Carbon emission reduction calculation and intelligent management method and system for coal-based solid waste industrial park

Abstract

This invention discloses a method and system for calculating and intelligently managing carbon emission reduction in coal-based solid waste industrial parks, belonging to the field of industrial solid waste resource utilization and intelligent carbon management. The calculation method targets the product, detects key indicators of coal-based solid waste, determines the baseline blending ratio through mixing ratio experiments, calculates the process adjustment coefficient using a gradient improvement regression model to obtain the actual blending ratio, and calculates the solid waste blending quality in conjunction with product quality. It also calculates the carbon emissions from solid waste transportation to the entire production chain to determine its emission factors, obtains the emission factors of traditional raw materials according to priority, and calculates emission reduction using the carbon emission factor method. A supporting system is implemented to streamline the calculation process. Intelligent management, based on this method, collects measured and theoretical carbon emission data from all stages, performs three-dimensional reliability assessment, uses a Sigmoid function for dynamic data fusion and graded alarms, and visualizes the data through a two-way linkage between a three-dimensional model and a Sankey diagram. It identifies high-emission stages, formulates quantitative optimization plans, and verifies, consolidates, and tracks these data to form a complete closed loop for carbon emission reduction, achieving systematic carbon accounting at the park level and constructing a full-chain carbon management system.

Description

Technical Field

[0001] This invention relates to the field of industrial solid waste resource utilization and intelligent carbon management technology, and in particular to a carbon emission reduction calculation and intelligent management method and system for a coal-based solid waste resource utilization industrial park. Background Technology

[0002] Traditional methods of storing coal-based solid waste not only occupy large amounts of land resources but also easily cause environmental problems such as soil pollution, groundwater pollution, dust pollution, and geological disasters, becoming a prominent bottleneck restricting the green transformation of the coal industry and regional ecological and environmental security. Therefore, promoting the resource utilization of coal-based solid waste is an inevitable choice for promoting the development of a circular economy.

[0003] Currently, coal gangue and fly ash are widely used in the production of conventional building materials such as eco-soil, mortar, permeable bricks, blocks, and cement admixtures. Furthermore, their use in the preparation of high-value products such as expanded clay, lightweight aggregates, geopolymers, soil conditioners, and road base materials is being explored. However, existing carbon emission reduction accounting methods are mostly limited to single products, lacking methods for carbon accounting across multiple products in the entire industrial park chain. Moreover, industrial parks lack dynamic monitoring and intelligent management tools based on real-time data, relying heavily on manual reporting or periodic testing. This results in poor data timeliness and coarse granularity, making it impossible to achieve accurate carbon emission measurement and real-time tracking.

[0004] The aforementioned problems result in a significant discrepancy between carbon accounting results and actual emissions, making it difficult to provide reliable data support for carbon emission reduction decisions in the industrial park, and also failing to meet the increasingly stringent requirements for carbon data quality from the carbon trading market, green finance, and environmental regulation. Summary of the Invention

[0005] This invention proposes a method and system for calculating and intelligently managing carbon emission reduction in coal-based solid waste industrial parks, in order to overcome the shortcomings of the existing technologies and realize full-chain carbon management within the industrial park, from accounting to monitoring to display and optimization.

[0006] In a first aspect, the present invention proposes a method for calculating carbon emission reduction in coal-based solid waste industrial parks, characterized by comprising the following steps: The target product is prepared by replacing some traditional raw materials with coal-based solid waste. The target product includes one or more of the following: ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, and coal-based plastic masterbatch new materials. The benchmark blending ratio of coal-based solid waste is determined through mix proportion tests. Based on the raw material composition test results and the core performance indicators of the target product, the process adjustment coefficient is calculated through a pre-trained gradient boosting regression model to determine the blending ratio of the coal-based solid waste. The blending quality of coal-based solid waste is calculated by combining the quality of the target product with the blending ratio. The carbon emission factors of the replaced traditional raw materials are obtained, and the carbon emission factors of coal-based solid waste are calculated. Based on the difference between the two types of carbon emission factors and the blending quality of the coal-based solid waste, the carbon emission reduction is calculated using the carbon emission factor method.

[0007] In at least one embodiment of the present invention, before determining the blending ratio, the raw material composition of the coal-based solid waste is tested, including particle size distribution, density, bulk density, moisture content and pozzolanic activity index. The raw material composition test results are used as the input basis for calculating the process adjustment coefficient. The benchmark blending ratio is set according to the type of the target product. Different types of target products are adapted to different types of coal-based solid waste and benchmark blending ratios.

[0008] In at least one embodiment of the present invention, the method for calculating the carbon emission factor of coal-based solid waste is as follows: the accounting boundary is determined as the entire resource utilization chain of coal-based solid waste from its place of origin to the industrial park, to the end of the processing and production of the target product. The carbon emissions of the coal-based solid waste generation process are not included in this accounting boundary; the carbon emissions of coal-based solid waste in each of the transportation, storage, pretreatment and production stages are calculated separately, and the total carbon emissions of the entire chain are summed; the total carbon emissions are compared with the blending mass of coal-based solid waste to obtain the carbon emission factor of coal-based solid waste.

[0009] In at least one embodiment of the present invention, if the raw material being replaced is a mixture of materials, its comprehensive carbon emission factor is calculated by weighting the mass percentage of each material.

[0010] Secondly, the present invention provides a carbon emission reduction calculation system for coal-based solid waste industrial parks, comprising: Raw material composition detection unit: Detects key indicators of coal-based solid waste such as particle size distribution, density, bulk density, moisture content, and pozzolanic activity index, providing data basis for adjusting the blending ratio; Blending ratio determination unit: The benchmark blending ratio is determined through mix proportion test, and the process adjustment coefficient is calculated by combining the gradient improvement regression model to obtain the actual blending ratio; Blending quality calculation unit: Calculates the blending quality of coal-based solid waste based on the production quality of the target product and the actual blending ratio; Carbon emission factor acquisition unit: Acquires carbon emission factors of the replaced traditional raw materials according to preset priority, and calculates carbon emission factors of coal-based solid waste at the same time; Carbon emission reduction calculation unit: Calculates carbon emission reduction based on the blending quality of coal-based solid waste and the difference between the two types of carbon emission factors.

