly employ conventional sampling and analysis algorithms with fixed cycles. In emission pipelines with a certain physical length and cross-sectional area, there is an objective lag in fluid transmission from the exhaust point to the monitoring point. Current technologies often directly combine flow and concentration data under the same hardware clock cycle. This ignores the time axis misalignment of heterogeneous data caused by differences in fluid velocity and sensor location. This misalignment causes phase deviations in the calculation of baseline carbon flow. In addition, industrial production equipment is not in an absolutely continuous and stable operating state. Frequent start-ups and shutdowns or load changes can cause sudden changes in operating power. Existing real-time monitoring technologies mostly rely on conventional time window averaging algorithms for data aggregation. This averaging algorithm erases the sudden fluctuations in carbon emissions caused by industrial equipment during start-ups and shutdowns. This leads to the loss of key transient emission characteristic data. To effectively capture such transient data, monitoring systems must increase the sampling and calculation frequency. Such high-frequency sudden data changes will cause a surge in the computing load on edge computing nodes. Existing technologies typically lack a physical correlation between edge hardware computing power consumption and system communication status. This ignores processor thermal saturation caused by computing power overload. This thermal saturation directly leads to random communication data packet loss. Existing methods struggle to predict and reverse-engineer missing data caused by packet loss. In long-term operation, continuous abnormal data compensation and minor errors from individual monitoring nodes accumulate. This accumulated bias from isolated nodes further impacts the entire pipeline network. The existing monitoring system lacks a verification and correction mechanism for multi-node topology balancing. This easily leads to topological imbalances in regional carbon quota accounting over long periods. This imbalance causes a disconnect between the sum of accounting data reported by each monitoring node and the measured data from the main discharge outlet.

[0003] Therefore, a real-time carbon emission monitoring method based on the Internet of Things and edge computing is needed. Summary of the Invention

[0004] This invention provides a real-time carbon emission monitoring method based on the Internet of Things and edge computing, which helps to solve the problems mentioned in the background art.

[0005] This invention provides the following technical solution: a method for real-time monitoring of carbon emissions based on the Internet of Things and edge computing, comprising: The fluid transport delay time is calculated from the physical length of the pipe, the cross-sectional area of ​​the pipe, and the gas volumetric flow rate. The original gas concentration is shifted according to the fluid transport delay time, and the synchronous baseline carbon emission mass flow rate is calculated in combination with the gas volume flow rate. The rate of change of device power is calculated based on real-time operating power, device operating power at the previous sampling time, and sampling interval. The transient compensation carbon emission mass flow rate is calculated by correcting the synchronous benchmark carbon emission mass flow rate based on the equipment's power change rate, transient reference time constant, and real-time operating power. The processor thermal saturation index is calculated based on the edge processor energy consumption constant, the transient compensation carbon emission mass flow rate, and the rated heat dissipation power. The proportion of communication data loss is calculated from the processor's thermal saturation index, and the real-time carbon emission mass flow rate is calculated and restored based on the transient compensation carbon emission mass flow rate. The cumulative carbon quota usage of the current node is calculated by summing the real-time carbon emission mass flow rate as described above, and the topological carbon balance difference is calculated by combining the measured cumulative carbon mass at the main emission outlet with the cumulative carbon quota usage of each monitoring node. Based on the topological carbon balance difference and the cumulative carbon quota usage of each monitoring node, the final adjusted carbon quota consumption is calculated by correcting the cumulative carbon quota usage of the current node.

[0006] Optionally, the calculation of fluid transport delay time based on the physical length of the pipe, the cross-sectional area of ​​the pipe, and the gas volumetric flow rate includes: Edge computing nodes, gas flow sensors, concentration sensors, power meters, and main outlet monitors; The gas flow sensor and the concentration sensor are installed at a fixed section of the discharge pipe; The power meter is installed at the power supply end of the associated production equipment; The main discharge outlet monitoring device is installed at the main discharge outlet; The edge computing node is communicatively connected to the gas flow sensor, the concentration sensor, the power meter, and the main discharge port monitor via an industrial bus. Obtain the physical length and cross-sectional area of ​​the pipe, and simultaneously obtain the gas volumetric flow rate; The volume of the monitoring section of the pipeline is obtained by multiplying the physical length of the pipeline by the cross-sectional area of ​​the pipeline. The fluid transport delay time is obtained by dividing the volume of the pipeline monitoring section by the gas volume flow rate.

[0007] Optionally, the step of shifting the original gas concentration according to the fluid transport delay time and calculating the synchronous baseline carbon emission mass flow rate in conjunction with the gas volumetric flow rate includes: Obtain the original gas concentration; The time axis of the original gas concentration is shifted and offset according to the fluid transport delay time to obtain the aligned synchronous gas concentration. The synchronous baseline carbon emission mass flow rate is obtained by multiplying the gas volume flow rate by the aligned synchronous gas concentration.

[0008] Optionally, the calculation of the device power change rate based on the real-time operating power, the device operating power at the previous sampling time, and the sampling interval includes: Set the sampling interval time; Obtain the current real-time operating power of the associated device and the operating power of the device at the previous sampling time; The absolute difference in work done by the device is obtained by subtracting the device's operating power at the previous sampling time from the current real-time operating power. The rate of change of electrical power of the device is obtained by dividing the absolute difference in work done by the sampling interval.

