Sandstone aggregate mine carbon emission real-time monitoring device and metering method thereof

Abstract

The invention relates to the technical field of mine environmental protection, and discloses a gravel aggregate mine carbon emission real-time monitoring and metering method, which comprises the following steps: S1, multi-source sensing, including mining blasting monitoring, ore transportation monitoring and aggregate processing monitoring; s2, edge calculation, including data preprocessing, dynamic compensation calculation and local storage and communication; in the dynamic compensation calculation, the CO2 concentration is output and corrected in real time through a dynamic compensation algorithm, and the dynamic compensation algorithm comprises particulate matter dust drop, temperature and humidity deviation and vibration compensation; and S3, cloud platform processing is carried out, block chain evidence storage, carbon sink deduction calculation and early warning control are carried out, and the carbon sink deduction calculation is vegetation carbon sink absorption amount calculation deduction and waste rock recycling carbon emission reduction calculation deduction according to satellite data. The edge calculation layer adopts a dynamic compensation algorithm, temperature and humidity, vibration and particulate matter compensation items are fused in real time, and the hysteresis bottleneck of accounting is solved; a satellite-ground fusion deduction mechanism is adopted to construct a judicial-level trusted data chain.

Description

Technical Field

[0001] This invention relates to the field of environmental protection in mining, and in particular to a real-time monitoring device for carbon emissions from sand and gravel aggregate mines and its measurement method. Background Technology

[0002] As the global climate change problem becomes increasingly severe, people are increasingly aware that reducing greenhouse gas emissions is one of the effective means to mitigate climate change. Mining projects, due to their large implementation areas and significant impact on the surrounding environment, generate substantial total carbon emissions. Therefore, it is necessary to statistically analyze and calculate the carbon emissions of sand and gravel aggregate mines. In practice, the monitoring of carbon emissions from sand and gravel aggregate mines faces the following problems:

[0003] 1. Accounting lag problem

[0004] Currently, carbon emission accounting in mines mainly relies on manual statistics and periodic reports (such as monthly / quarterly summaries), resulting in long data update cycles and an inability to provide real-time feedback on production fluctuations. For example, carbon emission reduction accounting for waste rock resource recovery still uses the emission factor method (such as the IPCC standard equation), requiring the collection of energy consumption data afterward for calculation, leading to a lag of more than 7 days in carbon emission control. Although some systems attempt real-time monitoring, they only cover a single link (such as the processing end) and do not connect the entire chain of mining-transportation-processing, making it difficult to dynamically optimize emission reduction strategies.

[0005] 2. Environmental interference causes distortion in monitoring sensors.

[0006] Mining environments are characterized by high dust levels, high vibration, and significant temperature and humidity fluctuations, making traditional sensors (such as electrochemical CO2 sensors) susceptible to interference.

[0007] Particulate matter blockage: Dust from open-pit mines causes blockage in gas sampling pipelines, and the error rate of Shenzhen Ruijing's open-pit coal mine detection device exceeds 15%.

[0008] Temperature drift effect: Extreme temperatures of -30℃ to 70℃ cause the sensor baseline to drift. Actual measurement data from the Inner Mongolia Institute of Metrology show that uncompensated temperature and humidity deviations can amplify the error by up to 12%.

[0009] Insufficient vibration resistance: Explosion vibrations cause a decrease in sensor sensitivity, requiring frequent calibration.

[0010] 3. Lack of boundary delineation and carbon offsetting

[0011] The accounting boundaries are unclear: there is a lack of unified standards for carbon emission reduction accounting in the utilization of waste rock resources. For example, the carbon emission reduction method proposed by Ansteel does not clearly define the calculation scope of carbon sink loss due to vegetation destruction, and ignores indirect emissions during waste rock transportation.

[0012] Dynamic carbon sequestration not integrated: Satellite remote sensing vegetation carbon sequestration data is not linked with ground monitoring systems, resulting in the inability to offset carbon emissions in real time with the amount of carbon sequestration from ecological restoration.