[0011] This invention also proposes a smart management method for carbon emission reduction in coal-based solid waste industrial parks, comprising the following steps: Dynamic parameter acquisition: In the four stages of transportation, stacking, pretreatment and production, operating condition data, energy consumption data and material flow data are collected in real time based on a cluster of multiple types of sensors to obtain the actual blending ratio fluctuation and actual carbon emission intensity of coal-based solid waste. Dual-channel carbon emission calculation: The measured carbon emission value is calculated based on the real-time acquired data, and the theoretical carbon emission value is calculated based on the raw material composition detection results, process parameters and emission factors of coal-based solid waste. Credibility assessment and dynamic fusion: A quantitative evaluation system is established from three dimensions: sensor accuracy, data integrity, and operating condition stability. The comprehensive credibility score of the measured carbon emission values ​​is calculated. Based on the standard deviation ratio of the theoretical carbon emission value and the measured value, and combined with the credibility correction function based on the Sigmoid function, the weight coefficient of the measured value is calculated. The theoretical value and the measured value are fused according to the weight coefficient to obtain the dynamic carbon emission factor of the four links of transportation, storage, pretreatment and production. Parameter feedback update and closed-loop management: The dynamic carbon emission factor is fed back to the carbon emission factor acquisition stage to update the coal-based solid waste carbon emission factor used for carbon emission reduction calculation; Based on the actual blending ratio fluctuation and the updated raw material composition detection results, the process adjustment coefficient is recalculated and the blending ratio is updated to iteratively optimize the carbon emission reduction calculation results and realize dynamic accounting and closed-loop management. 3D visualization linkage display: Based on BIM or 3D scanning technology, a full-process 3D model of the industrial park is constructed, a carbon emission Sankey diagram is designed, and the dynamic carbon emission factors and the updated carbon emission reduction calculation results are integrated into the 3D model and Sankey diagram to achieve bidirectional linkage visualization display. It supports triggering the parameter feedback update step based on the visualization analysis results.

[0012] In at least one embodiment of the present invention, the deployment scheme of the multi-type sensor cluster is as follows: Transportation process: Vehicle-mounted GPS and Beidou dual-mode positioning devices are used to collect transportation mileage data, and vehicle-mounted weighing sensors are used to collect load data; During the stacking process: LiDAR and cameras are used to collect data on the volume and height of the stockpile. Preprocessing stage: Energy consumption data is collected using smart meters and gas meters; Production process: Energy consumption and material input / output data are collected using a cluster of sensors on the production line.

[0013] In at least one embodiment of the present invention, the confidence correction function β(S) based on the Sigmoid function is determined in the following manner: Where S is the overall credibility score, parameter a controls the steepness of the curve, and parameter b is the switching midpoint; the parameters a and b are determined by least squares fitting based on the correlation analysis between the credibility score of the pilot park and the actual data quality.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention overcomes the limitations of single-product accounting by establishing a systematic carbon emission reduction accounting method at the park level that integrates multiple products, including ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, and coal-based plastic masterbatch. By introducing a real-time data acquisition mechanism based on a multi-type sensor cluster, a three-dimensional credibility assessment system is constructed, and the Sigmoid function is used to dynamically fuse theoretical and measured values, significantly improving the accuracy and timeliness of carbon accounting and overcoming the shortcomings of static carbon emission factor methods in reflecting production fluctuations. Furthermore, by establishing a closed-loop management system encompassing "accounting-monitoring-display," a two-way interactive visualization of the 3D model and Sankey diagram is achieved, forming a verifiable and traceable intelligent management loop. This effectively improves the carbon management level of the industrial park and meets the stringent requirements of the carbon trading market, green finance, and environmental regulation for carbon data quality. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the calculation of carbon emission reduction from the resource utilization of coal-based solid waste in this invention. Figure 2 This is a diagram of the carbon emission reduction calculation system for the comprehensive utilization of coal-based solid waste of the present invention. Figure 3 This is a diagram illustrating the dynamic data fusion and credibility assessment mechanism of this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Currently, coal gangue and fly ash are widely used in the production of conventional building materials such as eco-soil, mortar, permeable bricks, blocks, and cement admixtures; and their use in the preparation of high-value products such as ceramsite, lightweight aggregates, geopolymers, soil conditioners, and road base materials has begun to be explored. However, existing methods for calculating carbon emission reductions still have significant limitations:

[0018] First, the accounting boundaries are mostly limited to a single product or a single production link, lacking a systematic carbon accounting methodology for the entire chain and multi-product collaboration of industrial parks; Second, the determination of carbon emission factors mainly relies on industry experience values ​​or static assumptions, failing to fully consider dynamic factors such as fluctuations in raw material composition, differences in production processes, changes in energy structure, and equipment operating status, making it difficult to reflect the characteristics of carbon emission fluctuations in the actual production process. Third, there is a lack of dynamic monitoring and intelligent management methods based on real-time data. Existing systems mostly rely on manual reporting or periodic testing, resulting in poor data timeliness and coarse granularity, which makes it impossible to achieve accurate measurement, real-time tracking and intelligent early warning of carbon emissions.

[0019] The aforementioned problems result in a significant discrepancy between carbon accounting results and actual emissions, making it difficult to provide reliable data support for carbon emission reduction decisions in the industrial park, and also failing to meet the increasingly stringent requirements for carbon data quality from the carbon trading market, green finance, and environmental regulation.

[0020] This invention focuses on the accurate calculation of carbon emission reductions from replacing traditional raw materials with coal-based solid waste. It establishes a systematic calculation method at the park level covering multiple product types. Through a gradient-enhancing regression model, it dynamically optimizes blending ratios, achieving a methodological breakthrough from single-product to full-chain calculations and from static assumptions to dynamic calculations. Data-driven, it relies on a multi-type sensor cluster to achieve real-time data acquisition across all stages. Model-driven, it constructs a reliability assessment and Sigmoid dynamic fusion mechanism, outputting high-precision carbon emission data through dual-channel collaborative output. The invention establishes a closed-loop system encompassing accounting (dynamic fusion of theoretical and measured data), management (monitoring, analysis, optimization, verification, and target management), and visualization (two-way linkage between 3D model and Sankey diagram), forming an integrated intelligent carbon management system that combines data collection, calculation, display, and control. This achieves a strategic upgrade in carbon emission reduction from passive accounting to proactive optimization.

[0021] Combination Figure 1 As shown, a method for calculating carbon emission reduction in a coal-based solid waste industrial park is described, with the following specific steps: Raw material composition detection and blending ratio determination: Key indicators such as particle size distribution, density, bulk density, moisture content, and pozzolanic activity index of coal-based solid waste are detected. Coal-based solid waste is used to replace part of the traditional raw materials for the production of target products (ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, and coal-based plastic masterbatch new materials). The benchmark blending ratio w0 of coal-based solid waste is determined through mix proportion tests. Based on the raw material composition detection results (denoted as vector X) and the core performance indicators of the target product (denoted as vector Y), the process adjustment coefficient k = g(X, Y) is calculated. The process adjustment coefficient k is calculated using a pre-trained gradient boosting regression model (GBR), k = g(X, Y), where the function g ensures that the value of k is within a reasonable range of 0.7 to 1.0, and prioritizes ensuring that the product performance meets the standards. The final blending ratio w = w0 × k;

[0022] Calculate the mass of coal-based solid waste blending: M 煤基固废 =Target product quality M 产品 ×w.

[0023] Obtain / Calculate Carbon Emission Factor: Obtain the carbon emission factor F of the replaced traditional raw materials. 传统原材料 (Data source priority: primary source is the latest IPCC report; secondary source is authoritative industry databases (such as the China Carbon Accounting Database CEADs); tertiary source is industry standards (such as GB / T 51366-2019 "Standard for Calculation of Carbon Emissions from Buildings"), to calculate the carbon emission factor F of coal-based solid waste. 煤基固废 ; (4) The carbon emission reduction of comprehensive utilization of coal-based solid waste was calculated using the carbon emission factor method, C a =M 煤基固废 ×(F 传统原材料 -F 煤基固废 ).