[0009] Optionally, the step of correcting the synchronous baseline carbon emission mass flow rate to calculate the transient compensation carbon emission mass flow rate using the device's power change rate, transient reference time constant, and real-time operating power includes: Set the transient reference time constant; Extract the absolute value of the rate of change of the electrical power of the device; Multiply the absolute value of the rate of change of the electrical power of the device by the transient reference time constant; The result of multiplication is divided by the current real-time operating power to obtain the transient compensation multiplier coefficient. Add the constant 1 to the transient compensation multiplier coefficient; The summation result is multiplied by the synchronous baseline carbon emission mass flow rate to obtain the transient compensated carbon emission mass flow rate.

[0010] Optionally, the step of calculating the processor thermal saturation index based on the edge processor energy consumption constant, the transient compensation carbon emission mass flow rate, and the rated heat dissipation power includes: Obtain the edge processor's power consumption constant and rated heat dissipation power; The real-time thermal power load of the processor is obtained by multiplying the edge processor energy consumption constant by the transient compensation carbon emission mass flow rate. The processor thermal saturation index is obtained by dividing the real-time thermal power load of the processor by the rated heat dissipation power.

[0011] Optionally, the step of calculating the communication data loss ratio based on the processor's thermal saturation index and then restoring the real-time carbon emission mass flow rate by calculating the transient compensation carbon emission mass flow rate includes: The first value is obtained by adding the constant 1 to the processor's thermal saturation index; The second value is obtained by dividing the constant 1 by the first value. The communication data loss ratio is obtained by subtracting the second value from the constant 1; The third value is obtained by subtracting the communication data loss ratio from the constant 1. The real-time carbon emission mass flow rate is obtained by dividing the transient compensated carbon emission mass flow rate by the third value.

[0012] Optionally, the step of calculating the cumulative carbon quota usage of the current node based on the recovered real-time carbon emission mass flow rate, and calculating the topological carbon balance difference by combining the measured cumulative carbon mass at the main emission outlet with the cumulative carbon quota usage of each monitoring node, includes: Obtain the total number of monitoring nodes and the measured cumulative carbon mass at the main discharge outlet; The single calculation amount is obtained by multiplying the recovered real-time carbon emission mass flow rate at each discrete time point by the sampling interval time; The cumulative carbon quota usage of the current node is obtained by summing up all the single calculations within the total sampling period. The total carbon quota usage of each monitoring node is summed to obtain the total global node usage. The topological carbon balance difference is obtained by subtracting the total global node accounting amount from the measured cumulative carbon mass of the main outlet.

[0013] Optionally, the step of calculating the final corrected management carbon quota consumption based on the topological carbon balance difference and the cumulative carbon quota usage of each monitoring node includes: The total carbon quota usage of each monitoring node is summed to obtain the total global carbon quota usage. The allocation ratio is obtained by dividing the cumulative carbon quota usage of the current node by the total carbon quota usage of all nodes. The dynamic compensation correction value is obtained by multiplying the topological carbon balance difference by the allocation ratio; The final corrected carbon quota consumption is obtained by adding the cumulative carbon quota usage of the current node to the dynamic compensation correction value.

[0014] The present invention has the following beneficial effects:

[0015] 1. By calculating the fluid transport delay time to shift the gas concentration time axis, and combining parameters such as gas volumetric flow rate, electrical power change rate, and processor thermal saturation index, this scheme constructs a real-time carbon emission monitoring method based on the Internet of Things and edge computing. In specific industrial environments with emission pipelines of a certain physical length and cross-sectional area, frequent start-ups and shutdowns of industrial equipment, and limited computing power of edge computing nodes, differences in fluid velocity and sensor location can lead to misalignment of heterogeneous data time axes. Conventional time window averaging algorithms can erase sudden changes in carbon emissions, high-frequency data can cause edge node overload and packet loss, and long-term compensation can lead to regional topological imbalance. To address the above issues, this solution employs multimodal physical data alignment and correction, transient emission fluctuation feature capture, theoretical packet loss reconstruction under computing power saturation, and weighted allocation of long-term quota errors based on spatial topology network verification differences. This effectively avoids phase deviations in basic benchmark carbon flow calculation and loss of transient emission feature data, solves the problems of computing power overload and random communication data loss caused by high-frequency sudden data changes, and prevents topological imbalance in regional carbon quota calculation caused by continuous compensation of local edge nodes. It achieves complete calculation from underlying physical data synchronization to transient feature retention, computing power overload recovery, and global quota correction.