[0013] 4. Data credibility and tamper-proof deficiencies

[0014] Existing blockchain technology is only used for post-processing data storage (such as the system of China Energy Engineering Corporation), and does not cover real-time verification during the monitoring process:

[0015] Verification lag: Data upload intervals can be as long as several hours, making it impossible to detect sensor anomalies or human tampering in a timely manner;

[0016] Redundancy deficiency: A single sensor failure can lead to a data loss, and a multi-node cross-validation mechanism is not used.

[0017] In summary, the technical problems in the existing carbon emission monitoring process of sand and gravel aggregate mines are as follows:

[0018] 1. Calculation timeliness: cycle > 7 days, reliance on manual reports.

[0019] 2. Environmental adaptability: Dust clogging + temperature drift, error rate >15%.

[0020] 3. Boundary integrity: Carbon sink deduction is missing, and there are no standards for waste rock accounting.

[0021] 4. Data credibility: Blockchain only stores evidence, without real-time verification.

[0022] The current technical bottlenecks lie in: data real-time performance, environmental robustness, system boundary conditions, and on-chain trustworthiness. Summary of the Invention

[0023] In order to overcome or alleviate one or more of the above technical problems, the purpose of this invention is to provide a real-time carbon emission monitoring device and its measurement method for sand and gravel aggregate mines, which is a comprehensive solution through multi-source sensor fusion, edge real-time compensation, dynamic deduction of carbon sinks across the entire chain and full-process blockchain verification.

[0024] This invention provides the following technical solution:

[0025] A method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines, comprising the following steps:

[0026] S1. Multi-source sensing, including mining blasting monitoring, ore transportation monitoring, and aggregate processing monitoring. Mining blasting monitoring employs an impact-resistant CO2 sensor array, a triaxial vibration sensor, and a laser scattering particle counter. Auxiliary modules include an active dust removal system and a temperature and humidity compensation module. Ore transportation monitoring involves installing an infrared dual-gas sensor, a load pressure sensor, a GPS positioning module, and an OBD interface power supply module on the vehicle-mounted terminal. Aggregate processing monitoring includes installing a turbine flow meter at the crusher inlet, a dustproof laser scattering particle counter at the screening machine outlet, and a pH sensor in the wastewater recovery tank.

[0027] S2, Edge computing, includes data preprocessing, dynamic compensation calculation, and local storage and communication. Data preprocessing involves Kalman filtering, data normalization, and preliminary outlier detection based on the sensor data collected in step S1. Dynamic compensation calculation outputs corrected CO2 concentration in real time through a dynamic compensation algorithm, which includes compensation for particulate matter dust settling, temperature and humidity deviation, and vibration. Local storage and communication are used for local storage of carbon sequestration data and communication with the cloud platform.

[0028] S3. Cloud platform processing includes blockchain notarization, carbon offset calculation, and early warning control. Blockchain notarization generates data hash values ​​in real time and synchronizes them to a distributed ledger, dynamically offsetting net emissions based on satellite remote sensing carbon data. This includes hash value storage, timestamp services, and digital signatures, with self-verification of the notarized data. Carbon offset calculation calculates and offsets the carbon absorption of vegetation and carbon emission reduction from waste rock resource utilization based on satellite data. Early warning control determines whether carbon emissions exceed standards based on monitored carbon emission data and issues corresponding early warnings according to a multi-level early warning mechanism.

[0029] According to some implementation methods, the sensors in step S1 are arranged in an anti-interference manner, specifically as follows:

[0030] A laser scattering particulate counter is used to separate large dust particles through spiral airflow, avoiding clogging of the sampling pipeline; an integrated constant temperature control module maintains the baseline stability of the sensor under temperature fluctuations of -30℃ to 70℃, with a temperature drift error ≤ ±0.5%; a redundant CO2 sensor array is deployed, with three sets of laser sensors arranged in a 120° ring, and a majority voting mechanism is used to eliminate single-point failures; an active dust removal system automatically scrapes off attached dust every 30 minutes, maintaining a gas permeability >95%;

[0031] During the blasting process, a GPS positioning module is used to synchronously record the blasting coordinates and timestamps, and vibration sensors are used to quantify the carbon emission intensity of a single blast. During transportation, the mining truck's onboard terminal obtains real-time fuel consumption E via the OBD interface. 运输 .