[0024] As an alternative embodiment, the baseline blending ratio w0 of coal-based solid waste is determined based on the target product type: For ecological soil, the coal-based solid waste used is coal gangue, with a benchmark blending ratio w. 0-生态土 The percentage of coal gangue mass in the ecological soil mass, with a mix ratio of coal gangue:auxiliary material = 9:1, w 0-生态土 =90%; For mortar materials, coal-based solid waste is a mixture of coal gangue and fly ash, with a benchmark blending ratio w 0-砂浆材料 The percentage of the total mass of coal gangue and fly ash in the mortar material, with a mix ratio of coal gangue:fly ash:other materials = 30:1:5. 0-砂浆材料 ≈86.11%; For 3D printing raw materials, coal-based solid waste is a mixture of coal gangue and fly ash, with a benchmark blending ratio w. 0-3D打印 The percentage of the total mass of coal gangue and fly ash in the total mass of 3D printing raw materials, with a ratio of coal gangue:fly ash:other materials = 1:2:7, w 0-3D打印 =30%; For roadbed materials, coal-based solid waste consists of coal gangue, fly ash, and gasification slag, with a benchmark blending ratio w. 0-路基材料 This refers to the percentage of the total mass of coal gangue, fly ash, and gasification slag to the total mass of the roadbed materials, with a mix ratio of coal gangue:fly ash:gasification slag:cement:coagulant = 50:15:25:7:3. 0-路基材料 =90%; For underground filling and mine backfill materials, coal-based solid waste consists of coal gangue, fly ash, and gasification slag, with a benchmark blending ratio w. 0-井下填充及矿坑回填材料This refers to the percentage of the total mass of coal gangue, fly ash, and gasification slag to the mass of underground filling and mine backfill materials, with a mix ratio of coal gangue: fly ash: gasification slag: cement: special material = 50:20:20:7:3. 0-井下填充及矿坑回填材料 =90%; For new coal-based plastic masterbatch materials, the coal-based solid waste consists of coal gangue and fly ash, with a benchmark blending ratio w. 0-塑料母粒 The percentage of the total mass of coal gangue and fly ash in the mass of the new coal-based plastic masterbatch material is given by the following ratio: coal gangue: fly ash: polyethylene plastic: additives = 18:3:7.5:1.5. 0-塑料母粒 =70%.

[0025] As an alternative example, the carbon emission factor F of coal-based solid waste 煤基固废 The calculation method is as follows: Determine the calculation boundary: Starting from the coal-based solid waste generation site, transport to the industrial park → stockpiling in the industrial park → pretreatment → completion of target product processing and production; the carbon emissions of the coal-based solid waste generation process have been included in the accounting scope of coal mining enterprises. This patent only calculates the carbon emissions of the entire solid waste resource utilization chain (from transportation to the end of production), which corresponds to the carbon emissions of the entire life cycle of the replaced traditional raw materials (transportation → production). Calculate total carbon emissions C 总 =C 运输 +C 堆放 +C 预处理 +C 生产 ,in: C 运输 The carbon emissions from transporting coal-based solid waste from the storage yard to the industrial park; if the coal-based solid waste comes from multiple sources, the transport distance is calculated using a weighted average: L 加权 =Σ(M i ×L i ) / ΣM i M i Let L be the mass of coal-based solid waste from the i-th source. i C represents the transportation distance from the i-th source to the industrial park. 运输 =Material of coal-based solid waste M 煤基固废 ×Transportation distance L × Carbon emission factor F per unit mass of transportation distance 运输 ; C 堆放 The potential carbon sequestration loss caused by land occupation due to coal-based solid waste stockpiling; C 堆放 =A 占用 ×Forestry carbon sink factor F 林 The land area occupied is A 占用 =M 固 / (ρ 堆 ×h), ρ 堆Where is the bulk density of coal-based solid waste, and h is the weighted average height of the stockpiling area. The calculation formula is h=Σ(V i ×h i ) / ΣV i V i Let h be the volume of the i-th stacking partition. i This represents the average height of the corresponding partition; C 预处理 Carbon emissions from coal-based solid waste pretreatment processes; C 预处理 =Material of coal-based solid waste M 煤基固废 ×Construction machinery efficiency index f 预处理 × Carbon emission factor F of energy 能 , where f 预处理 Determined based on equipment service life and operating load, unit is energy consumption / ton of coal-based solid waste; F when multiple energy sources coexist 能 =Σ(m i ×F i ) / Σm i m i Let F be the consumption of the i-th type of energy. i The carbon emission factor for the corresponding energy source; C 生产 C represents the carbon emissions from the processing and production of the target product. 生产 =M 产品 ×Construction machinery efficiency index f 生产 × Carbon emission factor F of energy 能 f 生产 Determined based on the service life and operating load of equipment in a specific section of the target product production line; F when multiple energy sources coexist. 能 =Σ(m i ×F i ) / Σm i m i Let represent the consumption of the i-th type of energy, and Fi represent the carbon emission factor of the corresponding energy. Carbon emission factor F of coal-based solid waste 煤基固废 =C 总 / M 煤基固废 .

[0026] Furthermore, the acquisition of forestry carbon sink factors involves the following steps: ① Carbon content estimation: Refer to the "Guidelines for the Approval and Verification of Forestry Carbon Sequestration Projects" (GB / T 41198-2021), local forestry carbon sequestration measurement and monitoring technical regulations, or relevant literature on forest carbon content in the corresponding region to determine the carbon content in tree biomass; ② Carbon sink factor calculation: Combine carbon content with forest area to calculate the carbon storage per unit area (usually per hectare) of forest, forming the forestry carbon sink factor.

[0027] The construction machinery efficiency index f represents the energy consumption per unit mass of coal-based solid waste or target product during pretreatment or production. f = energy consumption per unit mass of coal-based solid waste or target product × energy consumption adjustment coefficient e. The energy consumption adjustment coefficient e ranges from 1.1 to 1.69. The value of the energy consumption adjustment coefficient e is based on: e = e 年限 ×e 负荷 , where e 年限 The equipment service life coefficient is used (1.1 for ≤3 years, 1.2 for 3~8 years, and 1.3 for >8 years), e 负荷 The load factor is 1.1 for full load and 1.3 for half load; to avoid excessive deviation of energy consumption estimation from reality under extreme working conditions and to allow dynamic correction through real-time monitoring data; the actual measurement method of f is to calculate the energy consumption per unit mass of coal-based solid waste or target product treatment through the equipment energy consumption ledger.