[0016] 2. By obtaining the physical length and cross-sectional area of ​​the pipeline at a fixed section of the discharge pipeline, multiplying the two to obtain the spatial volume of the pipeline monitoring section, and further dividing the spatial volume of the pipeline monitoring section by the synchronously acquired gas volumetric flow rate, the fluid transmission delay time is obtained. This calculation method, which combines objectively existing physical structural parameters with real-time fluid dynamic parameters, can reconstruct the actual physical transmission delay process of gas and fluid inside the pipeline when multiple heterogeneous sensors are distributed in different physical locations. This provides an objective time offset measurement benchmark for the time-series alignment of subsequent physical data of different modes, avoids the direct aggregation of different physical quantities in the time dimension, and ensures the spatial matching rationality of the underlying physical parameters.

[0017] 3. By shifting the time axis of the obtained original gas concentration based on the fluid transport delay time obtained in the previous steps, the synchronized gas concentration after eliminating the physical space transport time misalignment is obtained. Then, the real-time measured gas volume flow rate is multiplied with the synchronized gas concentration to obtain the synchronized baseline carbon emission mass flow rate. This process takes into account the data heterogeneity time axis misalignment caused by fluid velocity differences and the discrete installation positions of multiple sensors, so that the volume data and concentration data involved in the mass flow rate calculation maintain a synchronous correspondence at the physical occurrence time, and overcome the problem of phase deviation in the calculation of the baseline carbon flow rate.

[0018] 4. By acquiring the current real-time operating power of the associated equipment and the operating power of the equipment at the previous sampling time, the absolute power difference of the equipment is obtained by subtracting the operating power of the equipment at the previous sampling time from the current real-time operating power. The absolute power difference of the equipment is then divided by the set sampling interval time to obtain the standardized equipment power change rate. This process utilizes the first-order differential change characteristics of the power consumption of associated production equipment to effectively identify the sudden fluctuations in power consumption caused by the start-up and shutdown transients or load changes of industrial equipment. It prevents the transient emission peaks caused by sudden changes in equipment operating status from being erased by the conventional time window averaging algorithm, and improves the dynamic synchronization between the underlying power consumption status and emission status.

[0019] 5. By extracting the absolute value of the rate of change of the electrical power of the equipment, multiplying it by the set transient reference time constant, and dividing it by the current real-time operating electrical power, a dimensionless transient compensation multiplier coefficient is obtained. Then, the constant value is added to the transient compensation multiplier coefficient, and the result is multiplied by the synchronous benchmark carbon emission mass flow rate to finally obtain the transient compensation carbon emission mass flow rate. This processing logic transforms the work fluctuation characteristics of the associated equipment into a flow compensation multiplier and superimposes it onto the aligned benchmark carbon mass flow rate, so that the transient emission characteristics of industrial equipment operating conditions that are easily lost can be explicitly preserved, ensuring the feature integrity of carbon emission mass flow rate data during transient changes.

[0020] 6. By obtaining the edge processor's energy consumption constant, multiplying it by the transient compensation carbon emission mass flow rate to obtain the processor's real-time thermal power load, and further dividing the processor's real-time thermal power load by the device's rated heat dissipation power to obtain the edge hardware's processor thermal saturation index, this process establishes the physical correlation between the data flow load of the edge computing node when processing transient data changes and the computing power consumption of the underlying hardware. It quantifies the accumulation of edge device hardware thermal load caused by the sudden increase in computing power due to processing high-frequency data changes, and provides a direct underlying hardware indicator measurement basis for subsequent evaluation of the abnormal state of system communication operation caused by the thermodynamic saturation of computing power of node devices.

[0021] 7. By adding the constant value to the processor thermal saturation index and calculating the division ratio, the communication data loss ratio is obtained by continuously subtracting the constant value. Then, the third value is obtained by subtracting the communication data loss ratio from the constant value as the inverse divisor factor. Finally, the transient compensation carbon emission mass flow rate is divided by the third value to obtain the restored real-time carbon emission mass flow rate. This step uses the computing power thermal saturation state to perform nonlinear correlation prediction of the communication packet loss ratio of the edge computing node, and performs inverse numerical amplification and restoration reconstruction of the packet loss data in the calculation process, making up for the random communication data packet loss gap caused by the surge in computing power load due to sudden data changes.

[0022] 8. By multiplying the recovered real-time carbon emission mass flow rate at each discrete time point by the sampling interval, the cumulative carbon quota usage of the current node is calculated by summing over the total sampling period. Then, the cumulative carbon quota usage of each monitoring node is summed globally, and finally, it is subtracted from the measured cumulative carbon mass at the main discharge outlet to obtain the topological carbon balance difference. This mechanism introduces a multi-node pipeline topological balance verification equation, quantifies the accumulation of small compensation errors caused by long-term edge-side calculation and packet loss recovery, reveals the systematic verification difference between the sum of the calculation values ​​of each sub-node and the global actual physical emission value, and provides a global topological verification benchmark.

[0023] 9. The total global carbon quota usage is obtained by summing the cumulative carbon quota usage of each monitoring node. The allocation ratio is generated by dividing the current node's cumulative carbon quota usage by the total carbon quota usage. The dynamic compensation correction value is obtained by multiplying the topological carbon balance difference by the allocation ratio. Finally, the current node's cumulative carbon quota usage is added to the dynamic compensation correction value to obtain the final corrected carbon quota consumption. This calculation logic distributes the long-term accumulated system topology error according to the weight scale based on the relative proportion of the current monitoring node's total carbon quota usage in the global network topology, thus alleviating the topological imbalance problem of regional carbon quota accounting over a long period. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the basic process of the present invention.

