Real-time monitoring system for carbon emission in bamboo wood processing process

Abstract

The invention is applicable to the field of bamboo processing, and provides a bamboo processing process carbon emission real-time monitoring system, which comprises a data acquisition module which is deployed in each key link and has a space-time synchronization function based on the space-time characteristics of the whole bamboo processing flow; a linkage monitoring curve reflecting the carbon emission conduction relation between links is generated; a pre-warning model fused with machine learning is constructed based on a linkage monitoring curve, and pre-judgment of a potential standard exceeding risk is realized by identifying an abnormal slope change value, an inter-link lag correlation deviation value and a cumulative effect threshold value of the linkage monitoring curve. Early warning information including a risk link, an association influence link and optimization suggestions is pushed, a carbon emission change trend and cross-link conduction influence are accurately captured through a multi-dimensional data linkage analysis and dynamic weight early warning model, and the problems that traditional monitoring is lagged and risks are difficult to prejudge are solved; and in combination with visual pushing and a closed-loop optimization mechanism, the risk is ensured to be managed and controlled in time.

Description

Technical Field

[0001] This invention belongs to the field of bamboo processing, and in particular relates to a real-time monitoring system for carbon emissions during bamboo processing. Background Technology

[0002] As an important component of the green economy, the monitoring and control of carbon emissions from the forestry industry's production processes has become a crucial aspect of its sustainable development. Bamboo, as a renewable resource, is characterized by its short growth cycle and high utilization rate, and is widely used in construction, furniture, papermaking, and other fields, leading to the continuous expansion of the bamboo processing industry. However, bamboo processing involves multiple stages, including raw material handling, hot pressing, and drying, each with varying degrees of energy consumption and carbon emissions. Accurately understanding the carbon emission status of each stage is a prerequisite for achieving a low-carbon transformation of the industry.

[0003] Currently, carbon emission monitoring in the bamboo processing industry relies heavily on traditional manual recording and post-event calculations, estimating emissions based on industry averages or empirical formulas. This approach suffers from significant data lag and insufficient early warning capabilities. Due to the diverse processing techniques and complex equipment types in bamboo processing, and the substantial differences in production processes among different enterprises, traditional monitoring methods struggle to reflect real-time carbon emission dynamics, fail to provide data support for immediate optimization of production processes, and are insufficient to meet the demand for precise carbon emission monitoring and early warning. Therefore, there is an urgent need to establish a real-time carbon emission monitoring system specifically for the bamboo processing industry. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time carbon emission monitoring system for bamboo processing, which aims to solve the problems mentioned in the background art.

[0005] This invention is achieved by providing a real-time carbon emission monitoring system for bamboo processing, the system comprising: Based on the spatiotemporal characteristics of the entire bamboo processing process, data acquisition modules with spatiotemporal synchronization functions are deployed at each key stage to collect multi-dimensional production data, including equipment energy consumption parameters, raw material conversion efficiency parameters, and environmental interaction parameters. Time stamping is used to achieve spatiotemporal alignment of data across stages. Edge computing nodes are used to preprocess the collected data, filter out noisy data and extract feature values, and then upload the data to the cloud data processing center in real time through an encrypted transmission protocol. A dynamic correlation algorithm is constructed to couple carbon emission data of each link with equipment operating status and raw material characteristic parameters in a multi-dimensional analysis, and generate linkage monitoring curves that reflect the carbon emission transmission relationship between links. The linkage monitoring curve includes three dimensions: real-time emission values, historical trend baseline, and cross-link influence coefficient. Based on the linkage monitoring curve, a pre-warning model integrating machine learning is constructed. By identifying abnormal slope changes, inter-link lag correlation deviations, and cumulative effect thresholds of the linkage monitoring curve, and dynamically adjusting the weights of the warning parameters in conjunction with production conditions, the model can predict potential risks of exceeding standards. The dynamic correlation process of the linkage monitoring curve is displayed in real time through the visual interactive module. When the front-end early warning model triggers risk judgment, it pushes early warning information including the risk link, the related impact link and optimization suggestions.

[0006] As a further aspect of the present invention, the construction of the dynamic correlation algorithm, which couples carbon emission data from each stage with equipment operating status and raw material characteristic parameters in a multi-dimensional manner to generate a linkage monitoring curve reflecting the carbon emission transmission relationship between stages, specifically includes: Determine the scope of production processes for multi-dimensional data collection, as well as the types of parameters to be collected, including carbon emissions, equipment operating status, and raw material characteristics. A dynamic correlation algorithm is constructed using a combination algorithm to calculate the correlation between equipment operating status, bamboo raw material characteristic parameters and carbon emissions, and to assign weights to each parameter. The matrix coupling method is used to perform coupled analysis on multi-dimensional data, and the comprehensive impact of each parameter on carbon emissions is quantified after the heterogeneous data is normalized. The system generates real-time emission values ​​for linkage monitoring curves by combining measured correction results, generates historical trend baselines based on historical normal operating condition data, and generates cross-stage impact coefficients through regression analysis. The real-time emission values ​​of the linkage monitoring curve are compared with the actual measured values ​​on site, and the correlation algorithm parameters are adjusted according to the deviation value to verify the accuracy of the curve.

[0007] As a further aspect of the present invention, the aforementioned pre-warning model based on the linkage monitoring curve and incorporating machine learning, which identifies abnormal slope changes, inter-stage lag correlation deviations, and cumulative effect thresholds of the linkage monitoring curve, and dynamically adjusts the weights of the warning parameters in conjunction with production conditions, specifically includes: An early warning model is constructed using an architecture that combines temporal networks and classifiers, and the types of input data and output risk assessment results of the early warning model are clearly defined. Analyze historical data to extract abnormal slope changes, inter-linkage lag correlation deviations, and cumulative effect thresholds from the linkage monitoring curves; Establish a mapping relationship between production conditions and the weights of early warning parameters, and adjust the weight ratio of each early warning parameter according to the real-time production conditions; A pre-warning model was trained using historical normal and exceeding operating condition data, and the accuracy of the model's judgment was verified by adjusting hyperparameters; The real-time monitoring curve data is input into the model, and dynamic weights are used to identify whether the feature parameters exceed the limits, calculate the risk probability, and determine the risk level.