[0028] If the replaced traditional raw material is a mixture, F is calculated based on the weighted average of the replaced material's mass percentage. 传统原材料 That is, F 传统原材料 =Σ(w i ×F i ), where w i F represents the mass percentage of the i-th traditional raw material (consistent with the proportion of coal-based solid waste substitutes in the target product). i Let be the carbon emission factor of the i-th traditional raw material; the specific types of traditional raw materials being replaced are shown in the table below: In a second aspect, the present invention provides a carbon emission reduction calculation system for coal-based solid waste industrial parks, comprising: Raw material composition testing unit: Tests key indicators of coal-based solid waste such as particle size distribution, density, bulk density, moisture content, and pozzolanic activity index, providing a basis for adjusting the blending ratio; Blending ratio determination unit: Coal-based solid waste is used to replace part of the traditional raw materials for the production of target products. The benchmark blending ratio w0 of coal-based solid waste is determined through blending ratio test. Target products include ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, and new coal-based plastic masterbatch materials. Blending quality calculation unit: Calculates the blending quality M of coal-based solid waste. 煤基固废 =Target product quality M 产品 ×w; Carbon emission factor acquisition unit: Acquires the carbon emission factor F of the replaced traditional raw materials. 传统原材料 (Selected according to data source priority), calculate the carbon emission factor F of coal-based solid waste. 煤基固废 ; Carbon emission reduction calculation unit: Carbon emission reduction C is calculated using the carbon emission factor method. a =M煤基固废 ×(F 传统原材料 -F 煤基固废 ).

[0029] Exception handling unit: 1) When the mix proportion test is abnormal, the abnormality diagnosis mechanism is automatically triggered. The handling strategy is matched according to the abnormality type (such as abnormal raw material composition, equipment failure, process parameter deviation, etc.). For example, if the raw material composition is abnormal, the "raw material pretreatment enhancement instruction" is triggered (such as adding crushing and screening processes). After enhanced pretreatment, the activity index and particle size distribution are retested. If they still do not meet the standards, the "raw material replacement suggestion" is triggered (such as changing the type of auxiliary material), and the abnormality handling log is recorded and the blending ratio is recalculated. 2) When carbon emission factor data is missing, the default values ​​of IPCC and national greenhouse gas inventories stored in the industry default database will be called to establish a "dynamic update mechanism for default values". Data synchronization will be completed before the 15th of the first month of each quarter. The latest IPCC report has higher priority than the CEADs database. If there is no corresponding data in IPCC, the CEADs data will be used. When the difference between data from different sources is ≥10%, it will be marked as "data conflict" and all source data will be provided for users to manually select. The outdated default value will be automatically replaced and marked with "update time + original default value" and the data source will be marked as "default value". 3) Equipment failure handling: Establish a "backup data acquisition channel" (e.g., when GPS fails during transportation, call up license plate recognition and load estimation data from road surveillance cameras). Data verification unit: 1) Data consistency verification: blending quality M 煤基固废 The error between the material input ledger and the production line is ≤5%; 2) Reasonableness verification: The error between the transportation distance and the distance planned by map navigation is ≤10%; 3) Accuracy verification: The error between energy consumption data and settlement data from the power grid / gas company is ≤3%; Carbon emission reduction target management unit: used to set phased carbon emission reduction targets for industrial parks, production lines or products. The targets are broken down into "annual targets → quarterly targets → monthly targets → weekly targets → daily targets", and then allocated in layers of "industrial park → production line → process → equipment". The breakdown is based on the historical carbon emission ratio of each process, tracks the actual emission reduction progress, and generates carbon emission reduction performance reports that comply with international or domestic standards.

[0030] Data Security and Compatibility Unit: 1) Data security: The transmission layer uses HTTPS+VPN for encrypted transmission, and critical data is digitally signed; the storage layer uses tiered storage (sensitive data is encrypted and stored locally, and non-sensitive data is backed up in the cloud), and tiered access permissions are set; the backup mechanism is daily incremental backup + weekly full backup, and disaster recovery time is ≤4 hours. 2) Interface Standards: Supports OPC UA 1.0 and above, provides interface test cases for different versions of OPC UA devices to ensure compatibility with mainstream versions (1.0-1.05), RESTful API (carbon trading platform interface), JSON format (environmental supervision system interface), and provides interface test cases; This invention also proposes a smart carbon emission reduction management method for coal-based solid waste resource utilization industrial parks. It introduces a dynamic data fusion mechanism, collecting real-time data on operating conditions, energy consumption, and materials throughout the transportation, storage, pretreatment, and production processes. A reliability assessment model is constructed, dynamically fusing theoretical and measured values ​​to output accurate carbon emission data. Through bidirectional linkage between a three-dimensional process flow and a carbon emission Sankey diagram, transparent and visual display of carbon emissions is achieved. Based on monitoring data, carbon emission reduction optimization suggestions are provided, forming a closed-loop management system encompassing "accounting → monitoring → display → optimization." The steps include:

[0031] Real-time data acquisition: Real-time acquisition of operating data, energy consumption data, and material flow data throughout the entire solid waste treatment process to obtain measured and theoretical values ​​of carbon emissions. The sensor deployment scheme is shown in the table below;

[0032] Construct a credibility assessment mechanism: Based on sensor accuracy score, data integrity score, operating condition stability score, and data consistency score, assess the credibility of measured values; Dynamic data fusion: Based on the credibility assessment results, the weight ratio of theoretical carbon emission values ​​and measured values ​​is dynamically adjusted to calculate the final carbon emission value; 3D Visualization Linked Display: The final carbon emission data is visualized through a two-way linkage between the 3D process flow model and the carbon emission Sankey diagram, supporting multi-dimensional analysis and interactive operation; Carbon emission reduction optimization and closed-loop management: Based on monitoring data, high-emission links are automatically identified, quantitative optimization schemes are provided, and the optimization effects are verified to form a closed-loop management system.

[0033] Furthermore, the acquisition of measured and theoretical carbon emission values ​​can be divided into the following three aspects: 1) Calculation channel for measured values The measured values ​​are calculated based on direct sensor measurement data, and mainly include the following steps: Transportation segment: Calculated based on vehicle GPS mileage and actual load data, combined with the unit emission factor of the transportation mode; Stacking process: Calculated based on data from the stockpile volume monitoring instrument; Pre-processing stage: Based on data from electricity and gas meters, and combined with the energy consumption model of the pre-processing equipment, calculations are performed. Resource-based production process: Based on production line energy consumption and material input-output data, combined with product carbon footprint model calculations.

[0034] 2) Theoretical value calculation channel Theoretical values ​​are calculated based on material properties, process parameters, and emission factor models, and are used to supplement missing or abnormal measured data or to establish calibration benchmarks. Theoretical values ​​for transportation: calculated based on the distribution of solid waste sources, transportation route optimization model, and standard emission factors; Theoretical values ​​for stockpiling: calculated based on a model of solid waste stockpiling density, stockpiling height, and degradation rate; Pretreatment theoretical values: calculated based on energy consumption models of coal-based solid waste composition, calorific value, crushing, screening, and other processes; Theoretical production value: calculated based on product ratio, chemical reaction process, and energy conversion efficiency model.