[0032] According to some implementation methods, the dynamic compensation algorithm in step S2 outputs the correction concentration value C in real time. 校正 The calculation formula is as follows:

[0033]

[0034] In the above formula, C 校正 The corrected CO2 concentration is in ppm.

[0035] C 原始 The original CO2 concentration from the sensor is in ppm.

[0036] ΔT represents the temperature deviation, calculated as measured value minus calibration value, in °C.

[0037] ΔH represents the humidity deviation, calculated as measured value minus calibration value, in percentage (%).

[0038] P 颗粒物 The concentration of particulate matter is μg / m³. 3 ;

[0039] V 振动 The peak value of the vibration acceleration is g, and the gravitational acceleration is g.

[0040] ΔE 废石 The carbon emission reduction from waste rock recycling, expressed as kg CO2 / h, is shown in the following formula:

[0041] ΔE 废石 =(E 原生开采 -E 废石运输 )× Waste rock utilization rate (7)

[0042] In the above formula, E 原生开采 Carbon emissions from primary ore mining, kgCO2 / ton of ore;

[0043] E 废石运输 Carbon emissions from waste rock transportation: kgCO2 / ton·km; Waste rock utilization rate: the proportion of waste rock replacing primary ore: %.

[0044] K1 is the basic calibration coefficient, calibrated at the sensor factory, with a value range of [0.98, 1.02].

[0045] K2 is the particulate matter compensation coefficient, ppm·m 3 / μg;

[0046] K3 is the vibration compensation coefficient, in ppm / g;

[0047] α, β, ε are temperature and humidity coupling coefficients, in °C. -1 ,% -1 .

[0048] According to some implementation methods, the dynamic compensation algorithm in step S2 outputs the correction concentration value C in real time.校正 The calculation formula is as follows:

[0049] C 校正 =1+αΔT+βΔHC 原始 ·K1+K2·P 颗粒物 -K3·V 振动 (3)

[0050] In the above formula, C 校正 The corrected CO2 concentration is in ppm.

[0051] C 原始 The original CO2 concentration from the sensor is in ppm.

[0052] ΔT represents the temperature deviation, calculated as measured value minus calibration value, in °C.

[0053] ΔH represents the humidity deviation, calculated as measured value minus calibration value, in percentage (%).

[0054] P 颗粒物 The concentration of particulate matter is μg / m³. 3 ;

[0055] V 振动 The peak value of the vibration acceleration is g, and the gravitational acceleration is g.

[0056] K1 is the basic calibration coefficient, calibrated at the sensor factory, with a value range of [0.98, 1.02].

[0057] K2 is the particulate matter compensation coefficient, ppm·m 3 / μg;

[0058] K3 is the vibration compensation coefficient, in ppm / g;

[0059] α, β, ε are temperature and humidity coupling coefficients, in °C. -1 ,% -1 .

[0060] According to some implementation methods, K3 = 0.28.

[0061] According to some implementation methods, step S3 includes the following steps:

[0062] S3.1 Blockchain traceability and evidence storage on the cloud platform. The blockchain evidence storage adopts dual-chain storage. The dual-chain storage process is as follows: the main chain generates a data packet hash value every 5 seconds and synchronizes it to the judicial node; the side chain stores the encrypted original data packet and supports decryption authorized by the judicial institution; after storage, evidence storage self-verification is performed.

[0063] S3.2 Dynamic carbon offsetting utilizes access to satellite data, including NDVI index acquisition and vegetation cover analysis. The carbon sink calculation model is as follows:

[0064] S碳汇 =NDVI×A×ρ×γ (4)

[0065] Among them, S 碳汇 The carbon sequestration capacity of vegetation is expressed in kg CO2 / h.

