Online monitoring method and system for carbon emission in rubber tube manufacturing process

Abstract

The invention relates to the technical field of data processing, in particular to an online monitoring method and system for carbon emission in the rubber tube manufacturing process, and the method comprises the steps that gas data are collected, and the gas data comprise # imgabs0 # concentration and gas flow; and calculating the time distance between the historical data of the gas data and the currently collected gas data, and obtaining the contribution degree of the historical data according to the product of the time distance and the quality of the historical data. The contribution degree of the historical data to the currently collected gas data is calculated by combining the time distance and the data quality. And weighting the difference degree between the historical data and the currently collected gas data by using the contribution degree of the currently collected gas data to obtain the noise expression degree of the currently collected gas data, thereby obtaining the correction value of the actual value of the currently collected gas data, so that the data of the # imgabs1 # concentration and gas flow monitored in real time are closer to the real value, and the accuracy of the real-time monitoring is improved. And the accuracy of carbon emission monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to an online monitoring method and system for carbon emissions in the process of rubber hose manufacturing. Background Art

[0002] Rubber hoses are widely used in fields such as automobiles, industrial machinery, petrochemicals, etc. The manufacturing process involves multiple links such as raw material processing, mixing, shaping, vulcanization, and cutting. The energy consumption of these links mainly comes from the combustion of electricity, natural gas, or coal, and the greenhouse gases emitted mainly include CO, SO, and NO, etc. Under the promotion of the "dual-carbon" policy, rubber hose manufacturing enterprises are facing the pressure of reducing carbon emissions. However, the current carbon emission monitoring means in the industry are still relatively rough and it is difficult to accurately quantify the carbon emissions of each process link. In order to meet the needs of precise monitoring, the online monitoring system should combine sensor technology, gas analysis technology, and big data processing capabilities to achieve real-time and dynamic tracking of carbon emissions during the production process. The system can connect production equipment, flue gas emission devices, and energy metering systems to build a complete carbon emission data collection and analysis system, and provide visualization display and intelligent early warning functions to help enterprises optimize the production process and reduce the carbon emission intensity.

[0003] In related technologies, for example, the Chinese patent document with the publication number CN116930421A discloses a carbon emission monitoring device and a carbon emission monitoring method. This invention can analyze the influence of air parameters on concentration, and predict the concentration change trend, and then intervene in it in advance to prevent areas with relatively low carbon emissions from gradually developing into areas with relatively high carbon emissions.

[0004] However, during the monitoring process of carbon dioxide emissions, due to factors such as the accuracy of sensors, environmental factors, and electromagnetic interference, there are noise data in the monitored data, resulting in deviations in the monitored carbon emissions, thus causing inaccurate carbon emission monitoring. Summary of the Invention

[0005] The present invention provides an online monitoring method and system for carbon emissions in the process of rubber hose manufacturing, aiming to solve the problem in related technologies that due to factors such as the accuracy of sensors, environmental factors, and electromagnetic interference, there are noise data in the monitored data, resulting in deviations in the monitored carbon emissions, thus causing inaccurate carbon emission monitoring.

[0006] In the first aspect, the present invention provides an online monitoring method for carbon emissions in the process of rubber hose manufacturing, including: collecting gas data, where the gas data includes Concentration and gas flow rate; calculate the time distance between the historical data of the gas data and the currently collected gas data, and obtain the contribution degree of the historical data according to the product of the time distance and the quality of the historical data, where the quality of the historical data reflects the deviation between the predicted value and the actual value of the historical data; by performing a weighted average on the product of the difference degree and the contribution degree of all historical data and the currently collected gas data, obtain the noise performance degree of the currently collected gas data , so as to calculate the correction value of the actual value of any kind of data in the currently collected gas data , and the calculation formula is: ; in the formula, represents the actual value of the currently collected gas data; represents the time of the currently collected gas data; represents the th data time before the currently collected gas data; represents the attenuation function; represents the th data actual value before the currently collected gas data; represents the natural exponential function, represents the number of all historical data before the currently collected gas data; according to the correction value of the actual value of the currently collected gas data, obtain the carbon emission at the currently collected moment to monitor the carbon emission. By introducing the contribution degree and the noise performance degree of the historical data, correct the currently collected gas data (such as CO 2 concentration and gas flow rate). The correction value comprehensively considers the actual value of the current data and the weighted average of the historical data, which can effectively reduce noise interference and improve the accuracy of the data.