[0035] 3) Dual-channel data collaboration mechanism Measured values ​​and theoretical values ​​are correlated in the following ways: Data completion: When measured data is missing or abnormal, theoretical values ​​are automatically retrieved to complete the data; Benchmark verification: Theoretical values ​​serve as a reference benchmark for the rationality of data, helping to identify sensor drift or process abnormalities; Dynamic calibration: Based on the credibility assessment results, the weights of the two channels are dynamically adjusted to achieve data fusion output.

[0036] Furthermore, the credibility assessment mechanism is constructed as follows: 1) The evaluation dimensions and methods are shown in the table below: Maximum permissible error standard table for sensors: 2) Comprehensive credibility calculation The initial weight coefficients (0.3, 0.5, 0.2) can be dynamically calculated based on the characteristics of different stages, or by using the entropy weight method based on the dispersion of the scoring data of each dimension, in order to enhance the adaptability of the model to different scenarios.

[0037] S 可信度 =0.3×(S) 误差 ×S 时效 ) + 0.5 × (0.7 × S 缺失 +0.3×S 异常 )+0.2×S 稳定 The credibility score ranges from 0 to 1, corresponding to 0% to 100%.

[0038] Furthermore, the specific details of dynamic weight adjustment and data fusion are as follows: The weighting adjustment formula dynamically adjusts the weight α of the measured values ​​based on the credibility score and data volatility: Where: σ 理论 σ 实测 These represent the standard deviations of the theoretical and measured values, respectively, reflecting the data volatility. β(S) 可信度 The confidence adjustment function is defined using the Sigmoid function to ensure the continuity of weight changes and avoid abrupt changes. Parameter 'a' controls the steepness of the curve, and parameter 'b' is the switching midpoint. Fitting steps: Based on the correlation analysis between the pilot park's credibility score (S) and the actual data quality, the values ​​of 'a' and 'b' are fitted using the least squares method (Example: when a=10, b=0.7, S=0.7→β=0.8, S=0.9→β=1.0); a parameter adjustment tool is provided, allowing users to customize parameters according to the actual data quality of the park.

[0039] Integration of output and alarm mechanism The final carbon emission value E is: The tiered alarm mechanism is as follows: Furthermore, the specific content displayed in conjunction with the 3D visualization and Sankey diagram includes: 1) Three-dimensional process flow modeling A full-process 3D model of the industrial park is constructed based on BIM or 3D scanning technology. The BIM model accuracy reaches LOD400, supporting visualization of equipment internal structure, pipelines, and sensor deployment locations. The model interfaces with the industrial park's ERP, MES, energy management system (EMS), and sensor network via standard industrial communication protocols (such as OPCUA and MQTT). It supports multi-terminal adaptation for mobile devices (WeChat mini-program / APP), desktop devices (Windows / Mac clients), and large-screen devices (industrial-grade monitors). The large-screen device supports 4K resolution and split-screen display (3D model on the left, Sankey diagram + trend curve on the right). It supports multiple users viewing online simultaneously, with hierarchical access permissions (administrators can modify parameters, while ordinary users can only view), and real-time recording of operation logs. It supports mobile device adaptation (WeChat mini-program, APP) and provides AR real-scene overlay function (scanning equipment displays real-time carbon emission data). Static facility models are updated monthly, while the model status of dynamic facilities (such as storage areas and mobile devices) is updated in real-time or near real-time based on sensor data streams or key events (such as process adjustment instructions). The response time for linking the 3D model with the Sankey diagram is ≤500ms, ensuring a real-time interactive experience.

[0040] 2) Carbon emission Sankey diagram design The Sankey diagram displays the carbon emission contribution of each stage in the form of flow rates: The left side shows the input sources (energy sources such as coal gangue, fly ash, gasification slag, electricity, natural gas, and diesel). The middle stage is the processing stage (transportation, pretreatment → crushing / screening / grinding / activation, production → mixing / sintering / forming / curing and other sub-stages); The right side shows the output items (ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, coal-based plastic masterbatch new materials, carbon emissions); The thickness of the lines indicates the amount of carbon emissions, and the color gradient is standardized: it is divided into 5 levels according to carbon emission intensity (≤0.1 tons of CO2 / ton of solid waste is green, 0.1~0.3 is light blue, 0.3~0.5 is yellow, 0.5~0.7 is orange, and >0.7 is red); the shade of the color indicates the carbon emission intensity.

[0041] 3) Two-way interactive linkage From 3D Model to Sankey Diagram: Click on any device in the 3D model, and the Sankey diagram will highlight the carbon emission flow for that stage; From Sankey diagram to 3D model: Click on a streamline in the Sankey diagram, and the 3D model will be located to the corresponding equipment or process section, and a real-time data panel will pop up.

[0042] Multi-dimensional filtering: Supports filtering carbon emission data by "product type (eco-soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, coal-based plastic masterbatch new materials, etc.), time (last day, last week, last month), and process (transportation, pretreatment, stockpiling, production)"; Trend Analysis: Carbon emission change curves are overlaid on the 3D model and Sankey diagram to visually demonstrate the trend of emission reduction effects; three curves are overlaid: "actual value → predicted value → optimized target value"; Comparative analysis: Supports comparative charts such as "actual carbon emissions vs. target carbon emissions" and "carbon emission ratio of different products".

[0043] 4) Multi-dimensional analysis function: "Emissions attribution analysis", supports three-level analysis by energy type → process → product (e.g., carbon emission ratio of diesel → transportation → ecological soil). Furthermore, the specific content of the carbon emission reduction optimization unit includes: 1) High-emission segment identification: Based on monitoring data, the carbon emission ratio of each segment is statistically analyzed. When the ratio of a certain segment exceeds a dynamic threshold (such as setting based on historical data or benchmarks of similar parks, manual adjustment is supported), optimization suggestions are triggered. 2) Quantitative Optimization Scheme: Provides specific adjustment suggestions for high-emission processes and integrates a "scenario simulation" function. Users can manually or through system recommendations adjust process parameters (such as crusher speed), energy structure (such as green electricity ratio), and logistics routes. The system quickly predicts changes in carbon emissions after adjustments based on built-in models, providing quantitative basis for user decision-making; "Cost-Emission Reduction Benefit Ratio" indicator (e.g., "Increasing the green electricity ratio by 10% reduces emission costs by 20 yuan / ton CO2, resulting in an 8% increase in emission reduction"); supports users setting cost constraints (e.g., total optimization cost ≤ 1 million yuan), and the system automatically selects the optimal scheme combination;

[0044] 3) Optimization effect verification: Compare the actual carbon emissions after optimization with the predicted values ​​to verify the effectiveness of the optimization measures; introduce a control group mechanism (select similar production lines that have not been optimized as controls) to eliminate interference from external factors; feed back the verification results to the carbon emission reduction target management unit to update the emission reduction progress, and use them as knowledge base cases for subsequent optimization recommendations, forming an enhanced closed loop of "monitoring → analysis → optimization → verification → target management".