[0066] NDVI is the normalized vegetation index, [0,1];

[0067] A represents the vegetation cover area, in meters. 2 ;

[0068] ρ is the vegetation density coefficient, i.e., biomass per unit area, kg / m². 2 ;

[0069] γ is the carbon conversion efficiency coefficient, i.e. CO2 fixation rate, kg CO2 / kg biomass;

[0070] Satellite remote sensing data is used to retrieve vegetation carbon sink S using the NDVI index. 碳汇 The net carbon emission value Cnet is dynamically adjusted, as shown in the following formula:

[0071] C 净排放 =C 校正 -S 碳汇 ·η 吸收率 (5)

[0072] Where η 吸收率 The vegetation CO2 absorption rate, with a value range of [0,1];

[0073] C 净排放 Net carbon emissions after deduction, kg CO2 / h;

[0074] η 吸收率 Dynamic matching based on vegetation type in the mining area;

[0075] S3.3 Early Warning Control employs a multi-level early warning mechanism for multi-level early warning response, as follows:

[0076] Level 1 warning: When carbon emissions during the processing stage exceed the threshold by 20%, an audible and visual alarm will be triggered.

[0077] Level 2 warning: When the standard is exceeded for 3 consecutive times or the change rate is >10% / min, the feed speed of the crusher will be automatically reduced and the dust removal and pressurization will be activated.

[0078] Level 3 warning: When carbon emissions during transportation exceed the threshold by 50%, the mining truck engine is locked and an emission reduction plan is sent to the dispatch center.

[0079] According to some implementation methods, the evidence self-verification in step S3.1 includes the following steps:

[0080] S3.1.1 Abnormal data identification: When the data is abnormal, such as temperature and humidity deviation ΔT>5℃ or ΔH>10%, self-calibration is automatically started and the process proceeds to step S3.1.2.

[0081] Algorithms for anomaly identification include Isolation Forest and Local Anomaly Factor, as follows:

[0082] Isolation Forest: Quickly locates outliers by randomly segmenting the feature space;

[0083] Local anomaly factor: Calculates the local density deviation of data points to identify low-density anomalies;

[0084] S3.1.2 Redundancy verification, wherein the redundancy verification is to enable the three-sensor ring array to perform a two-out-of-three voting;

[0085] S3.1.3 Data Reconstruction, Performing Data... 重构 The calculation is as follows:

[0086] Data 重构 =α·SensorA +β·SensorB +γ·Satellite; (8)

[0087] In the formula, Data 重构 The data is the reconstructed effective data, kg CO2 / h; Sensor A / B are the redundant sensor measurements, kg CO2 / h; Satellite is the satellite-retrieved carbon emission reference value, kg CO2 / h; α, β are sensor weighting coefficients; γ is the satellite data weighting coefficient;

[0088] S3.1.4 Blockchain notarization encrypts the reconstructed data to generate a new hash value, which is then synchronously updated to the main chain and side chain.

[0089] On the other hand, the present invention also provides a real-time carbon emission monitoring device for sand and gravel aggregate mines, comprising:

[0090] The sensing layer is used for monitoring mining blasting, ore transportation, and aggregate processing. Mining blasting monitoring employs an impact-resistant CO2 sensor array, a triaxial vibration sensor, and a laser-scattering particulate counter. Auxiliary modules include an active dust removal system and a temperature and humidity compensation module. Ore transportation monitoring involves installing an infrared dual-gas sensor, a load pressure sensor, a GPS positioning module, and an OBD interface power supply module on the vehicle-mounted terminal. Aggregate processing monitoring includes installing a turbine flow meter at the crusher inlet, a dustproof laser-scattering particulate counter at the screening machine outlet, and a pH sensor in the wastewater recovery tank.

[0091] The edge computing layer is used for data preprocessing, dynamic compensation calculation, and local storage and communication. Data preprocessing involves Kalman filtering, data normalization, and preliminary outlier detection based on the sensor data collected in step S1. Dynamic compensation calculation outputs corrected CO2 concentration in real time through a dynamic compensation algorithm, which includes compensation for particulate matter dust settling, temperature and humidity deviation, and vibration. Local storage and communication are used for local storage of carbon sequestration data and communication with the cloud platform.