[0008] As a further aspect of the present invention, the step of inputting real-time linkage monitoring curve data into the model, combining dynamic weights to identify whether feature parameters exceed limits, calculating the risk probability, and determining the risk level specifically includes: Obtain the dynamic weight parameters corresponding to the current production condition, associate and match the preprocessed real-time linkage monitoring curve data with the dynamic weights, and assign weight coefficients under the current condition to each feature parameter. Check each weighted feature parameter against the preset feature parameter thresholds to see if it exceeds the judgment range, and record the type and degree of the out-of-limit parameter. Based on the number of parameters exceeding the limits, the degree of exceeding the limits, and the corresponding weights, calculate the probability value of potential exceeding the limits; Based on the preset risk probability interval division standard, the calculated risk probability is matched with the interval to determine the low, medium and high risk levels, and a preliminary judgment result containing the risk probability and level is generated.

[0009] As a further aspect of the present invention, the dynamic correlation process of the linkage monitoring curve is displayed in real time through the visual interaction module. When the current early warning model triggers a risk judgment, the push of early warning information including the risk link, the related impact link, and optimization suggestions specifically includes: The design includes a visual interactive module with multiple data display formats, and the corresponding linkage monitoring curves and related data content for each format are determined. The visualization module content is updated synchronously through a real-time data transmission protocol, allowing users to interactively view the relationship between parameters and carbon emissions at a specific moment. When the current early warning model triggers a high risk, the risk links and related impact links are extracted and matched with historical cases to generate actionable optimization suggestions; Promptly send complete early warning information to relevant personnel through multiple channels after a risk is triggered; Record user feedback on the processing of early warning information, and add false alarm cases to the dataset to iteratively optimize the parameters of the early warning model.

[0010] As a further aspect of the present invention, when the current early warning model triggers a high risk, extracting the risk factors, associating the impact factors, and matching historical cases to generate actionable optimization suggestions specifically includes: When the early warning model triggers a high risk, it retrieves the linkage monitoring curve segment during the risk triggering period and the corresponding equipment operation and raw material characteristics raw data to lock in the time node of risk triggering and the core over-limit characteristic parameters. Based on the carbon emission transmission relationship recorded by the dynamic correlation algorithm, the carbon emission sources corresponding to the excess characteristic parameters are analyzed, and the links with carbon emissions exceeding the historical trend baseline and abnormal parameters are identified as core risk links. Based on the cross-link impact coefficient in the linkage monitoring curve, upstream and downstream links that are significantly related to the transmission of core risk links are screened to determine the links affected by risk transmission. Call the preset historical carbon emission optimization case library, and select historical valid cases that meet the preset standards based on multi-dimensional matching rules of risk link type, characteristic parameter over-limit mode and number of linked links; By combining current production conditions with optimization measures from historical cases, we determine the implementation steps, adjustment range of key parameters, and expected emission reduction effects of the optimization measures, and generate optimization recommendations.

[0011] As a further aspect of the present invention, the design includes a visualization and interactive module for various data display formats, and the determination of the linkage monitoring curves and related data content corresponding to each format specifically includes: The visualization and interactive module uses a combination of line charts, heatmaps, and data tables for display. Line charts are used to display the real-time emission values ​​and historical trend baselines of the linked monitoring curves, and support zooming and detailed viewing; Heatmaps are used to display cross-stage influence coefficients, and users can click to view the associated parameters of specific stages. Data tables are used to display raw data and support filtering by process and time.

[0012] As a further aspect of the present invention, the characteristics of the cross-stage influence coefficient specifically include: Directionality: Positive and negative values ​​distinguish the types of impact. Positive values ​​indicate that an increase in carbon emissions in a certain stage will lead to an increase in emissions in related stages in the same direction, while negative values ​​indicate a reverse impact. Intensity grading: The degree of influence is reflected by the absolute value. The larger the value, the more significant the transmission effect. The threshold for significant influence can be determined by combining the correlation with the production process. Dynamics: Based on real-time collection of multi-stage operation data, it is updated regularly. When process parameters are adjusted, raw materials are changed, or equipment status changes, it automatically recalculates to adapt to the new stage relationships. Correlation: It works in conjunction with real-time emission values ​​and historical trend baselines to explain the cross-stage transmission path of current emission fluctuations and to provide a basis for cross-stage correction of historical trend baselines.

[0013] This invention provides a real-time carbon emission monitoring system for bamboo processing. Through multi-dimensional data linkage analysis and a dynamic weighted early warning model, it accurately captures the trend of carbon emission changes and the cross-process transmission impact, solving the problems of traditional monitoring lag and difficulty in risk prediction. Combined with a visual push and closed-loop optimization mechanism, it ensures timely risk control, takes into account both environmental compliance and production efficiency, and provides reliable support for the refined management of carbon emissions in bamboo processing. Attached Figure Description

[0014] Figure 1 This is the main flowchart of a real-time carbon emission monitoring system for bamboo processing.

[0015] Figure 2 This is a flowchart of a real-time carbon emission monitoring system for bamboo processing. It uses a dynamic correlation algorithm to perform multi-dimensional coupling analysis of carbon emission data from each stage with equipment operating status and raw material characteristic parameters, generating a linkage monitoring curve that reflects the carbon emission transmission relationship between stages.

[0016] Figure 3 This is a flowchart in a real-time carbon emission monitoring system for bamboo processing that uses a visual interactive module to display the dynamic correlation process of the linked monitoring curves. When the pre-set early warning model triggers a risk assessment, it pushes early warning information including the risk link, the related impact links, and optimization suggestions.