[0007] Further, calculate the contribution degree of the historical data, and the calculation formula is: ; in the formula, represents the contribution degree of the th historical data to the previously collected data; represents the time of the currently collected gas data; represents the th historical data time; represents the attenuation function; represents the th historical data quality. According to the time characteristics of the collected data, when the time between the historical data and the currently collected gas data is longer, the result of the historical data is less accurate. Therefore, according to this characteristic, the contribution degree of the historical data of concentration and gas flow rate can be accurately calculated.

[0008] Further, calculate the quality of the historical data, including: ; in the formula, Indicates the quality of the nth historical data; Indicates the actual value of the nth historical data; Indicates the predicted value of the nth historical data; Indicates the number of historical data before the current collected gas data point; Indicates that among the historical data, except for the nth historical data, the actual value of the mth historical data; Indicates that among the historical data, except for the nth historical data, the predicted value of the mth historical data. If there are large fluctuations in the historical data, it will lead to inaccurate results in predicting using the historical data. Therefore, it is necessary to first evaluate the quality of the historical data. The quality of the historical data can be calculated by the above method (the difference between the predicted value and the actual value of the historical data), so as to improve the accuracy and reliability of the evaluation data.

[0009] Furthermore, obtaining the noise performance degree of the currently collected gas data of the concentration and gas flow , further includes: obtaining the weighted average value of the product of the difference degree and contribution degree between all historical data and the currently collected gas data, and normalizing this value to obtain the noise performance degree of the currently collected gas data of the concentration and gas flow. Considering that the difference degree between the current data and the historical data is significant, and this historical data has a high contribution degree due to high quality and close time, this indicates that the current data is likely to be an outlier and needs to be further corrected. Using the above method of weighted difference degree not only considers the difference degree between data, but also combines the quality and time factors of data, making the evaluation result more accurate and reliable.

[0010] Furthermore, the calculation method of the carbon emission amount includes: using the product of the currently monitored carbon dioxide concentration, gas flow data and carbon dioxide molar mass to calculate the carbon emission amount.

[0011] Furthermore, obtaining the carbon emission amount at the current collection point to monitor the carbon emission amount includes: comparing the sum of the carbon emission amount monitored at the current moment and the carbon emission amount collected historically with the carbon emission index; in response to the sum of the carbon emission amount monitored at the current moment and the carbon emission amount collected historically being greater than the carbon emission index, an alarm is given. By judging whether the sum of the currently monitored carbon emission amount and the carbon emission amount at the historical collection point exceeds the carbon emission index, when it exceeds the carbon emission index, timely warning can be given, avoiding the problem of potential risks brought by excessive emissions.

[0012] Further, obtaining the predicted value of historical data includes: obtaining the predicted value of historical data by using a weighted average algorithm or an autoregressive method.

[0013] Further, collect concentration, including: collecting concentration data by using a carbon dioxide detector or a carbon dioxide sensor.

[0014] Further, collect the gas flow rate, including: measuring the flow rate of the gas discharge port by using a flow meter.

[0015] In the second aspect of the present invention, an online carbon emission monitoring system for the hose manufacturing process is further provided, including a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the online carbon emission monitoring method for the hose manufacturing process described in any one of the above.

[0016] Beneficial effects: By analyzing the deviation between the predicted value and the actual value of historical data, the quality of the data is obtained, and the contribution degree of historical data to the currently collected gas data is calculated by combining the time distance and the quality of the data. The contribution degree of the currently collected gas data is used to weight the difference degree between the historical data and the currently collected gas data, and the noise performance degree of the currently collected gas data is obtained, so as to obtain the correction value of the actual value of the currently collected gas data, making the concentration and gas flow rate data of real-time monitoring closer to the true value, and improving the accuracy of carbon emission monitoring. Description of the Drawings

[0017] Figure 1 is a flowchart showing the correction of currently collected data according to an embodiment of the present invention. Detailed Embodiments

[0018] In one embodiment, the online carbon emission monitoring system for the hose manufacturing process generally includes modules such as data collection, data transmission, data processing, early warning reports, and regulation standards. Different sensors are used to collect parameters such as the concentration and gas flow rate of the gas discharge port. The following will describe the detailed embodiments of the present invention with reference to the drawings.

[0019] As Figure 1 shown, step S101: Collect gas information.

[0020] In one embodiment, a data transmission device is built into each measuring device to measure the gas data of the gas discharge port in real time. Among them, the gas data includes concentration and gas flow rate, and the collected The data values of the concentration and gas flow rate are transmitted to the data processing center in real time. Specifically, the preset acquisition frequency can be once every 2 seconds, once every 3 seconds, once every 4 seconds, etc., and can be specifically set according to the actual scenario.

[0021] In one embodiment, a carbon dioxide detector or a carbon dioxide sensor can be used to collect the concentration data at the gas discharge port. A flowmeter can be used to measure the flow rate at the gas discharge port.

