Cloud analysis and scheduling method for cement telegraph pole carbon footprint calculation

Abstract

The invention relates to the technical field of environmental information and industrial carbon emission, in particular to a cloud analysis and scheduling method for cement telegraph pole carbon footprint calculation, which comprises the following steps: constructing a cloud unified data access layer, and collecting raw material, energy and production logistics full-link data in real time; on the basis of life cycle evaluation, eight process stages are divided, and a high-precision carbon footprint accounting model is established; performing multi-dimensional sensitivity analysis on the key process parameters through Monte Carlo simulation to generate a carbon emission scene probability map; outputting a low-carbon scheduling instruction according to the optimal scene, and driving an advanced plan scheduling system to execute emission reduction and production scheduling; and a long-short-term memory neural network is introduced to realize model self-learning. According to the invention, real-time monitoring, accounting and active optimization of carbon emission in the whole life cycle of the cement telegraph pole are realized.

Description

Technical Field

[0001] This invention relates to the fields of environmental information technology and industrial carbon emission technology, and in particular to a cloud-based analysis and scheduling method for calculating the carbon footprint of cement utility poles. Background Technology

[0002] Cement power poles, as prefabricated components widely used in power infrastructure, involve typical high-energy-consuming and high-emission processes in their production. Therefore, accurate accounting and dynamic optimization of their entire life-cycle carbon footprint are necessary through digital means. Currently, carbon footprint calculations largely rely on static bills of materials and empirical emission factors, which fail to reflect actual production conditions, resulting in time-sensitive accounting results.

[0003] Among them, carbon footprint analysis methods for the cement products industry are usually based on localized software or offline spreadsheet tools. Data collection is scattered and updates are lagging, making it impossible to link with cloud-based production management systems in real time and difficult to support high-concurrency carbon emission extrapolation and analysis. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud-based analysis and scheduling method for calculating the carbon footprint of cement utility poles, in order to solve the technical problems of crude carbon emission accounting and slow response in the existing cement utility pole manufacturing process.

[0005] This invention provides a cloud-based analysis and scheduling method for calculating the carbon footprint of cement utility poles, comprising at least: Establish a unified data access layer, deploy it on a cloud server, and connect it with the internal manufacturing, energy management, warehousing and logistics systems of cement pole manufacturing enterprises as well as external supplier platform systems to collect data on raw material origin, transportation distance, energy consumption type and carbon emission factors in real time. Based on the life cycle assessment theoretical framework, the entire production process of cement power poles is divided into eight stages: raw material mining, material transportation, concrete mixing, steel bar processing, mold assembly, steam curing, demolding and stacking, and finished product delivery. Each stage is set up with an independent carbon emission accounting unit. The stage carbon emission is calculated by multiplying the activity data with the dynamically updated emission factors, and a dynamic carbon footprint accounting model is constructed. Initiate parallel computing tasks in the cloud and set variation ranges for key variables affecting carbon footprint. The variation ranges should include at least the following: cement admixture ratio between 0.18 and 0.24, selection of three alternative transportation routes, and adjustment of steam curing temperature between 75°C and 95°C. Use Monte Carlo simulation to generate no less than 100,000 carbon emission scenario combinations and output carbon emission distribution probability maps and ranking of key influencing factors. Based on the optimal carbon emission scenario, the corresponding combination of process parameters and resource allocation scheme are extracted and transformed into specific production scheduling instructions. The actual carbon emission data after the execution of the scheduling command is continuously monitored and compared with the predicted value: if the actual emissions of three consecutive batches exceed the predicted range by more than ±8%, the model self-learning mechanism is triggered.

[0006] In some embodiments, the data access layer adopts a lightweight message queue telemetry transmission protocol to achieve low-latency communication across systems; before all raw data is written, it is processed by a consistency verification module to remove abnormal jump values ​​and duplicate records; for energy consumption data missing for no more than 2 hours, cubic spline interpolation is used to complete it.

[0007] In some embodiments, the carbon emission accounting process in the concrete mixing stage includes dry mixing and wet mixing. The dry mixing time is set to 90 seconds. A water-reducing agent is added during the wet mixing process, which can reduce the amount of cement used by 3% to 5%. The reduced amount of cement used is converted into the corresponding carbon dioxide emission reduction and included in the total accounting.