[0092] The cloud platform is used for blockchain-based evidence storage, carbon offset calculation, and early warning control. Blockchain evidence storage involves generating data hash values ​​in real time and synchronizing them to a distributed ledger. It dynamically offsets net emissions by combining satellite remote sensing carbon data. Specifically, this includes hash value storage, timestamp services, and digital signatures, and performs self-verification of the evidence. Carbon offset calculation involves calculating and offsetting carbon absorption by vegetation and carbon emission reduction through waste rock resource utilization based on satellite data. Early warning control determines whether carbon emissions exceed standards based on monitored carbon emission data and issues corresponding warnings according to a multi-level early warning mechanism.

[0093] Compared with the prior art, the present invention has the following beneficial effects:

[0094] This invention provides a real-time carbon emission monitoring device and measurement method for sand and gravel aggregate mines, solving the problems of large lag, high sensitivity to environmental interference, and insufficient data reliability in traditional accounting methods. It achieves a technological leap from manual statistics to real-time, accurate measurement of carbon emissions, providing highly reliable data support for mine carbon reduction management and carbon trading. Specifically:

[0095] 1. Addressing the bottleneck of accounting lag

[0096] The edge computing layer employs a dynamic compensation algorithm to integrate temperature, humidity, vibration, and particulate matter compensation items in real time. It features full-chain 5G-MQTT transmission, enabling second-level data connectivity across the entire mining, transportation, and processing process. The carbon emission accounting cycle is reduced from the traditional >7 days to ≤5 seconds. It also provides real-time feedback on production fluctuations (such as outputting compensation concentration C correction within 30 seconds after blasting) to support the optimization of dynamic emission reduction strategies.

[0097] 2. Eradicate environmental interference distortion

[0098] The shock-resistant sensing layer design utilizes a three-sensor redundant array (120° ring distribution, two out of three voting). A multi-factor coupled compensation model is adopted, with a new vibration compensation term added, and K3 = 0.28 for explosion calibration; the constant temperature module suppresses temperature drift (drift ≤ ±0.5% from -30℃ to 70℃), and the environmental interference error rate is reduced from >15% to ≤3%.

[0099] 3. Achieve dynamic carbon offsetting across the entire carbon sequestration chain.

[0100] A satellite-ground fusion offsetting mechanism was adopted: the NDVI index is updated every 30 minutes (10m resolution), and S is calculated in real time. 碳汇 An extended application of a carbon sink model in a steel plant, dynamically matching the absorption rate η. 吸收率 .

[0101] Real-time accounting of waste rock resource utilization: ΔE 废石 The deduction amount is dynamically injected, and the boundaries of the transportation process are clearly defined.

[0102] The average annual carbon offset is 12,000 tons of CO2; addressing the pain point of unmeasured vegetation damage losses.

[0103] 4. Construct a judicial-grade trusted data chain

[0104] Real-time verification using a dual-chain blockchain: the main chain stores hash values, and the side chain stores encrypted data packets; smart contract triggering conditions: automatic redundancy verification when ΔT>5℃ or ΔH>10%;

[0105] Self-calibrated judicial closed loop: anomaly identification (isolated forest + LOF dual model) → data reconstruction → 5-second evidence storage, reducing the risk of data tampering by 98% (compared to the traditional 10-second evidence storage interval); generating evidence packages (timestamp + location hash) that comply with the Data Security Law, which can be directly used for judicial authentication of carbon trading. Attached Figure Description

[0106] Figure 1 A block diagram of a real-time carbon emission monitoring device for sand and gravel aggregate mines provided in an embodiment of the present invention.

[0107] Figure 2 A schematic diagram of the multi-level early warning mechanism of the early warning control center of the real-time carbon emission monitoring device for sand and gravel aggregate mines provided in this embodiment of the invention.

[0108] Figure 3 This is a schematic diagram of the self-calibration process of the metering method for the real-time carbon emission monitoring device for sand and gravel aggregate mines provided in this embodiment of the invention. Detailed Implementation

[0109] The present invention will now be described in detail with reference to embodiments and accompanying drawings. However, it should be understood that the embodiments and drawings are for illustrative purposes only and do not constitute any limitation on the scope of protection of the present invention. All reasonable modifications and combinations included within the inventive spirit of the present invention fall within the scope of protection of the present invention.