[0045] 4) Optimization suggestion closed-loop tracking module: Optimization suggestions are classified according to urgency (high / medium / low) and implementation cost (high / medium / low), and suggestions with "low cost and high urgency" are given priority to be included in the production plan; a verification cycle is set according to the suggestion type (7 days for process parameter adjustment verification cycle and 30 days for equipment modification verification cycle), and an optimization effect report is automatically generated upon expiration; verified optimization cases are stored in the knowledge base, and similar scenarios are automatically recommended in the future.

[0046] Specific embodiments of the present invention: Example 1: Pre-trained Gradient Boosting Regression Model (GBR) 1. Model training data preparation (1) Definition of input feature vector Raw material composition vector X: particle size distribution (μm), density (g / cm³), bulk density (kg / m³), moisture content (%), pozzolanic activity index (%), loss on ignition (%).

[0047] Target product core performance vector Y (dimensions: 3-5, adjusted according to product type).

[0048] Ecological soil: pH value, organic matter content (g / kg), compaction degree (%); Mortar materials: compressive strength (MPa, 7d), water retention rate (%), setting time (h); 3D printing raw materials: flowability (s), bond strength (MPa), shrinkage (%); Subgrade materials: CBR value (%), compaction degree (%), permeability coefficient (mL / min); Underground filling and mine backfill materials: compressive strength (MPa, 28d), density (%), moisture content (%). New coal-based plastic masterbatch material: tensile strength (MPa), impact strength (kJ / m²), melt flow index (g / 10min).

[0049] (2) Sample set construction Historical production data from the coal-based solid waste resource utilization industrial park were collected, resulting in 1500 valid samples (covering 6 target products), including 1200 training samples and 300 validation samples. The samples were required to meet the prerequisite of "product performance meeting standards".

[0050] (3) Determining the label value The label value is the actual application value k of the process adjustment coefficient. 实 Based on the "actual blending ratio when product performance met standards" in the company's historical records, we can infer: k 实 =Actual blending ratio w 实 / w0 (w0 is the baseline blending ratio), all k 实 All are between 0.72 and 0.98.

[0051] 2. Model Training and Optimization (1) Feature preprocessing Standardization: All indicators in X are standardized using Z-score (mean μ=0, standard deviation σ=1) to eliminate the influence of dimensions; Feature screening: Using Pearson correlation coefficient analysis, features with k were excluded. 实 Redundant features with a real correlation of less than 0.1 (no redundant features in this embodiment); Data augmentation: The SMOTE algorithm is used to expand the sample size in the training set to ensure a balanced proportion of samples from various product categories.

[0052] (2) GBR model parameter settings Basic parameters: learning rate = 0.05, number of decision trees = 200, maximum depth of a single tree = 6, minimum number of splits per sample = 10, minimum number of leaf nodes per sample = 5; Regularization settings: L2 regularization is introduced (penalty coefficient λ=0.01) to avoid overfitting; Constraint function g: Add an activation function to the model output layer to force the value of k to fall within the interval [0.7, 1.0]. The formula is as follows: 3) Model training and validation Training process: Five-fold cross-validation is used, with mean squared error (MSE) as the loss function, and the model parameters are iteratively optimized; Validation results: MSE=0.0012 on the validation set, and coefficient of determination R²=0.92, indicating that the model's prediction accuracy meets engineering requirements; Performance priority guarantee: The prediction results are post-processed. If the mixing ratio corresponding to the predicted k value may lead to substandard performance (judged by the Y vector threshold), the k value is automatically reduced by 0.05-0.1 until the performance requirements are met.

[0053] 3. Model Application Examples Taking the production of mortar materials in a certain industrial park as an example: Input X: [350,2.65,1450,8.2,75,4.3] (corresponding to particle size distribution of 350μm, density of 2.65g / cm³, bulk density of 1450kg / m³, moisture content of 8.2%, pozzolanic activity index of 75%, and loss on ignition of 4.3%, respectively). Input Y: [12.5, 90.3, 8.5] (corresponding to 7-day compressive strength of 12.5 MPa, water retention rate of 90.3%, and setting time of 8.5 h, respectively); Model output: k=0.95 (falls in the range of 0.7-1.0, and the performance meets the standard at the corresponding blending ratio); Final mixing ratio: w = w0 - mortar material × k = 86.11% × 0.95 ≈ 81.80%.

[0054] Example 2: Calculation of carbon emission reduction in the production of mortar materials from coal-based solid waste 1. Scenario setting: Producing 100 tons of mortar (M product = 100t). Coal-based solid waste comes from two sources: Source A (coal gangue) and Source B (fly ash).

[0055] 2. Calculation steps (1) Determine the blending ratio and solid waste quality: Reference blending ratio w 0−砂浆材料 ≈86.11% (coal gangue: fly ash: other materials = 30:1:5), using the GBR model, with a process adjustment coefficient k = 0.95, the final total blending ratio w = w0. -砂浆材料 ×k=86.11%×0.95≈81.80%, total coal-based solid waste mass M 总煤基固废 =100t × 81.8% = 81.8t Among them, the mass of coal gangue M 煤矸石 =81.8t × (30 / 31) = 79.16t; Mass of fly ash M 粉煤灰 =81.8t × (1 / 31) = 2.64t (2) Obtaining the carbon emission factor of the replaced traditional materials: The replaced cement accounted for 2.64%, and the replaced natural aggregate (sand) accounted for 79.16%. (Refer to Appendix D of GB / T 51366-2019). Carbon emission factor F of ordinary silicate cement 水泥 =735kgCO2e / t, F 砂 =2.51kgCO2e / t, F 被代替传统材料 =Σ(wi×Fi)=2.64%×735+79.16%×2.51≈22.524kgCO2e / t (3) Carbon emission factor F of coal-based solid waste 煤基固废 (Multi-source transportation weighting) ① Weighted calculation of multi-source transportation distance Coal gangue sources: 2 stockpiles, M1=50 t, L1=10km; M2=29.16t, L2=20km; Weighted distance L 煤矸石 =Σ(M i ×L i ) / ΣM i =(50×10+29.16×20) / 79.16≈13.68km; Fly ash source: 1 stockpile, L 粉煤灰 =12 km; Total transport weighted distance (based on total mass of coal-based solid waste): L 总 =(79.16×13.68+2.64×12) / 81.8≈13.63 km.