[0008] In some embodiments, CNC bending machines and automatic cutting equipment are introduced in the steel bar processing stage, which reduces the energy consumption per unit product by 42% compared with traditional manual operation. The equipment operating status is uploaded in real time through IoT sensors, and the electricity consumption data is accurate to the kilowatt-hour level and associated with the unique identification code of each utility pole, realizing individual-level traceability of carbon footprint.

[0009] In some embodiments, the triggering model self-learning mechanism includes: training the historical error sequence using a long short-term memory neural network, adjusting the estimation weights of subsequent emission factors and the response coefficients of process parameters, and ensuring that the model accuracy is maintained above 95%.

[0010] In some embodiments, the task assignment for the Monte Carlo simulation is managed by a cloud-based container orchestration engine.

[0011] In some embodiments, when the scheduling instruction is pushed to the advanced planning and scheduling system, a carbon emission impact assessment report is attached. The carbon emission impact assessment report includes: the expected reduction in carbon dioxide equivalent tons compared to the baseline scheme, the amount of energy cost savings, and the number of days of impact on the delivery cycle.

[0012] In some embodiments, the input layer of the long short-term memory neural network includes: daily predicted carbon emissions, actual carbon emissions, weather temperature and humidity data, production line operating rate and raw material batch change information for the past 30 days, the hidden layer is configured with 3 recursive unit blocks, the output layer generates the correction coefficient of the emission factor for the next cycle, and the network training cycle is 2:00 AM every day.

[0013] In some embodiments, the system further includes: establishing a digital labeling system for the carbon footprint of cement utility poles, generating a unique QR code electronic label for each cement utility pole.

[0014] In some embodiments, the system further includes: deploying a multi-tenant access control system on a cloud server, where users at different levels have different access permissions.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. By constructing a cloud-integrated dynamic analysis and scheduling feedback mechanism for carbon footprint, the invention achieves timely management of carbon emissions throughout the entire process of cement pole manufacturing. Compared with the traditional offline accounting method, this invention utilizes real-time data streams to drive dynamic model updates, significantly improving the accuracy and timeliness of carbon footprint calculation and solving the problem of decision failure caused by data lag. 2. This invention directly converts carbon footprint calculation results into executable production scheduling instructions and establishes a closed-loop feedback mechanism, enabling carbon emission reduction to shift from passive assessment to active control, truly achieving seamless integration of monitoring and execution throughout the entire process; 3. This invention not only reduces the carbon intensity per unit product, but also enhances the competitiveness of enterprises in the green supply chain, providing a replicable technology for deep decarbonization. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the cloud-based analysis and scheduling method of the present invention; Figure 2 This is a schematic diagram of the core principle of carbon footprint accounting in this invention. Detailed Implementation

[0018] The following will be based on embodiments of the present invention. Figures 1-2 The technical solutions in the embodiments of the present invention will be clearly and completely described together. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Partial interpretation 1. Spline interpolation: This is an interpolation method that uses piecewise low-order polynomial functions to smoothly connect data points. The most commonly used is cubic spline interpolation.

[0020] 2. Carbon Emission Distribution Probability Map: This is a professional quantitative analysis tool that focuses on the changing trends of carbon emissions. Its core is to divide scenarios according to the time dimension and emission reduction intensity, and mark the probability of occurrence for each potential carbon emission scenario, based on key objectives such as "two carbons".

[0021] 3. Sobol index method: It is the most classic and widely used variance decomposition method in global sensitivity analysis. Its core is to determine the importance of each input factor by quantifying the contribution of the input factors to the total variance of the output result. At the same time, it can identify the interaction between factors and is suitable for complex models that are nonlinear, non-monotonic, and have multiple coupled factors.

[0022] 4. Pareto Front Analysis: This is the core analytical method for multi-objective optimization problems. Its core is to find the set of all optimal trade-off solutions among multiple conflicting objectives, i.e., the Pareto front. Essentially, it solves the problem of how to choose the optimal trade-off solution when there is no single optimal solution.