[0017] Figure 4 This is a flowchart in a real-time carbon emission monitoring system for bamboo processing that, when the current early warning model triggers a high-risk event, extracts the risk factors, correlates the influencing factors, and matches historical cases to generate actionable optimization suggestions. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0020] The present invention provides a real-time carbon emission monitoring system for bamboo processing, which solves the technical problems in the background art.

[0021] like Figure 1 The diagram shown is a main structural diagram of a real-time carbon emission monitoring system for bamboo processing according to an embodiment of the present invention. The real-time carbon emission monitoring system for bamboo processing includes: Step S100: Based on the spatiotemporal characteristics of the entire bamboo processing process, deploy data acquisition modules with spatiotemporal synchronization functions at each key stage to collect multi-dimensional production data, including equipment energy consumption parameters, raw material conversion efficiency parameters, and environmental interaction parameters, and achieve spatiotemporal alignment of cross-stage data through timestamp marking; Step S200: The collected data is preprocessed using edge computing nodes, noise data is filtered and feature values ​​are extracted, and then the data is uploaded to the cloud data processing center in real time through an encrypted transmission protocol. Step S300: Construct a dynamic correlation algorithm to perform multi-dimensional coupling analysis of carbon emission data at each stage with equipment operating status and raw material characteristic parameters, and generate a linkage monitoring curve that reflects the carbon emission transmission relationship between stages; The linkage monitoring curve includes three dimensions: real-time emission values, historical trend baseline, and cross-link influence coefficient. Step S400: Construct a pre-warning model based on the linkage monitoring curve and integrating machine learning. By identifying abnormal slope changes, inter-link lag correlation deviations, and cumulative effect thresholds of the linkage monitoring curve, and dynamically adjusting the weights of the warning parameters in conjunction with production conditions, the model can predict potential risks of exceeding standards. Step S500: The dynamic correlation process of the linkage monitoring curve is displayed in real time through the visualization interaction module. When the front-end early warning model triggers risk judgment, early warning information including risk links, related impact links and optimization suggestions is pushed. In this embodiment, the key stages of the entire bamboo processing flow are specifically the six core stages: post-harvest processing, slicing, steaming, drying, hot pressing, and surface treatment. These are all carbon-intensive stages in bamboo processing. Multi-dimensional production data includes equipment energy consumption parameters such as slicer power and dryer heating power; raw material conversion efficiency parameters such as bamboo utilization rate in the slicing stage and finished product rate in the forming stage; and environmental interaction parameters such as steam emissions in the steaming stage and greenhouse gas concentrations in the drying stage. All of these are key factors affecting carbon emissions from bamboo processing. The dynamic correlation algorithm uses a combination of Pearson correlation coefficient and grey relational analysis. The former calculates linear correlations, while the latter supplements nonlinear correlations. The generated linkage monitoring curve provides basic data for subsequent early warnings. The pre-warning model integrates an LSTM time-series network and a random forest classifier, with production condition weights adjusted for reference. For example, when operating at full capacity, the weight of equipment parameters is increased to 50%. The visual interaction module pushes early warning information through system pop-ups, SMS (to environmental specialists), and the enterprise APP, ensuring timely delivery of risk information and effectively solving the problems of lagging carbon emission monitoring and difficulty in risk prediction in bamboo processing, while balancing environmental compliance and production efficiency.

[0022] like Figure 2As shown, in another preferred embodiment of the present invention, the construction of the dynamic correlation algorithm, which couples carbon emission data of each stage with equipment operating status and raw material characteristic parameters in a multi-dimensional manner to generate a linkage monitoring curve reflecting the carbon emission transmission relationship between stages, specifically includes: Step S301: Determine the scope of the production process for multi-dimensional data collection and the types of parameters to be collected, including carbon emissions, equipment operating status, and raw material characteristics; Step S302: Use a combination algorithm to construct a dynamic correlation algorithm, calculate the correlation between equipment operating status, bamboo raw material characteristic parameters and carbon emissions, and assign weights to each parameter; Step S303: Perform coupled analysis on multi-dimensional data using matrix coupling method, and quantify the comprehensive impact of each parameter on carbon emissions after normalizing the heterogeneous data; Step S304: Combine the measured correction results to generate the real-time emission value of the linkage monitoring curve, generate the historical trend baseline based on historical normal operating condition data, and generate the cross-link influence coefficient through regression analysis; Step S305: Compare the real-time emission values ​​of the linkage monitoring curve with the actual measured values ​​on site, and adjust the correlation algorithm parameters according to the deviation value to verify the accuracy of the curve; In this embodiment, the multi-dimensional data collection scope focuses on four high-emission stages of bamboo processing: slicing, steaming, drying, and hot pressing. Parameter types are refined as follows: carbon emission data includes instantaneous and cumulative emissions for each stage; equipment operating status parameters include steaming tank temperature, dryer wind speed, and hot press pressure; bamboo raw material characteristic parameters include bamboo moisture content, fiber content, and feeding rate, ensuring coverage of core influencing factors. In the combined algorithm, the Pearson correlation coefficient calculates the linear correlation between equipment / raw material parameters and carbon emissions (values ​​range from 0 to 1, with ≥0.6 considered a strong correlation). Grey relational analysis supplements non-linear correlations. Weight allocation is calculated based on the correlation degree, with strong correlation parameters having a weight ≥40%, avoiding interference from weak correlation parameters in the analysis results. A matrix coupling method constructs a 3×3 coupling matrix, and the min-max method is used for heterogeneous data normalization. Matrix multiplication quantifies the comprehensive influence value, eliminating analytical bias caused by differences in data units. During the generation of the linkage curve dimension, the real-time emission value is corrected by combining the measured value of the on-site infrared carbon emission monitoring instrument; the historical trend baseline uses data from the past 3 months of normal operating conditions, and the cross-link influence coefficient is calculated through linear regression until the deviation reaches the standard, ensuring that the curve can truly reflect the carbon emission transmission relationship between bamboo processing links.