[0025] Figure 2 This is a schematic diagram of the sensor deployment and edge computing node communication connection relationship of the present invention.

[0026] Figure 3 This is a schematic diagram illustrating the relationship between timing alignment, transient compensation, and communication packet loss recovery in this invention.

[0027] Figure 4 This is a schematic diagram illustrating the relationship between topological carbon balance differences and carbon quota correction in this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1, refer to Figure 1 A real-time carbon emission monitoring method based on the Internet of Things and edge computing includes: The fluid transport delay time is calculated from the physical length of the pipe, the cross-sectional area of ​​the pipe, and the gas volumetric flow rate. The original gas concentration is shifted according to the fluid transport delay time, and the synchronous baseline carbon emission mass flow rate is calculated in combination with the gas volume flow rate. The rate of change of device power is calculated based on real-time operating power, device operating power at the previous sampling time, and sampling interval. The transient compensation carbon emission mass flow rate is calculated by correcting the synchronous benchmark carbon emission mass flow rate based on the equipment's power change rate, transient reference time constant, and real-time operating power. The processor thermal saturation index is calculated based on the edge processor energy consumption constant, the transient compensation carbon emission mass flow rate, and the rated heat dissipation power. The proportion of communication data loss is calculated from the processor's thermal saturation index, and the real-time carbon emission mass flow rate is calculated and restored based on the transient compensation carbon emission mass flow rate. The cumulative carbon quota usage of the current node is calculated by summing the real-time carbon emission mass flow rate as described above, and the topological carbon balance difference is calculated by combining the measured cumulative carbon mass at the main emission outlet with the cumulative carbon quota usage of each monitoring node. Based on the topological carbon balance difference and the cumulative carbon quota usage of each monitoring node, the final adjusted carbon quota consumption is calculated by correcting the cumulative carbon quota usage of the current node.

[0030] The following combination Figures 2 to 4 The document further explains the sensor deployment and communication connection, edge computing processing, and the multi-node carbon quota correction relationship. The calculation of fluid transport delay time based on the physical length of the pipe, the cross-sectional area of ​​the pipe, and the gas volumetric flow rate includes: The current step is used to calculate the fluid physical transport delay between the exhaust point and the monitoring point. The hysteresis time is determined by the spatial volume of the pipeline monitoring section and the gas volume flow rate, providing an alignment reference for multimodal physical data. Edge computing nodes, gas flow sensors, concentration sensors, power meters, and main outlet monitors; The gas flow sensor and the concentration sensor are installed at a fixed section of the discharge pipe; The power meter is installed at the power supply end of the associated production equipment; the main discharge port monitor is installed at the main discharge port; The edge computing node is communicatively connected to the gas flow sensor, the concentration sensor, the power meter, and the main discharge port monitor via an industrial bus. Reference Figure 2Gas flow sensor, concentration sensor, power meter and main outlet monitor collect corresponding monitoring parameters and establish communication connection with edge computing node through industrial bus; Obtain the physical length and cross-sectional area of ​​the pipe, and simultaneously obtain the gas volumetric flow rate; First, calculate the volume of the monitoring section inside the existing discharge pipeline. The volume of the monitoring section is calculated through the following steps: The volume of the monitoring section of the pipeline is obtained by multiplying the physical length of the pipeline by the cross-sectional area of ​​the pipeline. By using the volume of the pipeline monitoring section and the real-time gas volumetric flow rate, the time offset of the fluid physical transport delay is calculated. The fluid transport delay time is then calculated through the following steps: The fluid transport delay time is obtained by dividing the volume of the pipeline monitoring section by the gas volume flow rate.

[0031] By obtaining the physical length and cross-sectional area of ​​the pipeline at a fixed section, multiplying them to obtain the spatial volume of the pipeline monitoring section, and then dividing the spatial volume of the pipeline monitoring section by the synchronously acquired gas volumetric flow rate, the fluid transmission delay time is obtained. This calculation method, which combines objectively existing physical structural parameters with real-time fluid dynamic parameters, can reconstruct the actual physical transmission delay process of gas and fluid inside the pipeline when multiple heterogeneous sensors are distributed in different physical locations. This provides an objective time offset measurement benchmark for the time-series alignment of subsequent physical data of different modes, avoids the direct aggregation of different physical quantities in the time dimension, and ensures the spatial matching rationality of the underlying physical parameters.