[0023] Example 1 This embodiment provides a cloud-based analysis and scheduling method for calculating the carbon footprint of cement utility poles, including at least: establishing a unified data access layer deployed on a cloud server, connecting to the internal manufacturing, energy management, warehousing and logistics systems of cement utility pole manufacturers, as well as external supplier platform systems, and collecting data on raw material origin, transportation distance, energy consumption type, and carbon emission factors in real time; based on the life cycle assessment theoretical framework, dividing the entire production process of cement utility poles into eight stages: raw material mining, material transportation, concrete mixing, steel bar processing, mold assembly, steam curing, demolding and stacking, and finished product delivery, setting up an independent carbon emission accounting unit for each stage, and calculating the stage carbon emissions by multiplying active data with dynamically updated emission factors, thus constructing a dynamic carbon footprint accounting system. The model initiates parallel computing tasks in the cloud, setting variation ranges for key variables affecting carbon footprint. These ranges include at least: cement admixture ratio varying between 0.18 and 0.24, three alternative transportation routes, and steam curing temperature adjusted between 75°C and 95°C. Monte Carlo simulations are used to generate at least 100,000 carbon emission scenario combinations, outputting a carbon emission distribution probability map and a ranking of key influencing factors. Based on the optimal carbon emission scenario, corresponding process parameter combinations and resource allocation schemes are extracted and transformed into specific production scheduling instructions. Actual carbon emission data after the execution of scheduling instructions is continuously monitored, and the deviation is compared with the predicted value. If the actual emissions of three consecutive batches exceed the predicted range by more than ±8%, the model's self-learning mechanism is triggered.

[0024] To better understand this invention, the details are as follows: first, Establish a unified data access layer, deployed on a cloud server, to connect with the internal manufacturing, energy management, warehousing and logistics systems of cement pole manufacturers, as well as external supplier platform systems, to collect data on raw material origin, transportation distance, energy consumption type, and carbon emission factors in real time.

[0025] The data access layer employs a lightweight message queue telemetry transmission protocol to achieve low-latency communication across systems. This protocol is based on a publish-subscribe model, ensuring that data streams from different heterogeneous systems can converge to the central node in the cloud with millisecond-level latency. Before being written to the distributed time-series database, all raw data undergoes consistency verification by a module that incorporates a sliding window algorithm. This module performs slope analysis on energy consumption readings at five consecutive time points. If the amplitude of a single point jump exceeds three times the standard deviation of the historical average, it is determined to be an abnormal jump value and is removed. For duplicate records, deduplication is performed by comparing the timestamp with the data fingerprint hash value.

[0026] For energy consumption data missing for no more than 2 hours, cubic spline interpolation is used to complete the data. This method uses 3 valid data points before and after the data point to construct a piecewise cubic polynomial, ensuring the continuity of the first and second derivatives of the curve, thereby maintaining the physical rationality of the data trend and ultimately ensuring that the data integrity rate is higher than 99.5%.

[0027] The raw material information collected by the data access layer includes the clinker coefficient of cement, the particle size distribution of sand and gravel, the yield strength grade of steel bars, and the corresponding carbon declaration document number of their upstream suppliers. Transportation data includes vehicle type, tonnage, mileage, and traffic congestion index of the routes traversed. Energy data is accurate to the meter readings of each device, sampled every 10 seconds, and simultaneously records voltage, current, and power factor.

[0028] It should be noted that all the aforementioned data are uniquely timestamped in the ISO 8601 standard format and written to a distributed time-series database built on Apache IoTDB in a columnar storage structure.

[0029] then, Based on the life cycle assessment theoretical framework, the entire production process of cement utility poles is divided into eight stages: raw material mining, material transportation, concrete mixing, steel bar processing, mold assembly, steam curing, demolding and stacking, and finished product delivery. Each stage is set up with an independent carbon emission accounting unit. The stage carbon emission is calculated by multiplying the activity data with the dynamically updated emission factors, and a dynamic carbon footprint accounting model is constructed.

[0030] The dynamic carbon footprint accounting model is organized in a modular way, with each accounting unit encapsulating independent calculation logic and parameter configuration.

[0031] The activity data during the raw material mining stage comes from the ore mining energy consumption report pushed by the supplier platform, and the emission factor is a weighted average of the IPCC default value and the value approved by the local environmental protection department.

[0032] During the material transportation phase, the Ministry of Transportation's freight carbon emission coefficient database is used to determine the transportation distance, vehicle type, and load, and real-time road conditions are used to adjust the idling fuel consumption.