[0023] In another preferred embodiment of the present invention, the pre-warning model based on the linkage monitoring curve and incorporating machine learning, which identifies abnormal slope changes, inter-stage lag correlation deviations, and cumulative effect thresholds of the linkage monitoring curve, and dynamically adjusts the weights of the warning parameters in conjunction with production conditions, specifically includes: An early warning model is constructed using an architecture that combines temporal networks and classifiers, and the types of input data and output risk assessment results of the early warning model are clearly defined. Analyze historical data to extract abnormal slope changes, inter-linkage lag correlation deviations, and cumulative effect thresholds from the linkage monitoring curves; Establish a mapping relationship between production conditions and the weights of early warning parameters, and adjust the weight ratio of each early warning parameter according to the real-time production conditions; A pre-warning model was trained using historical normal and exceeding operating condition data, and the accuracy of the model's judgment was verified by adjusting hyperparameters; The real-time linkage monitoring curve data is input into the model, and dynamic weights are used to identify whether the feature parameters exceed the limits, calculate the risk probability, and determine the risk level. In this embodiment, the model architecture uses an LSTM time series network and a random forest classifier (outputting low, medium, and high risk and probability). LSTM excels at capturing the trend of carbon emissions over time, while random forest has strong anti-overfitting capabilities. The input data is clearly defined as the real-time emission values ​​of the linkage curve, the historical trend baseline deviation, and the cross-stage influence coefficient. The output risk results include the risk level and probability (0-1). The judgment criteria are extracted based on the historical data of bamboo processing over the past year: the abnormal slope change value is set to a normal range of ±0.1; the inter-stage lag correlation deviation value is set to a normal lag time, such as a 30-minute lag in the carbon emission response from steaming to drying, with a deviation exceeding 20%, which is judged as abnormal; the cumulative effect threshold is set to the emission value exceeding the historical baseline by 10% for three consecutive sampling periods. A weight mapping relationship is established to correspond to the production conditions and parameter weights: during full-load production, the weight of equipment operating status parameters can be increased from 30% to 50%; during raw material change conditions, the weight of raw material characteristic parameters can be increased from 25% to 45%. The weight adjustment is executed in real time through a dynamic weight algorithm to avoid the problem of poor adaptability of fixed weights. Model training and validation employed a 7:3 data split (70% training, 30% validation), with 100 LSTM iterations, 64 hidden layer nodes, and 50 random forest decision trees. Hyperparameters were adjusted to ensure a validation set accuracy ≥95% and a false positive rate ≤3%, guaranteeing model reliability. The risk prediction process inputs real-time linkage curve data into the model, combining it with dynamic weights based on the current operating conditions to identify whether feature parameters exceed limits. A weighted summation is used to calculate the risk probability (e.g., if two parameters exceed limits, with corresponding weights of 0.5 and 0.3, exceeding degrees of 0.2 and 0.3 respectively, the probability = 0.5 × 0.2 + 0.3 × 0.3 = 0.19). Finally, a risk range is matched (low risk < 0.3, medium risk in the 0.3-0.8 range, high risk > 0.8).

[0024] In another preferred embodiment of the present invention, the step of inputting real-time linkage monitoring curve data into the model, combining dynamic weights to identify whether feature parameters exceed limits, calculating risk probability, and determining risk level specifically includes: Obtain the dynamic weight parameters corresponding to the current production condition, associate and match the preprocessed real-time linkage monitoring curve data with the dynamic weights, and assign weight coefficients under the current condition to each feature parameter. Check each weighted feature parameter against the preset feature parameter thresholds to see if it exceeds the judgment range, and record the type and degree of the out-of-limit parameter. Based on the number of parameters exceeding the limits, the degree of exceeding the limits, and the corresponding weights, calculate the probability value of potential exceeding the limits; Based on the preset risk probability interval division standard, the calculated risk probability is matched with the interval to determine the low, medium and high risk levels, and a preliminary judgment result containing the risk probability and level is generated.

[0025] In this embodiment, based on the established working condition-weight mapping relationship, dynamic weight parameters can be obtained in real time from the local bamboo processing and production management system. For example, if the current hot pressing stage is operating at full capacity, the system will provide a weight of 0.5 for the equipment pressure parameter and 0.3 for the raw material fiber content under this working condition. When matching the preprocessed real-time linkage curve data with the weights, key-value pair mapping is used to assign a unique weight coefficient to each feature parameter, avoiding calculation deviations caused by weight mismatch and ensuring that the weights are adapted to the current production state. In the threshold comparison, the preset feature parameter thresholds directly adopt the standards extracted in claim 3 (abnormal slope ±0.1, hysteresis deviation 20%, cumulative effect 10%). During the check, the parameters-weights-thresholds are verified one by one, and tabular records are used to facilitate subsequent probability calculation and traceability. The risk probability calculation uses a weighted summation algorithm. The preset range for risk level determination refers to industry standards and actual enterprise needs: low risk < 0.3 (emission fluctuations are within a controllable range and no intervention is required), medium risk 0.3-0.8 (trends need to be monitored and adjustment measures prepared), and high risk > 0.8 (immediate intervention is required to avoid exceeding the limit). When determining the risk level, the calculated probability is matched with the range to generate a preliminary result such as "low risk, probability 14.5%", and the determination basis (exceeding the limit parameter, weight, degree) is recorded at the same time.