[0056] ②C 总 Calculation (including energy consumption adjustment coefficient) The mode of transport is a heavy-duty gasoline truck (10t load capacity), F 运输 =0.104 kg CO2e / (t・km); C 运输 =81.8t×13.63km×0.104≈115.95kgCO2e; Coal gangue ρ 堆 =1.48t / m³, fly ash ρ 堆 =0.60t / m³; h=3.8m; A 占用煤矸石 =79.16 / (1.48×3.8)≈14.08 m²; A 占用粉煤灰 =2.64 / (0.6×3.8)≈1.16 m²; Forestry carbon sink factor F 林 =0.4 kg CO2e / m 2 (1 year) C 堆放=(14.08+1.16)×0.4≈6.096 kgCO2e; The energy consumption per unit mass of pretreated coal gangue is 1.1 kWh / t, and the energy consumption per unit mass of pretreated fly ash is 1.3 kWh / t; the carbon emission factor (grid emission factor) F of energy. 能 =0.581 kg CO2 / kw·h, pretreatment equipment service life 6 years (e 年限 =1.2), half-load operation (e 负荷 =1.3), e=1.56; Construction machinery efficiency index f 预处理煤矸石 =1.1×1.56≈1.716 kWh / t; Construction machinery efficiency index f 预处理粉煤灰 =1.3×1.56≈2.028 kWh / t; C 预处理 =79.16×1.716×0.581 + 2.64×2.028×0.581≈82.03 kgCO2e; Energy consumption per unit of target product produced = 0.75 kWh / t, e = 1.56, carbon emission factor (grid emission factor) F. 能 =0.581 kg CO2 / kw·h C 生产 =100×0.75×0.581≈43.58 kgCO2e; Total carbon emissions C 总 =C 运输 +C 堆放 +C 预处理 +C 生产 =115.95+6.096+82.03+43.58=247.66 kgCO2e Carbon emission factor F of coal-based solid waste 煤基固废 =C 总 / M 煤基固废 =247.66 / 81.8=3.03kgCO2e / t Carbon emission factor method for calculating carbon emission reductions: C a =M 煤基固废 ×(F 传统原材料 -F 煤基固废 )=81.8×(22.524-3.03)≈1594.61kgCO2e.

[0057] Example 3: Dynamic Data Fusion and Reliability Assessment in the Transportation Process Scenario setting: Monitor the transportation process of a batch of coal gangue (M=50t) from the coal-based solid waste production site to the industrial park.

[0058] Data collection: Actual measured value channel: Mileage obtained from vehicle GPS / BeiDou (L) 实测 =15.2km; load cell acquires load M 实测 =50.5t. The mode of transport is a heavy-duty gasoline truck (10t load capacity), F 运输 =0.104 kg CO2e / (t⋅km)

[0059] E 实测 =M 实测 ×L 实测 ×F 运输 =50.5t×15.2km×0.104≈79.83kgCO2e Theoretical value: Based on the route planning model (Gaode / Baidu Maps API), the optimal route distance L is theoretically 14.8km. The planned load value M is used. 理论 =50t

[0060] E 理论 =M 理论 ×L 理论 ×F 运输 =50t×14.8km×0.104=76.96kgCO2e Credibility assessment: Sensor accuracy: Mileage error: GPS maximum permissible error ±1%, the deviation between measured mileage and theoretical mileage is... (15.2−14.8) / 14.8≈2.7%, exceeding the limit, therefore S error=0 Calibration timeframe: GPS was last calibrated 60 days ago, S 时效 =max(0, ) =max(0, 0.75)=0.75, Precision sub-item score: S 误差 ×S 时效 =0 × 0.75 = 0 Data integrity: This batch of data has no missing data and no outliers. 缺失 =1,S 异常 =1. Completeness sub-item score: 0.7×1+0.3×1=1

[0061] Operational stability: The transport speed was stable, the load remained unchanged, and the process parameter fluctuation score was S. 稳定 =1 Overall credibility (using initial weights of 0.3, 0.5, 0.2): S可信度 =0.3×0 + 0.5×1 + 0.2×1 = 0 + 0.5 + 0.2 = 0.7 Dynamic weight adjustment and data fusion: Based on recent historical data, calculate the standard deviation between the theoretical and measured values: σ 理论 =1.8,σ 实测 =2.1.

[0062] The credibility correction function β(S) adopts the Sigmoid function, with parameters a=10 and b=0.7. Then...

[0063] Calculate the weight α of the measured value: The final carbon emission value of the merged output: kgCO2e Alarm mechanism: β(S) 可信度 =0.5, no alarm is triggered, and the data is used normally.

[0064] Example 4: Handling Abnormal Raw Material Composition in the Pretreatment Stage 1. Abnormal triggering scenarios Products manufactured: 3D printing raw materials, coal-based solid waste consisting of coal gangue and fly ash (w0=30%). Raw material composition test results: Volcanic ash activity index = 62% (lower than the product requirement of ≥70%), triggering a "raw material composition abnormality" alarm.

[0065] 2. Exception Handling Process (1) Automatic triggering of anomaly diagnosis mechanism The system identified the anomaly type as "abnormal raw material composition (activity index not up to standard)" and matched the processing strategy as "raw material pretreatment enhancement instruction".

[0066] (2) Enhanced implementation of raw material pretreatment Enhancement instructions: Increase the number of crushing and screening cycles (originally 1 crushing → 2 crushing, originally 1-stage screening → 2-stage screening) to refine particle size and improve activity index; Retesting: After enhanced pretreatment, the raw material composition was tested again, and the volcanic ash activity index was 73% (meeting the standard). If the standard is still not met: trigger "raw material replacement suggestion", suggest changing the type of auxiliary material (such as adding slag powder), and record the abnormal handling log (including abnormality type, handling measures, and test results).

[0067] (3) Recalculate the blending ratio After enhanced pretreatment, the raw material composition vector X is updated and input into the GBR model, resulting in a process adjustment coefficient k = 0.85 (original k = 0.78); the final blending ratio w = w0 × k = 30% × 0.85 = 25.5%; the blending mass M 煤基固废 =500t × 25.5% = 127.5t.

[0068] 3. Data verification and closed-loop management (1) Data validation 1) Data consistency verification: The error between the blending quality of coal-based solid waste and the material input ledger of the production line is ≤5%; 2) Reasonableness verification: The error between the transportation distance and the distance planned by map navigation is ≤10%; 3) Accuracy verification: The error between energy consumption data and settlement data from the power grid / gas company is ≤3%; (2) Closed-loop process After the anomaly is handled, the system updates the raw material composition database and the blending ratio calculation results, and stores the handled case in the knowledge base for automatic matching of similar anomalies in the future. At the same time, the carbon emission reduction calculation unit recalculates the emission reduction based on the updated blending ratio to ensure data consistency.

[0069] 4. Processing Results Raw material composition meets standards: Volcanic ash activity index = 73%, which meets the performance requirements of 3D printing raw materials; Blending ratio updated: w=25.5%, consistent with calculation logic; No secondary anomalies: The raw material composition remained stable during subsequent production, and no further anomaly alarms were triggered.

[0070] The beneficial effects of this invention are: 1. Achieved systematic carbon emission reduction accounting at the park level: Unlike existing technologies that are mostly limited to a single product or process, this invention proposes a carbon emission reduction calculation method based on the multi-product collaborative system of industrial parks. It covers a variety of mainstream resource utilization pathways, including ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, and coal-based plastic masterbatch new materials. It can systematically and comprehensively evaluate the carbon emission reduction benefits brought by the comprehensive utilization of coal-based solid waste, and provide a scientific basis for park-level carbon management.

[0071] 2. Introduction of dynamic data fusion mechanism: Improve the accuracy and real-time performance of accounting: By collecting real-time data on operating conditions, energy consumption and materials throughout the entire process of transportation, preprocessing, stacking and production, refining the sensor deployment scheme and data calibration mechanism, constructing an optimized reliability assessment model, and dynamically fusing theoretical values ​​and measured values, the traditional static carbon emission factor method is unable to reflect production fluctuations, and the accuracy and timeliness of carbon accounting are significantly improved.