[0022] Step S102: Calculate the quality of the historical data.

[0023] It should be noted that in carbon emission monitoring, the quality of historical data is crucial for prediction and decision-making. However, if there are large fluctuations in historical data, the results of using historical data for prediction will be inaccurate. Therefore, it is necessary to first evaluate the quality of historical data, and the accuracy and reliability of the data can be evaluated by calculating the difference between the predicted value and the actual value of the historical data. The quality of historical data reflects the deviation between the predicted value and the actual value of historical data. When the deviation between the predicted value and the actual value is larger, the data quality is lower, and vice versa, the data quality is higher.

[0024] Specifically, the weighted average algorithm is used to obtain the predicted values of the real-time collected data and historical data. Specifically, according to the difference between the predicted value and the actual value of any historical data before the current collected gas data point, the difference between the difference between the predicted value and the actual value of any historical data before the current collected gas data and the average value of the differences, and the standard deviation of the differences between all historical predicted data and actual data before the current collected gas data, the quality of the historical data is calculated.

[0025] In one embodiment, to calculate the quality of the historical data, the calculation formula is: . In the formula, represents the quality of the th historical data; represents the actual value of the th historical data; represents the predicted value of the th historical data; represents the number of historical data before the current collected gas data point; represents the actual value of the th historical data excluding the th historical data in the historical data; represents the predicted value of the th historical data excluding the th historical data in the historical data.

[0026] Among them, represents the The absolute value of the difference between the residual of an actual historical data and the predicted value and the mean of the residuals of the actual and predicted values of other historical data except the th historical data among the historical data before the currently collected gas data. The larger this value is, the greater the difference between the residual of the actual and predicted values of the th historical data and the mean of the residuals of the whole (excluding the th historical data). It is considered that the th historical data deviates more from the overall trend compared to other historical data. And represents the standard deviation of the residuals of the actual and predicted values of other historical data except the th historical data among the historical data before the currently collected gas data point. The smaller this value is, the more stable the change in the magnitude of the residuals of other historical data. At this time, if the difference value is larger and the standard deviation is smaller, then the difference value is more significant, indicating that the quality of the th historical data is lower.

[0027] Step S103: Calculate the contribution degree of historical data to the currently collected gas data.

[0028] It should also be noted that in the carbon emission monitoring system, since data is collected at regular intervals, the data change should be relatively stable in a short period of time. As time goes by, the environment and operating conditions may change, which will lead to a weakening of the correlation between the early collected data and the current data. It is considered that the historical data closer to the currently collected gas data has a greater impact on the currently collected gas data. However, even if the time distance of the data points is relatively close, if their data quality is low, such as sensor failures or other abnormal factors, then these data cannot accurately reflect the actual situation. Therefore, combining the time distance and data quality to calculate the contribution degree of this historical data to the currently collected gas data can more comprehensively evaluate the contribution degree of historical data to the currently collected gas data, thus ensuring the accuracy and reliability of data analysis and prediction.

[0029] In one embodiment, calculate the contribution degree of this historical data to the currently collected gas data; the specific calculation formula is as follows: . In the formula, represents the contribution degree of the th historical data to the previously collected data; represents the time of the currently collected gas data; represents the time of the th historical data; represents the attenuation function; represents the quality of the th historical data.

[0030] Among them, represents the The time distance difference between a piece of historical data and the currently collected gas data. The smaller this value is, it indicates that the piece of historical data is closer to the currently collected gas data. Indicates the importance of the piece of historical data to the currently collected gas data. The larger this value is, it indicates that the importance of the piece of historical data to the currently collected gas data. The larger this value is, it indicates that the piece of historical data has a higher data quality among the entire historical data. If at this time the piece of historical data is also closer to the currently collected gas data, it means that the piece of historical data has a higher contribution degree to the currently collected gas data.

[0031] Step S104: Calculate the noise performance degree of the currently collected gas data.

[0032] In one embodiment, carbon emission data is often affected by various factors, such as weather, equipment status, human operation, etc. These factors may lead to data instability and uncertainty. Therefore, it is necessary to identify whether there is a large difference between the currently collected carbon emission data and recent historical data. If the difference between the current data and the historical data is significant, and this historical data has a high contribution degree due to high quality and close time, it indicates that the current data is very likely to be an outlier and needs further inspection or correction.