[0033] Carbon emission accounting in the concrete mixing stage is divided into two sub-processes: dry mixing and wet mixing. The dry mixing time is set to 90 seconds, during which the energy consumption of the dust collection system is included in the carbon emission. This system is driven by a variable frequency fan, and its power consumption is measured by a dedicated electricity meter and linked to the batch mixing task. During the wet mixing process, the addition of water-reducing agent can reduce the amount of cement by 3% to 5%. The reduction in cement usage can be converted into the corresponding carbon dioxide emission reduction and included in the overall accounting. The reduction effect of water-reducing agent is pre-calibrated through laboratory mix proportion tests and embedded into the model in the form of a function.

[0034] The introduction of CNC bending machines and automatic cutting equipment in the steel bar processing stage reduces the energy consumption per unit product by 42% compared to traditional manual operation. The equipment operation status is uploaded in real time through IoT sensors, and the electricity consumption data is accurate to the kilowatt-hour level and linked to the unique identification code of each utility pole, realizing individual-level traceability of carbon footprint.

[0035] The energy consumption of hoisting equipment and auxiliary tooling is mainly calculated during the mold assembly stage.

[0036] During the steam curing stage, the boiler natural gas consumption and circulating water pump power consumption are calculated. The calorific value of natural gas is calculated based on the measured lower heating value, and the emission factor adopts the recommended value of the latest version of the "Provincial Greenhouse Gas Inventory Guide".

[0037] Energy consumption of forklift operations and fuel consumption of logistics vehicles were calculated separately during the demolding and stacking and finished product delivery stages.

[0038] The emission factors for all the aforementioned stages are retrieved hourly from the State Grid carbon management platform via API, using the latest regional grid baseline emission factors. These factors are then weighted against the actual green electricity usage ratio returned by the enterprise's green electricity trading platform, as shown in the following formula: in, EF 修正 The corrected comprehensive emission factor; EF 电网 As the baseline emission factor for the regional power grid; P 绿电 The actual green electricity usage ratio of the enterprise, with a value ranging from 0 to 1; EF 绿电 The green electricity emission factor is set at 0.02 kg CO2 equivalent per kilowatt-hour, representing the implicit carbon emissions of photovoltaic and wind power throughout their entire life cycle. This weighting mechanism ensures the model's real-time response to changes in the energy structure.

[0039] Immediately afterwards, Initiate parallel computing tasks in the cloud, set variation ranges for key variables affecting carbon footprint, including at least: cement admixture ratio between 0.18 and 0.24, selection of three alternative transportation routes, and adjustment of steam curing temperature between 75°C and 95°C. Use Monte Carlo simulation to generate no less than 100,000 carbon emission scenario combinations, and output carbon emission distribution probability maps and ranking of key influencing factors.

[0040] First, 12 key variables contributing more than 5% to the total carbon footprint were identified. From these, three highly controllable core variables were selected as the objects of change. In this embodiment, the core variables are cement admixture ratio, transportation route, and steam curing temperature. The range of cement admixture ratio was determined based on the C30 to C50 mix design specifications for concrete strength. Three alternative transportation routes were pre-generated by a logistics optimization algorithm, corresponding to the shortest distance, lowest toll, and fewest traffic light options, respectively. The adjustment range of steam curing temperature was limited by the cement hydration reaction kinetic window and the equipment safety limit.

[0041] The task allocation for the Monte Carlo simulation is managed by a cloud-based container orchestration engine built on Kubernetes. Each compute container runs a set of parameter perturbation experiments independently, and the container image comes pre-loaded with a complete copy of the carbon footprint accounting model, ensuring isolation of the computing environment.

[0042] The number of container instances can be dynamically expanded to a maximum of 128 based on the current cluster load. When the CPU utilization exceeds 70%, the capacity will automatically expand, and when it is below 30%, the capacity will shrink. The time taken for a single complete simulation is controlled within 180 seconds, and at least 6 rounds of high-frequency simulations per day are supported.

[0043] The carbon emission results for each scenario combination are recorded in a high-performance columnar database, and a carbon emission distribution probability map is generated through kernel density estimation. Simultaneously, the Sobol index method is used to calculate the contribution of each input variable to the output variance, outputting a ranked list of key influencing factors. This process not only quantifies the marginal effect of individual variables but also reveals the interactions between variables.

[0044] Then, Based on the optimal carbon emission scenario, the corresponding combination of process parameters and resource allocation schemes are extracted and transformed into specific production scheduling instructions.