[0032] Reference Figure 3 Fluid transport delay is used to correct the time axis of the original gas concentration; device power change is used for transient compensation; processor thermal saturation index is used for communication packet loss recovery. The step of shifting the original gas concentration according to the fluid transport delay time and calculating the synchronous baseline carbon emission mass flow rate in conjunction with the gas volume flow rate includes: The current step uses the fluid transport delay time calculated in the previous step to perform time offset backtracking on the transient gas concentration data, solves the problem of time axis misalignment of heterogeneous data caused by fluid flow rate differences and sensor positions, obtains the synchronous concentration to eliminate time misalignment, and calculates the baseline carbon mass flow rate under synchronous conditions. Obtain the original gas concentration; First, based on the fluid transport delay time, the original gas concentration time axis is shifted to obtain the aligned synchronous gas concentration. The aligned synchronous gas concentration is then calculated through the following steps: The time axis of the original gas concentration is shifted and offset according to the fluid transport delay time to obtain the aligned synchronous gas concentration. Subsequently, the synchronizing gas concentration is multiplied by the current gas volumetric flow rate to obtain the aligned baseline carbon mass flow rate. The synchronizing baseline carbon emission mass flow rate is then calculated using the following steps: The synchronous baseline carbon emission mass flow rate is obtained by multiplying the gas volume flow rate by the aligned synchronous gas concentration.

[0033] By shifting the time axis of the obtained original gas concentration based on the fluid transport delay time obtained in the previous steps, the synchronized gas concentration after eliminating the physical space transport time misalignment is obtained. Then, the real-time measured gas volume flow rate is multiplied with the synchronized gas concentration to obtain the synchronized baseline carbon emission mass flow rate. This process takes into account the data heterogeneity time axis misalignment caused by fluid velocity differences and the discrete installation positions of multiple sensors, so that the volume data and concentration data involved in the mass flow rate calculation maintain a synchronous correspondence at the physical occurrence time, and overcome the problem of phase deviation in the calculation of the baseline carbon flow rate.

[0034] The calculation of the device power change rate based on real-time operating power, the device operating power at the previous sampling time, and the sampling interval includes: After synchronizing the data, in order to address the problem that the sudden changes in carbon emissions generated by industrial equipment during start-up and shutdown are erased by conventional time window averaging algorithms and to prevent the peak of transient emissions from being smoothed out, the current step identifies the characteristics of sudden fluctuations caused by equipment start-up, shutdown or load changes by calculating the differential changes in the power of associated equipment. The sampling interval is set based on the sampling capability of the edge computing nodes and the accuracy requirements of carbon emission monitoring; Obtain the current real-time operating power of the associated device and the operating power of the device at the previous sampling time; First, the absolute difference in equipment work done within adjacent sampling periods is determined. The absolute difference in equipment work is calculated through the following steps: The absolute difference in work done by the device is obtained by subtracting the device's operating power at the previous sampling time from the current real-time operating power. By using the absolute work difference and the sampling interval, standardized equipment power fluctuation parameters are calculated. The equipment power change rate is then calculated using the following steps: The rate of change of electrical power of the device is obtained by dividing the absolute difference in work done by the sampling interval.

[0035] By acquiring the current real-time operating power of the associated equipment and the operating power of the equipment at the previous sampling time, the absolute power difference of the equipment is obtained by subtracting the operating power of the equipment at the previous sampling time from the current real-time operating power. The absolute power difference of the equipment is then divided by the set sampling interval time to obtain the standardized equipment power change rate. This process utilizes the first-order differential change characteristics of the power consumption of associated production equipment to effectively identify the sudden fluctuations in power consumption caused by the start-up and shutdown transients or load changes of industrial equipment. It prevents the transient emission peaks caused by sudden changes in equipment operating status from being erased by the conventional time window averaging algorithm, and improves the dynamic synchronization between the underlying power consumption status and emission status.

[0036] The step of calculating the transient compensated carbon emission mass flow rate by correcting the synchronous benchmark carbon emission mass flow rate using the equipment's power change rate, transient reference time constant, and real-time operating power includes: The current step converts the identified rate of change of electrical power into a dimensionless transient fluctuation multiplier and adds it to the baseline carbon emission mass flow rate to obtain a compensated flow rate that does not lose transient characteristics. A transient reference time constant is set, which reflects the relaxation or tightening of the penalty mechanism for transient abrupt changes. The larger the value, the heavier the penalty multiplier for indirect carbon emissions caused by transient power fluctuations in the accounting system; First, extract the absolute value of the rate of change of the equipment's electrical power. Combine this with the set transient reference time constant and the current real-time operating electrical power, and map it to a dimensionless transient compensation multiplier coefficient. The transient compensation multiplier coefficient is then calculated through the following steps: Extract the absolute value of the rate of change of the device's electrical power; multiply the absolute value of the rate of change of the device's electrical power by the transient reference time constant; divide the result of the multiplication by the current real-time operating electrical power to obtain the transient compensation multiplier coefficient; The transient compensation multiplier is superimposed and amplified onto the synchronous baseline carbon emission mass flow rate to obtain the flow rate after capturing transient characteristics. The transient compensated carbon emission mass flow rate is then calculated through the following steps: Add the constant 1 to the transient compensation multiplier coefficient; multiply the result of the addition with the synchronous baseline carbon emission mass flow rate to obtain the transient compensation carbon emission mass flow rate.