[0026] like Figure 3 As shown, in another preferred embodiment of the present invention, the dynamic correlation process of the linkage monitoring curve is displayed in real time through the visualization interaction module. When the front-end early warning model triggers a risk judgment, the push of early warning information including the risk link, the related impact link, and optimization suggestions specifically includes: Step S501: Design a visualization and interactive module that includes multiple data display formats, and determine the corresponding linkage monitoring curves and related data content for each format; Step S502: Synchronously update the content of the visualization module through the real-time data transmission protocol, and support users to interactively view the relationship between parameters and carbon emissions at a specific time. Step S503: When the current early warning model triggers a high risk, extract the risk links, associate the impact links, and match historical cases to generate actionable optimization suggestions; Step S504: Promptly push complete early warning information to relevant personnel through multiple channels after the risk is triggered; Step S505: Record the user's feedback on the processing of the warning information, and add false alarm cases to the dataset to iteratively optimize the parameters of the pre-warning model; In this embodiment, the visualization module is designed with a combination of line charts, heatmaps, and data tables, meeting human-computer interaction requirements: the line chart uses solid red lines to display real-time emission values ​​and dashed blue lines to display historical trend baselines, supporting scaling over 1 hour, 24 hours, and 7 days (adapting to different management needs), and hovering the mouse displays specific values; the heatmap uses each stage as a row and related stages as columns, with darker colors indicating larger absolute values ​​of cross-stage influence coefficients, and clicking on cells displays related parameters; the data table columns include stages, collection time, equipment parameters, raw material parameters, and emission values, supporting filtering by stage and time for easy tracing of original data. Data is updated synchronously every 5 minutes. When a user clicks on a data point in the line chart, the system automatically displays the equipment operating status, raw material characteristics, and cross-stage influence coefficients at that moment, providing a clear and intuitive presentation of the correlation logic between parameter changes and carbon emission response, helping users quickly pinpoint the causes of emission fluctuations. In handling high-risk situations, optimization suggestions are matched with the historical case library; multi-channel push notifications are completed within 1 minute after the risk is triggered, the system pop-up is pinned to the top of the visualization module homepage, SMS messages are sent to production managers and environmental protection specialists, and enterprise APP pushes a link containing the complete early warning report; feedback iteration records user handling results, false alarm cases can be added to the model training dataset, and model parameters are iterated once per quarter to continuously improve the accuracy of early warnings.

[0027] like Figure 4 As shown, in another preferred embodiment of the present invention, when the current early warning model triggers a high risk, extracting the risk link, associating the influencing link, and matching historical cases to generate actionable optimization suggestions specifically includes: Step S5031: When the early warning model triggers a high risk, retrieve the linkage monitoring curve segment of the triggered risk period and the corresponding equipment operation and raw material characteristics raw data to lock the time node of risk triggering and the core over-limit characteristic parameters; Step S5032: Based on the carbon emission transmission relationship recorded by the dynamic correlation algorithm, analyze the carbon emission sources corresponding to the exceedance characteristic parameters, locate the links whose carbon emissions exceed the historical trend baseline and whose parameters are abnormal, and identify them as core risk links; Step S5033: Based on the cross-link impact coefficient in the linkage monitoring curve, screen the upstream and downstream links that are significantly related to the transmission of core risk links, and determine the related impact links affected by risk transmission; Step S5034: Call the preset historical carbon emission optimization case library, and select historical valid cases that meet the preset standards in terms of similarity to the current high-risk scenario according to the multi-dimensional matching rules of risk link type, characteristic parameter over-limit mode and number of linked links; Step S5035: Based on the current production conditions, adjust the optimization measures in historical cases, determine the implementation steps of the optimization measures, the adjustment range of key parameters and the expected emission reduction effect, and generate optimization suggestions; In this embodiment, during risk data retrieval, when the pre-warning model triggers a high risk, it automatically retrieves the linked monitoring curve segment, raw equipment operation data, and raw material characteristic data for the risk period from the cloud data center. Timestamp matching is used to pinpoint the risk trigger node and core exceeding-limit characteristic parameters. The core risk link is located based on the transmission relationship recorded by the dynamic correlation algorithm, analyzing the carbon emission sources corresponding to the exceeding parameters: if the real-time emission value of the drying link exceeds the historical trend baseline by 20% and the equipment parameters are abnormal, the drying link is determined to be the core risk link, avoiding misjudgment of non-core links. The screening of related impact links is based on the generated cross-link impact coefficients, screening for upstream and downstream links significantly related to the drying link: the upstream cooking link has an impact coefficient of 0.6 (positive value, positive impact), and the downstream hot pressing link has an impact coefficient of 0.4 (positive value). Since the absolute values ​​of both coefficients are ≥0.3 (significant impact threshold), cooking and hot pressing are determined to be related impact links (excessive emissions in the drying link may lead to a chain reaction of increased emissions in the hot pressing link). Meanwhile, historical case matching utilizes a pre-set bamboo processing carbon emission optimization case library, selecting historical cases with a similarity ≥80%. This case library is updated monthly to include the latest valid cases, ensuring the timeliness of the recommendations. Optimization suggestions are generated by adjusting historical case measures based on current production conditions, while also highlighting potential emissions changes during the hot pressing process.

[0028] In another preferred embodiment of the present invention, the design includes a visualization and interactive module with multiple data display formats, and the determination of the linkage monitoring curves and related data content corresponding to each format specifically includes: The visualization and interactive module uses a combination of line charts, heatmaps, and data tables for display. Line charts are used to display the real-time emission values ​​and historical trend baselines of the linked monitoring curves, and support zooming and detailed viewing; Heatmaps are used to display cross-stage influence coefficients, and users can click to view the associated parameters of specific stages. Data tables are used to display raw data and support filtering by process and time.

[0029] In another preferred embodiment of the present invention, the characteristics of the cross-stage influence coefficient specifically include: Directionality: Positive and negative values ​​distinguish the types of impact. Positive values ​​indicate that an increase in carbon emissions in a certain stage will lead to an increase in emissions in related stages in the same direction, while negative values ​​indicate a reverse impact. Intensity grading: The degree of influence is reflected by the absolute value. The larger the value, the more significant the transmission effect. The threshold for significant influence can be determined by combining the correlation with the production process. Dynamics: Based on real-time collection of multi-stage operation data, it is updated regularly. When process parameters are adjusted, raw materials are changed, or equipment status changes, it automatically recalculates to adapt to the new stage relationships. Correlation: It works in conjunction with real-time emission values ​​and historical trend baselines to explain the cross-stage transmission path of current emission fluctuations and to provide a basis for cross-stage correction of historical trend baselines.