[0072] 3. A complete carbon management closed loop has been established. Through carbon emission reduction target management units and scenario simulation functions, cost-emission reduction benefit analysis is integrated, and the carbon trading market is connected. Carbon management is extended from passive accounting and monitoring to proactive target setting, strategy simulation and performance evaluation, achieving full-process coverage of strategy, operation and verification.

[0073] 4. Achieve closed-loop management and visual interaction across the entire chain: Upgrade the visual interaction experience through two-way linkage and multi-dimensional analysis functions of 3D process flow and carbon emission Sankey diagram, support mobile terminals and AR functions, and realize transparent display of carbon emissions; add a carbon emission reduction optimization unit, provide quantitative optimization suggestions based on monitoring data and verify the effect, forming a closed-loop chain of "accounting → monitoring → display → optimization", and effectively improve the carbon management level of the industrial park.

[0074] The above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions implemented in the present invention, and should all be covered within the protection scope of the present invention.

Claims

1. A method for calculating carbon emission reduction in a coal-based solid waste industrial park, characterized in that, Includes the following steps: The target products are prepared by replacing some traditional raw materials with coal-based solid waste. The target products include one or more of the following: ecological soil, mortar materials, 3D printing raw materials, roadbed materials, underground filling and mine backfill materials, and coal-based plastic masterbatch new materials. The benchmark blending ratio of coal-based solid waste was determined through mix proportion tests; Based on the raw material composition detection results and the core performance indicators of the target product, the process adjustment coefficient is calculated through a pre-trained gradient boosting regression model, and the benchmark blending ratio is corrected by the process adjustment coefficient to determine the actual blending ratio of the coal-based solid waste. The blending quality of coal-based solid waste is calculated by combining the quality of the target product with the blending ratio. The carbon emission factors of the replaced traditional raw materials and the carbon emission factors of coal-based solid waste are obtained. Based on the difference between the two types of carbon emission factors and the blending quality of the coal-based solid waste, the carbon emission reduction is calculated using the carbon emission factor method.

2. The carbon emission reduction calculation method for a coal-based solid waste industrial park as described in claim 1, characterized in that, Before determining the actual blending ratio, the raw material composition of coal-based solid waste is tested, including particle size distribution, density, bulk density, moisture content and pozzolanic activity index. The results of the raw material composition test are used as the input basis for calculating the process adjustment coefficient.

3. The carbon emission reduction calculation method for a coal-based solid waste industrial park as described in claim 2, characterized in that, The calculation boundary of the carbon emission factor of coal-based solid waste is the entire resource utilization chain from the transportation of coal-based solid waste from its place of origin to the industrial park, to the end of the processing and production of the target product. The carbon emissions of coal-based solid waste at each stage of transportation, storage, pretreatment and production are calculated separately, and the total carbon emissions of the entire chain are summed. The total carbon emissions are then compared with the blending mass of the coal-based solid waste to obtain the carbon emission factor of the coal-based solid waste.

4. The carbon emission reduction calculation method for a coal-based solid waste industrial park as described in claim 3, characterized in that, If the raw materials being replaced are mixed materials, their comprehensive carbon emission factor is calculated by weighting the mass percentage of each material.

5. A smart management method for carbon emission reduction in a coal-based solid waste industrial park, based on the carbon emission reduction calculation method described in claim 3, characterized in that, Includes the following steps: In the four stages of transportation, stacking, pretreatment and production, operating condition data, energy consumption data and material flow data are collected in real time based on a cluster of multiple types of sensors to obtain the actual blending ratio fluctuation and actual carbon emission intensity of coal-based solid waste. The measured carbon emissions are calculated based on the real-time collected data, and the theoretical carbon emissions are calculated based on the raw material composition detection results, blending ratio and carbon emission factor of coal-based solid waste. A quantitative evaluation system is established from three dimensions: sensor accuracy, data integrity, and operating condition stability. The comprehensive credibility score of the measured carbon emission values ​​is calculated. Based on the standard deviation ratio between the theoretical and measured carbon emission values, and combined with a credibility correction function based on the Sigmoid function, the weight coefficient of the measured values ​​is calculated. The theoretical and measured values ​​are then fused according to the weight coefficients to obtain the dynamic carbon emission factors of the four stages of transportation, storage, pretreatment, and production. The dynamic carbon emission factor is fed back to the carbon emission factor acquisition stage to update the coal-based solid waste carbon emission factor used for carbon emission reduction calculation. Based on the actual blending ratio fluctuations and the updated raw material composition test results, the process adjustment coefficient is recalculated and the blending ratio is updated. The carbon emission reduction calculation results are iteratively optimized to achieve dynamic accounting and closed-loop management. A full-process 3D model of the industrial park is constructed based on BIM or 3D scanning technology, and a carbon emission Sankey diagram is designed. The dynamic carbon emission factors and the updated carbon emission reduction calculation results are integrated into the 3D model and Sankey diagram to achieve two-way interactive visualization.

6. The intelligent management method for carbon emission reduction in a coal-based solid waste industrial park according to claim 5, characterized in that, The deployment scheme for the multi-type sensor cluster is as follows: Transportation process: Vehicle-mounted GPS and Beidou dual-mode positioning devices are used to collect transportation mileage data, and vehicle-mounted weighing sensors are used to collect load data; During the stacking process: LiDAR and cameras are used to collect data on the volume and height of the stockpile. Preprocessing stage: Energy consumption data is collected using smart meters and gas meters; Production process: Energy consumption and material input / output data are collected using a cluster of sensors on the production line.

7. The intelligent management method for carbon emission reduction in a coal-based solid waste industrial park according to claim 5, characterized in that, The confidence correction function β(S) based on the Sigmoid function is determined in the following way: Where S is the overall credibility score, parameter a controls the steepness of the curve, and parameter b is the switching midpoint; the parameters a and b are determined by least squares fitting based on the correlation analysis between the credibility score of the pilot park and the actual data quality.

8. A carbon emission reduction calculation system for a coal-based solid waste industrial park, characterized in that, include: Raw material composition detection unit: Detects particle size distribution, density, bulk density, moisture content and pozzolanic activity index of coal-based solid waste, providing data basis for adjusting the blending ratio; Blending ratio determination unit: The benchmark blending ratio is determined through mix proportion test, the process adjustment coefficient is calculated by combining the gradient improvement regression model, and the benchmark blending ratio is corrected by the process adjustment coefficient to obtain the actual blending ratio; Blending quality calculation unit: Calculates the blending quality of coal-based solid waste based on the production quality of the target product and the actual blending ratio; Carbon emission factor acquisition unit: Acquires carbon emission factors of the replaced traditional raw materials according to preset priority, and calculates carbon emission factors of coal-based solid waste at the same time; Carbon emission reduction calculation unit: Calculates carbon emission reduction based on the blending quality of coal-based solid waste and the difference between the two types of carbon emission factors.

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