[0037] By extracting the absolute value of the rate of change of the device's electrical power, multiplying it by the set transient reference time constant, and dividing it by the current real-time operating electrical power, a dimensionless transient compensation multiplier coefficient is obtained. Then, the constant value is added to the transient compensation multiplier coefficient, and the result is multiplied by the synchronous benchmark carbon emission mass flow rate to finally obtain the transient compensated carbon emission mass flow rate. This processing logic transforms the work fluctuation characteristics of the associated equipment into a flow compensation multiplier and superimposes it onto the aligned benchmark carbon mass flow rate, so that the transient emission characteristics of industrial equipment operating conditions that are easily lost can be explicitly preserved, ensuring the feature integrity of carbon emission mass flow rate data during transient changes.

[0038] The calculation of the processor thermal saturation index based on the edge processor energy consumption constant, the transient compensation carbon emission mass flow rate, and the rated heat dissipation power includes: High-frequency capture of transient data can cause a surge in computing power. To address the problem of high-frequency data mutation leading to a surge in computing load and thermal saturation of edge computing nodes, which in turn causes random data loss, this step evaluates the thermal load accumulation of the edge processor when processing transient traffic compensation and determines the thermal saturation state. Obtain the edge processor's power consumption constant and rated heat dissipation power; First, the real-time thermal load of the edge computing node is calculated by multiplying the energy consumption constant and the transient compensation flow. Then, the real-time thermal load of the processor is calculated through the following steps: The real-time thermal power load of the processor is obtained by multiplying the edge processor energy consumption constant by the transient compensation carbon emission mass flow rate. By comparing the real-time thermal power load with the processor's rated heat dissipation capacity, the dimensionless thermodynamic saturation boundary index of the edge hardware is determined. The processor's thermal saturation index is then calculated using the following steps: The processor thermal saturation index is obtained by dividing the real-time thermal power load of the processor by the rated heat dissipation power.

[0039] By obtaining the edge processor's energy consumption constant and multiplying it by the transient compensation carbon emission mass flow rate, the real-time thermal power load of the processor is obtained. Furthermore, the real-time thermal power load of the processor is divided by the rated heat dissipation power of the device to obtain the processor thermal saturation index of the edge hardware. This process establishes a physical correlation between the data flow load of the edge computing node when processing transient data changes and the computing power consumption of the underlying hardware. It quantifies the accumulation of hardware thermal load of the edge device caused by the sudden increase in computing power due to the processing of high-frequency data changes, and provides a direct underlying hardware indicator measurement basis for subsequent evaluation of the abnormal state of system communication operation caused by the thermodynamic saturation of computing power of the node device.

[0040] The step of calculating the communication data loss ratio using the processor's thermal saturation index and then restoring the real-time carbon emission mass flow rate based on this ratio includes: Based on the heat saturation index, the communication packet loss ratio of edge computing nodes is estimated, and the packet loss data missing in the calculation process is reversed to reconstruct the recovered quality traffic that is not affected by computing power overload. First, a nonlinear inverse mapping is performed based on the processor's thermal load index to construct the theoretical communication data loss ratio of the system. The communication data loss ratio is then calculated through the following steps: Add the constant 1 to the processor thermal saturation index to obtain a first value; calculate the constant 1 and divide it by the first value to obtain a second value; subtract the second value from the constant 1 to obtain the communication data loss ratio; Using this loss ratio as the basis for the inverse divisor, the transient compensation flow is numerically amplified and restored. The real-time carbon emission mass flow is then calculated and restored through the following steps: The third value is obtained by subtracting the communication data loss ratio from the constant 1; the recovered real-time carbon emission mass flow rate is obtained by dividing the transient compensation carbon emission mass flow rate by the third value.

[0041] By adding a constant value to the processor's thermal saturation index and calculating the division ratio, the communication data loss ratio is obtained by continuously subtracting the constant value. Then, the third value is obtained by subtracting the communication data loss ratio from the constant value as a reverse divisor factor. Finally, the transient compensation carbon emission mass flow rate is divided by the third value to obtain the restored real-time carbon emission mass flow rate. This step uses the computing power thermal saturation state to perform nonlinear correlation prediction of the communication packet loss ratio of edge computing nodes, and performs reverse numerical amplification and restoration reconstruction of the packet loss data in the calculation process, making up for the random communication data packet loss gap caused by the surge in computing power load due to sudden data changes.

[0042] Reference Figure 4 The cumulative accounting of each monitoring node is summarized to form the total accounting of all nodes, and after being compared with the actual measured cumulative carbon mass of the main discharge outlet, error correction is completed according to the allocation ratio. The calculation of the current node's cumulative carbon allowance usage based on the recovered real-time carbon emission mass flow rate, and the calculation of the topological carbon balance difference by combining the measured cumulative carbon mass at the main emission outlet with the cumulative carbon allowance usage at each monitoring node, include: Long-term edge-side packet loss recovery can lead to the accumulation of accounting errors, causing the problem of regional carbon quota accounting topology imbalance caused by the accumulation of small compensation errors over a long period of time. The current step introduces a multi-node topology balance verification mechanism to calculate the systematic difference between the total emission outlet measured value and the sum of the accounting values ​​of each sub-node. Obtain the total number of monitoring nodes and the measured cumulative carbon mass at the main discharge outlet; First, by multiplying the discrete-time single-recovery real-time carbon emission mass flow rate by the set sampling interval, the absolute cumulative accounting amount of the current monitoring node within the total sampling period is calculated. Then, the cumulative carbon quota usage of the current node is calculated through the following steps: The single-time calculation amount is obtained by multiplying the recovered real-time carbon emission mass flow rate at each discrete time point by the sampling interval; the cumulative carbon quota usage at the current node is obtained by summing all the single-time calculation amounts within the total sampling period. Then, combining the calculations returned by all other monitoring nodes globally, the absolute error of the carbon quota balance of the entire network topology is calculated, and the topology carbon balance difference is calculated through the following steps: The total global carbon quota usage is obtained by summing the cumulative carbon quota usage of each monitoring node; the topological carbon balance difference is obtained by subtracting the total global carbon quota usage from the measured cumulative carbon mass of the main discharge outlet.