[0045] The low-carbon scheduling suggestion generator incorporates a rule engine and optimization solver. First, it filters the scenario set from Monte Carlo simulation results, identifying scenarios with carbon emissions in the bottom 5%. Then, combining cost constraints and delivery deadlines, it performs Pareto front analysis to determine a feasible solution that balances low carbon emissions with economic viability. It should be noted that the scheduling suggestions include at least a priority labeling system. The first-level recommendation is a mandatory item, involving compliance measures, such as using low-clinker cement that meets national standards; The second-level recommendation is an economic incentive, corresponding to an emission reduction path with a unit carbon cost of less than 35 yuan / ton; Level 3 recommendations are for long-term optimization and are applicable to process upgrade plans with a technical transformation cycle of more than 6 months.

[0046] When scheduling instructions are pushed to the advanced planning and scheduling system, a carbon emission impact assessment report is attached. The report should include at least the expected reduction in carbon dioxide equivalent tons compared to the baseline plan, the amount of energy cost savings, and the number of days of impact on the delivery cycle, for management to make comprehensive decision-making reference.

[0047] For example, when cement from Mine A is recommended, the system automatically calculates the reduction in diesel consumption resulting from shortening the transportation distance from 120 kilometers to 65 kilometers, and converts it into an emission reduction benefit of 0.82 tons of carbon dioxide equivalent. Adjusting the steam curing heating rate to match the off-peak electricity hours at night, from 23:00 to 7:00 the next day, can reduce the electricity cost per unit product by 18%, and at the same time, due to the higher cleanliness of the power grid during the off-peak electricity price period, an additional 0.35 tons of carbon emission reduction is obtained.

[0048] at last, The actual carbon emission data after the execution of the scheduling command is continuously monitored and compared with the predicted value: if the actual emissions of three consecutive batches exceed the predicted range by more than ±8%, the model self-learning mechanism is triggered.

[0049] When the absolute value of the relative deviation between the actual and predicted carbon emissions for three consecutive batches is greater than 8%, the system initiates a self-learning process. The input layer of the Long Short-Term Memory (LSTM) neural network includes daily predicted carbon emissions, actual carbon emissions, weather temperature and humidity data, production line operating rates, and raw material batch variation information for the past 30 days. These features are normalized before being input into the network. The hidden layer is configured with three recursive unit blocks, each containing 128 memory cells, using the tanh activation function. The output layer generates the correction coefficient for the emission factor in the next cycle, which directly affects the calculation of the dynamic emission factor. The network training cycle is 2:00 AM daily, when the system load is at its lowest. The training dataset contains historical data from the past 90 days. Backpropagation is performed using the Adam optimizer, with an initial learning rate of 0.001 and 50 training epochs.

[0050] After training is completed, the new model parameters gradually replace the old version through a canary release mechanism to ensure service continuity. Understandably, this mechanism enables the system to continuously adapt to changes in the production environment. For example, when the introduction of a new water-reducing agent causes a change in the fluctuation pattern of cement usage, the model can automatically calibrate its response relationship within a week.

[0051] In some optional implementations, a digital labeling system for the carbon footprint of cement power poles is also included, in which a unique QR code electronic label is generated for each product when it leaves the factory. The label content includes the total carbon emissions of the pole, the proportion of major contributing factors, the proportion of green building materials used, and the carbon offset certification number, which supports downstream power grid construction units to scan the code to verify and record it in their overall project carbon ledger.

[0052] The label is dynamically created by a cloud-based label generation service. The final output of the data source autonomously calculates the model and is encrypted using the SM4 algorithm before being encoded into a QR code to ensure that the information cannot be tampered with.

[0053] In some alternative implementations, the present invention deploys a multi-tenant access control system in the cloud, where users at different levels have differentiated access permissions: Factory operators can only view data from their own workshop. Regional managers can compare carbon efficiency indicators across different plants; The group headquarters can access aggregated reports from across the network and supports the generation of third-party audit-ready reports that comply with international greenhouse gas accounting standards on a monthly, quarterly, and annual basis.

[0054] Example 2 This invention is applied to a large precast component group with an annual output of over 500,000 cement utility poles, covering seven production bases in North China, East China, and South China. After its implementation, the invention achieved 100% coverage of carbon data collection across the entire supply chain, reduced the average response time for calculating carbon footprint per batch from 72 hours to less than 15 minutes, and decreased the annual comprehensive carbon intensity by 12.7 percentage points year-on-year.