[0110] The present invention will be further described below with reference to the accompanying drawings.

[0111] Example 1

[0112] like Figure 1This embodiment provides a real-time carbon emission monitoring device for sand and gravel aggregate mines, including an environment-adaptive sensing layer, an edge computing layer, and a cloud platform.

[0113] The sensing layer employs a multi-source sensor array, specifically including a mining blasting monitoring unit, an ore transportation monitoring unit, and an aggregate processing monitoring unit. The core sensors in the mining blasting monitoring unit include an impact-resistant CO2 sensor array (explosion-proof rating ExibI Mb, IP68 protection), a triaxial vibration sensor (range ±50g, accuracy ±0.1g), and a laser scattering particle counter (measurement range 0-1000μg / m³). 3 The auxiliary modules include an active dust removal system (spiral airflow separation + automatic cleaning bar) and a temperature and humidity compensation module (operating range of -30℃ to 70℃).

[0114] The vehicle-mounted terminal of the ore transportation monitoring unit includes a mid-infrared dual gas sensor (CH4 / CO2 synchronous detection), a load pressure sensor (range 0-50 tons, accuracy ±0.5%), a GPS positioning module (accuracy ±0.1m, update frequency 1Hz) and an OBD interface power supply module (12 / 24V adaptive).

[0115] Key monitoring points of the aggregate processing monitoring unit include:

[0116] Crusher inlet: Turbine flow meter (range 0-500m) 3 / h, 4-20mA output);

[0117] Screening machine outlet: Dustproof laser scattering particle counter (IP68);

[0118] Wastewater recycling tank: pH sensor (range 0-14 pH, accuracy ±0.1 pH);

[0119] More specifically, the deployment optimization for specific sensor models (integrating anti-interference and multi-source monitoring) is as follows:

[0120] 1. In the mining blasting process, the following sensor selection is adopted:

[0121] Shock-resistant CO2 sensor: A mining-grade intrinsically safe infrared sensor (GRG5H type) is selected, with explosion-proof rating Ex ib IMb and shock-resistant rating IK08. It features a built-in temperature drift compensation chip (baseline drift ≤ ±0.5% within the range of -30℃ to 70℃). Deployed in a 200m radius annular zone around the blast point (4 redundant sets), it separates dust via spiral airflow (transmittance > 95%).

[0122] Vibration synchronous monitoring: A triaxial accelerometer (range ±50g) is installed to record the blasting vibration waveform, providing a vibration correction term V for the dynamic compensation algorithm. 振动 Input data.

[0123] 2. For the ore transportation process, the following sensor selection is adopted:

[0124] Mining truck terminal integration:

[0125] Exhaust gas monitoring module: adopts mid-infrared dual gas sensors (CH4 / CO2 synchronous detection), power consumption 38.3mW, CO2 detection error ≤0.05% (using low-power dual gas sensing technology);

[0126] Load matching system: Pressure sensors collect ore tonnage in real time, and combined with GPS positioning data (accuracy ±0.1m) to calculate ton-kilometer carbon emissions.

[0127] 3. In the aggregate processing stage, multiple monitoring points were adopted:

[0128] At the inlet of the coarse crusher: install a turbine flow meter (range 0-500m). 3 / h)+NDIR multi-gas analyzer (detects CO2 and dust concentration);

[0129] Screening machine outlet: Equipped with a dustproof laser scattering particle counter (IP68) to provide real-time feedback on aggregate powder content and airflow carbon content;

[0130] Wastewater recycling pond: Electrode-type pH sensor monitors carbon sequestration loss from acidic wastewater (referencing the vegetation destruction compensation term S from the Ansteel carbon sequestration model). 碳汇 ).

[0131] The CO2 laser sensor, temperature and humidity compensation module, vibration sensor and active dust removal components cover the entire process of mining blasting, ore transportation and aggregate processing.