[0033] Specifically, obtain the difference between any historical data and the currently collected gas data as the difference degree between this historical data and the currently collected gas data, denoted as . According to the contribution degree of any historical data to the currently collected gas data, perform weighted calculation on the difference degree to calculate the noise performance degree of the currently collected gas data. The specific calculation formula is as follows: . In the formula, represents the noise performance degree of the currently collected gas data; represents the difference degree between the piece of historical data and the currently collected gas data, where the difference degree is the difference between this historical data and the currently collected gas data; represents the contribution degree of the piece of historical data to the currently collected gas data; represents the standard normalization function; represents the number of historical data before the currently collected gas data point.

[0034] Among them, represents the The product of the difference degree and contribution degree between a historical data and the currently collected gas data. The smaller this value is, it indicates that the difference between this historical data and the currently collected gas data is not significant, which may be because the data is relatively stable or the historical data has similar characteristics to the current data. At the same time, if the contribution degree is also low, it means that the importance of this historical data in evaluating the currently collected gas data is not high. Therefore, when the product of the difference degree and contribution degree is very small, it can be considered that the influence of this historical data on the noise performance degree of evaluating the currently collected gas data is small, that is, the currently collected gas data performs relatively stably or reliably under the comparison of this historical data. It represents the weighted average of the products of the difference degrees and contribution degrees between all historical data and the current data to obtain the noise performance degree of the currently collected gas data. The larger this value is, it indicates that there are significant differences between the currently collected data and multiple high-quality historical data that are close in time, which usually means that there may be relatively large noise in the current data. On the contrary, if this value is small, it indicates that the difference between the currently collected gas data and the historical data is small, and the currently collected data is relatively stable and reliable.

[0035] Step S105: Calculate the correction value of the actual value of the currently collected gas data.

[0036] In one embodiment, considering the importance and sensitivity of monitoring data, the accuracy of data is crucial for formulating emission reduction strategies, evaluating environmental impacts, etc. Due to various factors such as equipment accuracy, environmental factors, and interference during transmission, the collected data often has noise, that is, there is a deviation from the true value. To evaluate the authenticity and reliability of these data, based on the above-obtained noise performance degree of the currently collected gas data, calculate the confidence level of the actual value of the currently collected gas data. The greater the noise performance degree, the lower the confidence level; conversely, the smaller the noise performance degree, the higher the confidence level. If the confidence level of the actual value of the currently collected gas data is low, it indicates that there may be relatively large errors or uncertainties in this data. To remove the influence of noise and obtain an estimate closer to the true value, the average value of historical data can be combined to correct the currently collected gas data. The degree of correction depends on the level of confidence. The lower the confidence level, the greater the degree of correction, that is, the smaller the weight of the currently collected gas data in the corrected result, and the greater the weight of the historical data. When the confidence level is low, the corrected value will be closer to the average value of the historical data. On the contrary, when the confidence level is high, the corrected value will be closer to the original value of the currently collected data.

[0037] Specifically, obtain the confidence level of the currently collected gas data according to the noise performance degree of the currently collected gas data, so as to obtain the correction value of the actual value of any data in the currently collected gas data . The specific calculation formula is as follows: ; In the formula, represents the actual value of the currently collected gas data; represents the time of the currently collected gas data; represents the th data time before the currently collected gas data; represents the attenuation function; represents the th data actual value before the currently collected gas data; represents the natural exponential function, represents the number of all historical data before the currently collected gas data.

[0038] Among them, represents the confidence level of the actual value of the currently collected gas data. The larger this value is, the smaller the noise performance degree of the currently collected gas data is, and the higher the confidence level of the actual value of the currently collected gas data is; on the contrary, the larger the noise performance degree of the currently collected gas data is, the lower the confidence level of the actual value of the currently collected gas data is. represents the average value of the actual values of all the th data before the currently collected gas data. When is close to 1, it means that the actual value of the currently collected gas data has a high reliability, and the correction value will be closer to the actual value of the currently collected gas data . When is smaller, it means that the actual value of the currently collected gas data has a low reliability, and the correction value will be closer to the average value of the data within a historical period of time.

[0039] Step S106: Calculate the carbon emission at the current collection moment.

[0040] In one embodiment, the calculation method of the carbon emission includes: calculating the carbon emission by using the product of the correction value of the actual value of the concentration data collected at the current moment, the correction value of the actual value of the gas flow data at the current collection moment, and the molar mass of carbon dioxide. It should be noted that the method of calculating the carbon emission belongs to the prior art and will not be elaborated in detail.

[0041] Step S107: Monitor the carbon emission according to the carbon emission at the current collection point.

[0042] In one embodiment, compare the sum of the currently monitored carbon emission and the historically collected carbon emission with the carbon emission index; if the sum of the currently monitored carbon emission and the historically collected carbon emission is greater than the carbon emission index, then give an alarm.