[0055] Example 3 The North China base successfully won a green infrastructure project by demonstrating to grid customers that its product carbon intensity was 15% lower than the industry average using the method of this invention, demonstrating the direct value of this invention in enhancing market competitiveness.

[0056] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0057] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A cloud-based analysis and scheduling method for calculating the carbon footprint of cement utility poles, characterized in that, At least including: Establish a unified data access layer, deploy it on a cloud server, and connect it with the internal manufacturing, energy management, warehousing and logistics systems of cement pole manufacturing enterprises as well as external supplier platform systems to collect data on raw material origin, transportation distance, energy consumption type and carbon emission factors in real time. Based on the life cycle assessment theoretical framework, the entire production process of cement power poles is divided into eight stages: raw material mining, material transportation, concrete mixing, steel bar processing, mold assembly, steam curing, demolding and stacking, and finished product delivery. Each stage is set up with an independent carbon emission accounting unit. The stage carbon emission is calculated by multiplying the activity data with the dynamically updated emission factors, and a dynamic carbon footprint accounting model is constructed. Initiate parallel computing tasks in the cloud and set variation ranges for key variables affecting carbon footprint. The variation ranges should include at least the following: cement admixture ratio between 0.18 and 0.24, selection of three alternative transportation routes, and adjustment of steam curing temperature between 75°C and 95°C. Use Monte Carlo simulation to generate no less than 100,000 carbon emission scenario combinations and output carbon emission distribution probability maps and ranking of key influencing factors. Based on the optimal carbon emission scenario, the corresponding combination of process parameters and resource allocation scheme are extracted and transformed into specific production scheduling instructions. The actual carbon emission data after the execution of the scheduling command is continuously monitored and compared with the predicted value: if the actual emissions of three consecutive batches exceed the predicted range by more than ±8%, the model self-learning mechanism is triggered.

2. The method according to claim 1, characterized in that, The data access layer adopts a lightweight message queue telemetry transmission protocol to achieve low-latency communication across systems. Before being written, all raw data is processed by a consistency verification module to remove abnormal jump values ​​and duplicate records. For energy consumption data missing for no more than 2 hours, cubic spline interpolation is used to complete it.

3. The method according to claim 1, characterized in that, The carbon emission accounting process for the concrete mixing stage includes dry mixing and wet mixing. The dry mixing time is set to 90 seconds. During the wet mixing process, a water-reducing agent is added, which can reduce the amount of cement used by 3% to 5%. The reduced amount of cement used is converted into the corresponding carbon dioxide emission reduction and included in the overall accounting.

4. The method according to claim 1, characterized in that, The steel bar processing stage introduces CNC bending machines and automatic cutting equipment, which reduces the energy consumption per unit product by 42% compared to traditional manual operation. The equipment operating status is uploaded in real time through IoT sensors, and the electricity consumption data is accurate to the kilowatt-hour level and linked to the unique identification code of each utility pole, realizing individual-level traceability of carbon footprint.

5. The method according to claim 1, characterized in that, The triggering model self-learning mechanism includes: using a long short-term memory neural network to train historical error sequences, adjusting the estimation weights of subsequent emission factors and the response coefficients of process parameters, and ensuring that the model accuracy is maintained above 95%.

6. The method according to claim 1, characterized in that, The task allocation for the Monte Carlo simulation is managed by a cloud-based container orchestration engine.

7. The method according to claim 1, characterized in that, When the scheduling instruction is pushed to the advanced planning and scheduling system, a carbon emission impact assessment report is attached. The carbon emission impact assessment report includes: the expected reduction in carbon dioxide equivalent tons compared to the baseline scheme, the amount of energy cost saved, and the number of days of impact on the delivery cycle.

8. The method according to claim 5, characterized in that, The input layer of the long short-term memory neural network includes: daily predicted carbon emissions, actual carbon emissions, weather temperature and humidity data, production line operating rate and raw material batch change information for the past 30 days. The hidden layer is configured with 3 recursive unit blocks. The output layer generates the correction coefficient of the emission factor for the next cycle. The network training cycle is 2:00 AM every day.

9. The method according to claim 1, characterized in that, Also includes: Establish a digital labeling system for the carbon footprint of cement utility poles, generating a unique QR code electronic label for each cement utility pole.

10. The method according to claim 1, characterized in that, Also includes: A multi-tenant access control system is deployed on a cloud server, where users at different levels have different access permissions.

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