[0030] The above embodiments of the present invention provide a real-time carbon emission monitoring system for bamboo processing. The key links in the entire bamboo processing process are the six core links of bamboo post-harvest processing, slicing, steaming, drying, hot pressing and forming, and surface treatment, all of which are carbon emission intensive links in bamboo processing. Among the multi-dimensional production data, the equipment energy consumption parameters include the power of the slicing machine and the heating power of the dryer; the raw material conversion efficiency parameters include the bamboo utilization rate in the slicing link and the finished product rate in the forming link; and the environmental interaction parameters include the steam emission in the steaming link and the greenhouse gas concentration in the drying link, all of which are key factors affecting the carbon emissions of bamboo processing. The dynamic correlation algorithm employs a combination of Pearson correlation coefficient and grey relational analysis. The former calculates linear correlations, while the latter supplements nonlinear correlations. The generated linkage monitoring curves provide foundational data for subsequent early warnings. The pre-warning model integrates LSTM time-series networks and random forest classifiers, with production condition weights adjusted for reference. For example, when operating at full capacity, the weight of equipment parameters is increased to 50%. The early warning push notifications from the visualization interaction module cover system pop-ups, SMS (environmental specialists), and enterprise apps, ensuring timely delivery of risk information. This effectively addresses the issues of lagging carbon emission monitoring and difficulty in predicting risks in bamboo processing, balancing environmental compliance and production efficiency. The multi-dimensional data collection scope focuses on four high-emission stages in bamboo processing: slicing, cooking, drying, and hot pressing. Parameter types are refined as follows: carbon emission data includes instantaneous and cumulative emission values ​​for each stage; equipment operating status parameters include cooking tank temperature, dryer wind speed, and hot press pressure; and bamboo raw material characteristic parameters include bamboo moisture content, fiber content, and feeding rate, ensuring coverage of core influencing factors. In the combined algorithm, the Pearson correlation coefficient is used to calculate the linear correlation between equipment / raw material parameters and carbon emissions (values ​​range from 0 to 1, with ≥0.6 considered a strong correlation). Grey relational analysis supplements the nonlinear correlation. Weight allocation is calculated based on the proportion of correlation degree, with strong correlation parameters having a weight of ≥40% to avoid weak correlation parameters interfering with the analysis results. A matrix coupling method is used to construct a 3×3 coupling matrix. Heterogeneous data normalization uses the min-max method, and the comprehensive influence value is quantified through matrix multiplication to eliminate analytical bias caused by differences in data units. In the generation of the linkage curve dimension, real-time emission values ​​are corrected by combining on-site infrared carbon emission monitoring instrument measurements; the historical trend baseline uses data from the past three months of normal operating conditions, and the cross-stage influence coefficient is calculated through linear regression until the deviation reaches the standard, ensuring that the curve can truly reflect the carbon emission transmission relationship between bamboo processing stages.

[0031] The model architecture uses an LSTM time series network and a random forest classifier (outputting low, medium, and high risk and probability). LSTM excels at capturing the trend of carbon emissions over time, while random forest has strong anti-overfitting capabilities. The input data are explicitly defined as real-time emission values ​​of the linkage curve, historical trend baseline deviation, and cross-stage influence coefficients. The output risk results include risk level and probability (0-1). The judgment criteria are extracted based on historical bamboo processing data from the past year: the abnormal slope change value is set to a normal range of ±0.1; the inter-stage lag correlation deviation value is set to a normal lag time, such as a 30-minute lag in carbon emission response from steaming to drying, with a deviation exceeding 20%, which is judged as abnormal; the cumulative effect threshold is set to emission values ​​exceeding the historical baseline by 10% for three consecutive sampling periods. A weight mapping relationship is established to correspond to production conditions and parameter weights: during full-load production, the weight of equipment operating status parameters can be increased from 30% to 50%; during raw material change conditions, the weight of raw material characteristic parameters can be increased from 25% to 45%. Weight adjustments are executed in real time through a dynamic weight algorithm to avoid the problem of poor adaptability of fixed weights. The model training and validation adopted a 7:3 data split (70% training, 30% validation), with 100 LSTM iterations, 64 hidden layer nodes, and 50 random forest decision trees. By adjusting the hyperparameters, the validation set accuracy was ≥95% and the false positive rate was ≤3%, ensuring the reliability of the model. The risk prediction process involves inputting real-time linkage curve data into the model, combining it with dynamic weights based on the current operating conditions, identifying whether feature parameters exceed limits, and calculating the risk probability through weighted summation (e.g., if there are two parameters exceeding limits, with corresponding weights of 0.5 and 0.3, and exceeding degrees of 0.2 and 0.3 respectively, the probability = 0.5 × 0.2 + 0.3 × 0.3 = 0.19). Finally, a risk range is matched (low risk < 0.3, medium risk in the range of 0.3-0.8, high risk > 0.8). Based on the established operating condition-weight mapping relationship, dynamic weight parameters can be obtained in real-time from the local bamboo processing production management system. For example, if the current hot-pressing stage is operating at full capacity, the system will provide a weight of 0.5 for the equipment pressure parameter and 0.3 for the raw material fiber content under this condition. When matching the pre-processed real-time linkage curve data with the weights, a key-value pair mapping is used to assign a unique weight coefficient to each feature parameter, avoiding calculation deviations caused by weight mismatches and ensuring that the weights are adapted to the current production state. In the threshold comparison, the preset feature parameter thresholds directly adopt the standards extracted in claim 3 (abnormal slope ±0.1, lag bias 20%, cumulative effect 10%). During the check, the parameters-weights-thresholds are verified one by one, and tabular records are used to facilitate subsequent probability calculation and traceability.The risk probability calculation uses a weighted summation algorithm. The preset range for risk level determination refers to industry standards and actual enterprise needs: low risk < 0.3 (emission fluctuations are within a controllable range and no intervention is required), medium risk 0.3-0.8 (trends need to be monitored and adjustment measures prepared), and high risk > 0.8 (immediate intervention is required to avoid exceeding the limit). When determining the risk level, the calculated probability is matched with the range to generate a preliminary result such as "low risk, probability 14.5%", and the determination basis (exceeding the limit parameter, weight, degree) is recorded at the same time.