[0043] ​Exemplarily, the historically collected carbon emissions are respectively , , , …… , and the carbon emission index of this enterprise is . If is less than , it indicates that the carbon emissions of this enterprise have not reached the carbon emission index of this enterprise, and then this enterprise can continue to emit. If is greater than , it indicates that the carbon emissions during the production and manufacturing of rubber hoses by this enterprise have reached the carbon emission index of this enterprise, and then an alarm is issued.

[0044] According to the above steps, the data quality is evaluated by analyzing the deviation between the predicted value and the actual value of the historical data, and the contribution degree of the historical data to the currently collected gas data is calculated in combination with the time distance and the data quality. Using these contribution degrees to weight the difference degree, the noise performance degree of the currently collected gas data of the concentration and gas flow is obtained, so as to evaluate its confidence level and obtain a correction value, making the data of the concentration and gas flow monitored in real time closer to the true value, and improving the accuracy of carbon emission monitoring.

[0045] The present invention also provides an on-line carbon emission monitoring system for the rubber hose manufacturing process. The system includes a processor and a memory, and the memory stores computer program instructions, which when executed by the processor implement an on-line carbon emission monitoring method for the rubber hose manufacturing process according to the first aspect of the present invention.

[0046] The system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be elaborated here.

[0047] In the present invention, the foregoing memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0048] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention.

Claims

1. A method for online monitoring of carbon emissions in a hose manufacturing process, characterized in that: include: Collecting gas data, the gas data includes concentration and gas flow rate; Calculate the time distance between the historical data of the gas data and the currently collected gas data, and obtain the contribution of the historical data according to the product of the time distance and the quality of the historical data, wherein the quality of the historical data reflects the deviation between the predicted value of the historical data and the actual value; The noise performance of the current collected gas data is obtained by weighted averaging the product of the difference and contribution of all historical data and the current collected gas data. , thereby calculating the correction value of the actual value of any data in the current collected gas data , the calculation formula is: ； In the formula, Indicates the actual value of the currently collected gas data; Indicates the time of current gas data collection; Indicates the number of gas data collected before the current one. The time of the data; represents the decay function; Indicates the number of gas data collected before the current one. The actual value of the data; represents the natural exponential function, Indicates the number of all historical data before the current gas data collection; According to the correction value of the actual value of the currently collected gas data, the carbon emissions at the current collection moment are obtained to monitor the carbon emissions.

2. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 1, characterized in that: The contribution of the historical data is calculated using the following formula: ； In the formula, Indicates The contribution of historical data to the currently collected gas data; Indicates the time of current gas data collection; Indicates The time of historical data; represents the decay function; Indicates The quality of historical data.

3. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 1, characterized in that: Calculate the quality of historical data, including: ； In the formula, Indicates The quality of historical data; Indicates The actual value of historical data; Indicates The predicted value of historical data; Indicates the number of historical data before the current gas data point; Indicates that the historical data is The historical data The actual value of historical data; Indicates that the historical data is The historical data The predicted value of historical data.

4. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 1, characterized in that: Get the noise performance of the current collected gas data , also includes: The weighted average value of the product of the difference and contribution of all historical data and the current collected gas data is obtained, and the value is normalized to obtain the noise performance degree of the current collected gas data.

5. The method for online monitoring of carbon emissions in a hose manufacturing process according to any one of claim 4, characterized in that: The method for calculating the carbon emissions includes: Carbon emissions are calculated using the product of the currently monitored carbon dioxide concentration, gas flow rate and carbon dioxide molar mass.

6. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 5, characterized in that: Obtain the carbon emissions at the current collection time to monitor carbon emissions, including: Compare the sum of the carbon emissions currently monitored and the carbon emissions collected historically with the carbon emissions index; In response to the sum of the carbon emissions monitored at the current moment and the carbon emissions collected historically being greater than the carbon emission index, an alarm is issued.

7. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 3, characterized in that: Get forecast values ​​for historical data, including: Use weighted average algorithm or autoregression method to obtain the predicted value of historical data.

8. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 1, characterized in that: collection Concentrations include: Use carbon dioxide detector or carbon dioxide sensor to collect concentration.

9. The method for online monitoring of carbon emissions in a hose manufacturing process according to claim 1, characterized in that: Collect gas flow, including: Use a flow meter to measure the flow rate of the gas discharge port.

10. An online carbon emission monitoring system for a hose manufacturing process, comprising a processor and a memory, wherein the memory stores a computer program, characterized in that: The processor executes the computer program to implement the method for online monitoring of carbon emissions in a hose manufacturing process as described in any one of claims 1-9.

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