[0043] By multiplying the recovered real-time carbon emission mass flow rate at each discrete time point by the sampling interval, and summing the results over the total sampling period, the cumulative carbon quota usage of the current node is calculated. Then, the cumulative carbon quota usage of each monitoring node is summed globally, and finally, this summation is subtracted from the measured cumulative carbon mass at the main discharge outlet to obtain the topological carbon balance difference. This mechanism introduces a multi-node pipeline topological balance verification equation, quantifies the accumulation of small compensation errors caused by long-term edge-side calculations and packet loss recovery, reveals the systematic verification difference between the sum of the calculated values ​​of each sub-node and the global actual physical emission value, and provides a global topological verification benchmark.

[0044] The calculation of the final corrected management carbon quota consumption, based on the topological carbon balance difference and the cumulative carbon quota usage of each monitoring node, includes: The current step distributes the topology error according to the weight based on the proportion of the total amount of accounting generated by the current node in the global network, and outputs the final settlement and management verification data. Using the mathematical proportion of the current node's own cumulative amount in the total cumulative amount of the entire network as a dynamic compensation weight, the topological carbon balance difference is injected and corrected in a targeted manner. The final corrected carbon quota consumption is calculated through the following steps: The total carbon quota usage of each monitoring node is summed to obtain the total global carbon quota usage. The allocation ratio is obtained by dividing the current node's cumulative carbon quota usage by the total global carbon quota usage. The dynamic compensation correction value is obtained by multiplying the topological carbon balance difference by the allocation ratio. The final corrected carbon quota consumption is obtained by adding the current node's cumulative carbon quota usage to the dynamic compensation correction value.

[0045] The total carbon quota usage of each monitoring node is summed to obtain the total global carbon quota usage. The current node's cumulative carbon quota usage is divided by this total carbon quota usage to generate an allocation weight. The topology carbon balance difference is multiplied by this allocation weight to obtain a dynamic compensation correction value. Finally, the current node's cumulative carbon quota usage is added to this dynamic compensation correction value to obtain the final corrected carbon quota consumption. This calculation logic distributes the long-term accumulated system topology error according to the relative proportion of the current monitoring node's total carbon quota usage in the global network topology, thus alleviating the topology imbalance problem of regional carbon quota accounting over a long period.

[0046] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0047] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of carbon emissions based on the Internet of Things and edge computing, characterized in that, include: The fluid transport delay time is calculated from the physical length of the pipe, the cross-sectional area of ​​the pipe, and the gas volumetric flow rate. The original gas concentration is shifted according to the fluid transport delay time, and the synchronous baseline carbon emission mass flow rate is calculated in combination with the gas volume flow rate. The rate of change of device power is calculated based on real-time operating power, device operating power at the previous sampling time, and sampling interval. The transient compensation carbon emission mass flow rate is calculated by correcting the synchronous benchmark carbon emission mass flow rate based on the equipment's power change rate, transient reference time constant, and real-time operating power. The processor thermal saturation index is calculated based on the edge processor energy consumption constant, the transient compensation carbon emission mass flow rate, and the rated heat dissipation power. The proportion of communication data loss is calculated from the processor's thermal saturation index, and the real-time carbon emission mass flow rate is calculated and restored based on the transient compensation carbon emission mass flow rate. The cumulative carbon quota usage of the current node is calculated by summing the real-time carbon emission mass flow rate as described above, and the topological carbon balance difference is calculated by combining the measured cumulative carbon mass at the main emission outlet with the cumulative carbon quota usage of each monitoring node. Based on the topological carbon balance difference and the cumulative carbon quota usage of each monitoring node, the final adjusted carbon quota consumption is calculated by correcting the cumulative carbon quota usage of the current node.

2. The method for real-time carbon emission monitoring based on the Internet of Things and edge computing according to claim 1, characterized in that, The calculation of fluid transport delay time based on the physical length of the pipe, the cross-sectional area of ​​the pipe, and the gas volumetric flow rate includes: Edge computing nodes, gas flow sensors, concentration sensors, power meters, and main outlet monitors; The gas flow sensor and the concentration sensor are installed at a fixed section of the discharge pipe; The power meter is installed at the power supply end of the associated production equipment; The main discharge outlet monitoring device is installed at the main discharge outlet; The edge computing node is communicatively connected to the gas flow sensor, the concentration sensor, the power meter, and the main discharge port monitor via an industrial bus. Obtain the physical length and cross-sectional area of ​​the pipe, and simultaneously obtain the gas volumetric flow rate; The volume of the monitoring section of the pipeline is obtained by multiplying the physical length of the pipeline by the cross-sectional area of ​​the pipeline. The fluid transport delay time is obtained by dividing the volume of the pipeline monitoring section by the gas volume flow rate.