[0032] The visualization module design employs a combination of line charts, heatmaps, and data tables, meeting human-computer interaction requirements: the line chart uses solid red lines to display real-time emission values ​​and dashed blue lines to display historical trend baselines, supporting scaling across 1-hour, 24-hour, and 7-day time dimensions (adapting to different management needs), with specific values ​​displayed when the mouse hovers over them; the heatmap uses rows for each emission stage and columns for related stages, with darker colors indicating larger absolute values ​​of cross-stage influence coefficients, and clicking on cells displays related parameters; the data table columns include stage, collection time, equipment parameters, raw material parameters, and emission values, supporting filtering by stage and time for easy traceability of original data. Data is updated synchronously every 5 minutes. When a user clicks on a data point in the line chart, the system automatically displays the equipment operating status, raw material characteristics, and cross-stage influence coefficients at that moment, providing a clear and intuitive presentation of the correlation between parameter changes and carbon emission response, helping users quickly pinpoint the causes of emission fluctuations. In high-risk handling, optimization suggestions are matched with the historical case library; multi-channel push notifications are completed within 1 minute of risk triggering, with system pop-ups pinned to the top of the visualization module homepage, SMS messages sent to production managers and environmental specialists, and enterprise APP pushes containing links to complete early warning reports; feedback iteration records user handling results, false alarm cases can be added to the model training dataset, model parameters are iterated once per quarter to continuously improve early warning accuracy; in risk data retrieval, when the pre-warning model triggers high risk, it automatically retrieves linkage monitoring curve segments, raw equipment operation data, and raw material characteristic data for the risk period from the cloud data center, and uses timestamp matching to lock the risk trigger node and core exceeding limit characteristic parameters. Core risk link positioning is based on the transmission relationship recorded by the dynamic correlation algorithm, analyzing the carbon emission sources corresponding to the exceeding parameters: the real-time emission value of the drying link exceeds the historical trend baseline by 20%, and the equipment parameters are abnormal, thus determining the drying link as the core risk link, avoiding misjudging non-core links. The screening of related impact links is based on the generated cross-link impact coefficients, identifying upstream and downstream links significantly related to the drying process: the upstream cooking link has an impact coefficient of 0.6 (positive value, positive impact), and the downstream hot pressing link has an impact coefficient of 0.4 (positive value). Since the absolute values ​​of both coefficients are ≥0.3 (significant impact threshold), cooking and hot pressing are determined to be related impact links (excessive emissions in the drying process may lead to a chain reaction of increased emissions in the hot pressing process). Simultaneously, historical case matching is performed using a pre-set bamboo processing carbon emission optimization case library, selecting historical cases with a similarity ≥80%. The case library is updated monthly, adding the latest valid cases to ensure the timeliness of the recommendations. The generated optimization recommendations adjust the measures from historical cases based on current production conditions, while also highlighting the need to monitor changes in emissions from the hot pressing process.

[0033] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0034] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0035] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0036] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time carbon emission monitoring system for bamboo processing, characterized in that, The system includes: Based on the spatiotemporal characteristics of the entire bamboo processing process, data acquisition modules with spatiotemporal synchronization functions are deployed at each key stage to collect multi-dimensional production data, including equipment energy consumption parameters, raw material conversion efficiency parameters, and environmental interaction parameters. Time stamping is used to achieve spatiotemporal alignment of data across stages. Edge computing nodes are used to preprocess the collected data, filter out noisy data and extract feature values, and then upload the data to the cloud data processing center in real time through an encrypted transmission protocol. A dynamic correlation algorithm is constructed to couple carbon emission data of each link with equipment operating status and raw material characteristic parameters in a multi-dimensional analysis, and generate linkage monitoring curves that reflect the carbon emission transmission relationship between links. The linkage monitoring curve includes three dimensions: real-time emission values, historical trend baseline, and cross-link influence coefficient. Based on the linkage monitoring curve, a pre-warning model integrating machine learning is constructed. By identifying abnormal slope changes, inter-link lag correlation deviations, and cumulative effect thresholds of the linkage monitoring curve, and dynamically adjusting the weights of the warning parameters in conjunction with production conditions, the model can predict potential risks of exceeding standards. The dynamic correlation process of the linkage monitoring curve is displayed in real time through the visual interactive module. When the front-end early warning model triggers risk judgment, it pushes early warning information including the risk link, the related impact link and optimization suggestions.

2. The real-time carbon emission monitoring system for bamboo processing according to claim 1, characterized in that, The aforementioned dynamic correlation algorithm, which couples carbon emission data from each stage with equipment operating status and raw material characteristic parameters in a multi-dimensional manner, generates a linkage monitoring curve reflecting the carbon emission transmission relationship between stages. Specifically, this includes: Determine the scope of production processes for multi-dimensional data collection, as well as the types of parameters to be collected, including carbon emissions, equipment operating status, and raw material characteristics. A dynamic correlation algorithm is constructed using a combination algorithm to calculate the correlation between equipment operating status, bamboo raw material characteristic parameters and carbon emissions, and to assign weights to each parameter. The matrix coupling method is used to perform coupled analysis on multi-dimensional data, and the comprehensive impact of each parameter on carbon emissions is quantified after the heterogeneous data is normalized. The system generates real-time emission values ​​for linkage monitoring curves by combining measured correction results, generates historical trend baselines based on historical normal operating condition data, and generates cross-stage impact coefficients through regression analysis. The real-time emission values ​​of the linkage monitoring curve are compared with the actual measured values ​​on site, and the correlation algorithm parameters are adjusted according to the deviation value to verify the accuracy of the curve.