[0132] The anti-interference design scheme for multi-source sensor groups is as follows:

[0133] A laser scattering particle counter is used to separate large dust particles through spiral airflow, avoiding blockage of the sampling pipeline;

[0134] An integrated constant temperature control module maintains the baseline stability of the sensor under temperature fluctuations of -30℃ to 70℃, with a temperature drift error of ≤±0.5%.

[0135] A redundant CO2 sensor array (three sets of laser sensors are arranged in a 120° ring) is deployed to eliminate single points of failure through a majority voting mechanism.

[0136] Active dust removal system: Built-in motor-driven cleaning bar (borrowing the self-cleaning structure of a highly adaptable detection device) automatically scrapes off attached dust every 30 minutes, maintaining gas permeability >95%.

[0137] The sensing layer has process penetration capabilities for real-time monitoring of the entire process.

[0138] Blasting process: GPS positioning module (accuracy ±0.1m) synchronously records blasting coordinates and timestamps, and combines vibration sensors to quantify the carbon emission intensity of a single blast;

[0139] Transportation: The mining truck's onboard terminal obtains real-time fuel consumption E via the OBD interface. 运输 The specific conversion formula is as follows:

[0140] E 运输 = Fuel consumption × EF 柴油 ×Load factor (1)

[0141] The edge computing layer includes a data preprocessing module, a dynamic compensation algorithm module, and a local storage and communication module. The data preprocessing module performs signal filtering (Kalman filtering algorithm), data normalization, and preliminary outlier detection. The dynamic compensation algorithm module outputs a corrected concentration value C in real time using a dynamic compensation algorithm (incorporating particulate matter dust settling, temperature and humidity deviation, and vibration compensation terms). 校正 The calculation formula is as follows:

[0142]

[0143] In the above formula, C 校正 The corrected CO2 concentration is in ppm.

[0144] C 原始 The original CO2 concentration from the sensor is in ppm.

[0145] ΔT represents the temperature deviation, calculated as measured value minus calibration value, in °C.

[0146] ΔH represents the humidity deviation, calculated as measured value minus calibration value, in percentage (%).

[0147] P 颗粒物 The concentration of particulate matter is μg / m³. 3 ;

[0148] V 振动 The peak value of the vibrational acceleration is given by g (gravitational acceleration).

[0149] ΔE 废石 Carbon emission reduction from waste rock recycling, kg CO2 / h;

[0150] K1 is the basic calibration coefficient, calibrated at the sensor factory, with a value range of [0.98, 1.02].

[0151] K2 is the particulate matter compensation coefficient, ppm·m 3 / μg;

[0152] K3 is the vibration compensation coefficient, in ppm / g;

[0153] α, β, ε are temperature and humidity coupling coefficients, in °C. -1 ,% -1 ; .

[0154] Furthermore, the dynamic compensation algorithm for the edge computing layer is upgraded to adopt a multi-factor coupled compensation model, as shown in the following equation:

[0155] C 校正 =1+αΔT+βΔHC 原始 ·K1+K2·P 颗粒物 -K3·V 振动 (3)

[0156] New vibration compensation item V 振动 (Unit: g), coefficient K3 is determined by blasting impact test (referencing vibration monitoring data from Ansteel's waste rock carbon emission reduction method).

[0157] The monitoring error rate using the dynamic compensation algorithm is ≤3%.

[0158] The cloud platform includes a blockchain evidence storage module, a carbon offset calculation module, and an early warning and control center. The blockchain evidence storage module includes a hash value storage module (SHA-256 algorithm), a timestamp service module (ISO 8601 standard), and a digital signature module (RSA-2048).

[0159] Furthermore, the blockchain evidence storage layer enhancement mechanism adopts a dual-chain verification architecture:

[0160] The main chain (using the Zhongnengjian framework) stores hash values, while the side chain (private chain) stores the original encrypted data package, achieving "data verifiable but not visible"; smart contract trigger conditions: when the temperature and humidity deviation ΔT>5℃ or ΔH>10%, redundant sensors are automatically activated to verify and record abnormal events.