3. The method for real-time monitoring of carbon emissions based on the Internet of Things and edge computing according to claim 2, characterized in that, The step of shifting the original gas concentration according to the fluid transport delay time and calculating the synchronous baseline carbon emission mass flow rate in conjunction with the gas volume flow rate includes: Obtain the original gas concentration; The time axis of the original gas concentration is shifted and offset according to the fluid transport delay time to obtain the aligned synchronous gas concentration. The synchronous baseline carbon emission mass flow rate is obtained by multiplying the gas volume flow rate by the aligned synchronous gas concentration.

4. The method for real-time carbon emission monitoring based on the Internet of Things and edge computing according to claim 3, characterized in that, The calculation of the device power change rate based on real-time operating power, the device operating power at the previous sampling time, and the sampling interval includes: Set the sampling interval time; Obtain the current real-time operating power of the associated device and the operating power of the device at the previous sampling time; The absolute difference in work done by the device is obtained by subtracting the device's operating power at the previous sampling time from the current real-time operating power. The rate of change of electrical power of the device is obtained by dividing the absolute difference in work done by the sampling interval.

5. A method for real-time monitoring of carbon emissions based on the Internet of Things and edge computing according to claim 4, characterized in that, The step of calculating the transient compensated carbon emission mass flow rate by correcting the synchronous benchmark carbon emission mass flow rate using the equipment's power change rate, transient reference time constant, and real-time operating power includes: Set the transient reference time constant; Extract the absolute value of the rate of change of the electrical power of the device; Multiply the absolute value of the rate of change of the electrical power of the device by the transient reference time constant; The result of multiplication is divided by the current real-time operating power to obtain the transient compensation multiplier coefficient. Add the constant 1 to the transient compensation multiplier coefficient; The summation result is multiplied by the synchronous baseline carbon emission mass flow rate to obtain the transient compensated carbon emission mass flow rate.

6. The method for real-time carbon emission monitoring based on the Internet of Things and edge computing according to claim 5, characterized in that, The calculation of the processor thermal saturation index based on the edge processor energy consumption constant, the transient compensation carbon emission mass flow rate, and the rated heat dissipation power includes: Obtain the edge processor's power consumption constant and rated heat dissipation power; The real-time thermal power load of the processor is obtained by multiplying the edge processor energy consumption constant by the transient compensation carbon emission mass flow rate. The processor thermal saturation index is obtained by dividing the real-time thermal power load of the processor by the rated heat dissipation power.

7. A method for real-time carbon emission monitoring based on the Internet of Things and edge computing according to claim 6, characterized in that, The step of calculating the communication data loss ratio using the processor's thermal saturation index and then restoring the real-time carbon emission mass flow rate based on this ratio includes: The first value is obtained by adding the constant 1 to the processor's thermal saturation index; The second value is obtained by dividing the constant 1 by the first value. The communication data loss ratio is obtained by subtracting the second value from the constant 1; The third value is obtained by subtracting the communication data loss ratio from the constant 1. The real-time carbon emission mass flow rate is obtained by dividing the transient compensated carbon emission mass flow rate by the third value.

8. The method for real-time monitoring of carbon emissions based on the Internet of Things and edge computing according to claim 7, characterized in that, The calculation of the current node's cumulative carbon allowance usage based on the recovered real-time carbon emission mass flow rate, and the calculation of the topological carbon balance difference by combining the measured cumulative carbon mass at the main emission outlet with the cumulative carbon allowance usage at each monitoring node, include: Obtain the total number of monitoring nodes and the measured cumulative carbon mass at the main discharge outlet; The single calculation amount is obtained by multiplying the recovered real-time carbon emission mass flow rate at each discrete time point by the sampling interval time; The cumulative carbon quota usage of the current node is obtained by summing up all the single calculations within the total sampling period. The total carbon quota usage of each monitoring node is summed to obtain the total global node usage. The topological carbon balance difference is obtained by subtracting the total global node accounting amount from the measured cumulative carbon mass of the main outlet.

9. A method for real-time monitoring of carbon emissions based on the Internet of Things and edge computing according to claim 8, characterized in that, The calculation of the final corrected management carbon quota consumption, based on the topological carbon balance difference and the cumulative carbon quota usage of each monitoring node, includes: The total carbon quota usage of each monitoring node is summed to obtain the total global carbon quota usage. The allocation ratio is obtained by dividing the cumulative carbon quota usage of the current node by the total carbon quota usage of all nodes. The dynamic compensation correction value is obtained by multiplying the topological carbon balance difference by the allocation ratio; The final corrected carbon quota consumption is obtained by adding the cumulative carbon quota usage of the current node to the dynamic compensation correction value.

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