3. The real-time carbon emission monitoring system for bamboo processing according to claim 1, characterized in that, The aforementioned early warning model, which integrates machine learning and is based on the linkage monitoring curve, identifies abnormal slope changes, inter-stage lag correlation deviations, and cumulative effect thresholds of the linkage monitoring curve. It then dynamically adjusts the weights of the early warning parameters based on production conditions to predict potential exceedance risks. Specifically, this includes: An early warning model is constructed using an architecture that combines temporal networks and classifiers, and the types of input data and output risk assessment results of the early warning model are clearly defined. Analyze historical data to extract abnormal slope changes, inter-linkage lag correlation deviations, and cumulative effect thresholds from the linkage monitoring curves; Establish a mapping relationship between production conditions and the weights of early warning parameters, and adjust the weight ratio of each early warning parameter according to the real-time production conditions; A pre-warning model was trained using historical normal and exceeding operating condition data, and the accuracy of the model's judgment was verified by adjusting hyperparameters; The real-time monitoring curve data is input into the model, and dynamic weights are used to identify whether the feature parameters exceed the limits, calculate the risk probability, and determine the risk level.

4. The real-time carbon emission monitoring system for bamboo processing according to claim 3, characterized in that, The process of inputting real-time monitoring curve data into the model, combining dynamic weights to identify whether feature parameters exceed limits, calculating risk probability, and determining risk level specifically includes: Obtain the dynamic weight parameters corresponding to the current production condition, associate and match the preprocessed real-time linkage monitoring curve data with the dynamic weights, and assign weight coefficients under the current condition to each feature parameter. Check each weighted feature parameter against the preset feature parameter thresholds to see if it exceeds the judgment range, and record the type and degree of the out-of-limit parameter. Based on the number of parameters exceeding the limits, the degree of exceeding the limits, and the corresponding weights, calculate the probability value of potential exceeding the limits; Based on the preset risk probability interval division standard, the calculated risk probability is matched with the interval to determine the low, medium and high risk levels, and a preliminary judgment result containing the risk probability and level is generated.

5. The real-time carbon emission monitoring system for bamboo processing according to claim 1, characterized in that, The dynamic correlation process of the linkage monitoring curve is displayed in real time through a visual interactive module. When the front-end early warning model triggers a risk judgment, the early warning information that includes the risk link, the related impact link, and optimization suggestions is pushed. Specifically, this includes: The design includes a visual interactive module with multiple data display formats, and the corresponding linkage monitoring curves and related data content for each format are determined. The visualization module content is updated synchronously through a real-time data transmission protocol, allowing users to interactively view the relationship between parameters and carbon emissions at a specific moment. When the current early warning model triggers a high risk, the risk links and related impact links are extracted and matched with historical cases to generate actionable optimization suggestions; Promptly send complete early warning information to relevant personnel through multiple channels after a risk is triggered; Record user feedback on the processing of early warning information, and add false alarm cases to the dataset to iteratively optimize the parameters of the early warning model.

6. The real-time carbon emission monitoring system for bamboo processing according to claim 5, characterized in that, When the current early warning model triggers a high risk, the extraction of risk factors, correlation of impact factors, and matching with historical cases to generate actionable optimization suggestions specifically include: When the early warning model triggers a high risk, it retrieves the linkage monitoring curve segment during the risk triggering period and the corresponding equipment operation and raw material characteristics raw data to lock in the time node of risk triggering and the core over-limit characteristic parameters. Based on the carbon emission transmission relationship recorded by the dynamic correlation algorithm, the carbon emission sources corresponding to the excess characteristic parameters are analyzed, and the links with carbon emissions exceeding the historical trend baseline and abnormal parameters are identified as core risk links. Based on the cross-link impact coefficient in the linkage monitoring curve, upstream and downstream links that are significantly related to the transmission of core risk links are screened to determine the links affected by risk transmission. Call the preset historical carbon emission optimization case library, and select historical valid cases that meet the preset standards based on multi-dimensional matching rules of risk link type, characteristic parameter over-limit mode and number of linked links; By combining current production conditions with optimization measures from historical cases, we determine the implementation steps, adjustment range of key parameters, and expected emission reduction effects of the optimization measures, and generate optimization recommendations.

7. The real-time carbon emission monitoring system for bamboo processing according to claim 5, characterized in that, The design includes a visualization and interactive module with multiple data display formats, and the specific linkage monitoring curves and related data content corresponding to each format include: The visualization and interactive module uses a combination of line charts, heatmaps, and data tables for display. Line charts are used to display the real-time emission values ​​and historical trend baselines of the linked monitoring curves, and support zooming and detailed viewing; Heatmaps are used to display cross-stage influence coefficients, and users can click to view the associated parameters of specific stages. Data tables are used to display raw data and support filtering by process and time.

8. The real-time carbon emission monitoring system for bamboo processing according to claim 1, characterized in that, The specific characteristics of the cross-link influence coefficient include: Directionality: Positive and negative values ​​distinguish the types of impact. Positive values ​​indicate that an increase in carbon emissions in a certain stage will lead to an increase in emissions in related stages in the same direction, while negative values ​​indicate a reverse impact. Intensity grading: The degree of influence is reflected by the absolute value. The larger the value, the more significant the transmission effect. The threshold for significant influence can be determined by combining the correlation with the production process. Dynamics: Based on real-time collection of multi-stage operation data, it is updated regularly. When process parameters are adjusted, raw materials are changed, or equipment status changes, it automatically recalculates to adapt to the new stage relationships. Correlation: It works in conjunction with real-time emission values ​​and historical trend baselines to explain the cross-stage transmission path of current emission fluctuations and to provide a basis for cross-stage correction of historical trend baselines.

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