[0161] The carbon offset calculation module accesses satellite data, including oNDVI index acquisition (updated every 30 minutes) and vegetation cover analysis (10m resolution). The carbon offset calculation model is as follows:

[0162] S 碳汇 =NDVI×A×ρ×γ (4)

[0163] Among them, S 碳汇 The carbon sequestration capacity of vegetation is expressed in kg CO2 / h.

[0164] NDVI is the normalized vegetation index, [0,1];

[0165] A represents the vegetation cover area, in meters. 2 ;

[0166] ρ is the vegetation density coefficient, i.e., biomass per unit area, kg / m². 2 ;

[0167] γ is the carbon conversion efficiency coefficient, i.e. CO2 fixation rate, kg CO2 / kg biomass;

[0168] Real-time carbon credit calculation:

[0169] Satellite remote sensing data is used to retrieve vegetation carbon sink S using the NDVI index. 碳汇 Dynamically adjust net carbon emissions:

[0170] C 净排放 =C 校正 -S 碳汇 • η absorption rate (5)

[0171] Where η 吸收率 The vegetation CO2 absorption rate, with a value range of [0,1];

[0172] C 净排放 Net carbon emissions after deduction, kg CO2 / h;

[0173] η 吸收率 Based on dynamic matching of vegetation types in the mining area (extended Ansteel carbon sink model), the cloud platform performs real-time carbon sink deduction and dynamic matching.

[0174] The NDVI index is updated every 30 minutes using satellite remote sensing data. The vegetation carbon absorption rate η is then retrieved using the Ansteel carbon sink model. 吸收率 ;

[0175] Carbon emission reduction accounting for waste rock resource utilization:

[0176] Based on Ansteel's waste rock carbon reduction method, the carbon deduction item for waste rock utilization is defined as follows:

[0177] ΔE 减排 =E 原生开采 -E 废石运输 (6)

[0178] Carbon emissions from primary ore mining 原生开采 Including vegetation damage and loss.

[0179] Waste rock transportation deduction amount ΔE 废石 Calculate in real time using the following formula:

[0180] ΔE 废石 =(E 原生开采 -E 废石运输 )× Waste rock utilization rate (7)

[0181] In the above formula, ΔE 废石 Carbon emission reduction from waste rock utilization, kg CO2 / h;

[0182] E 原生开采Carbon emissions from primary ore mining, kg CO2 / ton of ore;

[0183] E 废石运 Carbon emissions from waste rock transportation: kg CO2 / ton-km; Waste rock utilization rate: the proportion of waste rock replacing primary ore, %.

[0184] The blockchain storage layer generates a data hash value every 5 seconds and synchronizes it to the distributed ledger, which is then combined with satellite remote sensing carbon sink data to dynamically offset net emissions.

[0185] The early warning and control center is used to issue early warnings based on a multi-level early warning mechanism for carbon emissions, such as... Figure 2 By monitoring carbon emission data, it is determined whether carbon emissions exceed the standard. Under different circumstances, it issues a first-level warning (audible and visual alarm), a second-level warning (equipment adjustment), and a third-level warning (emergency shutdown). Each level is manually confirmed after execution.

[0186] For example, the processing method for a multi-level early warning mechanism is set as follows:

[0187] Level 1 warning: When carbon emissions during the processing stage exceed the threshold by 20%, an audible and visual alarm (120dB buzzer + red LED) will be triggered.

[0188] Level 2 warning: When the standard is exceeded for 3 consecutive times or the change rate is >10% / min, the feed speed of the crusher will be automatically reduced and the dust removal and pressurization will be activated.

[0189] Level 3 warning: When carbon emissions during transportation exceed the threshold by 50%, the mining truck engine is locked and an emission reduction plan is sent to the dispatch center.

[0190] Furthermore, the connections between the various systems are as follows:

[0191] The connection between the sensing layer and the edge computing layer includes:

[0192] Wired connection: RS-485 industrial bus, maximum transmission distance 1200m;

[0193] Wireless connectivity: LoRa transmission (blast zone): frequency 470MHz, transmission distance 2km; 5G transmission (mobile devices): uplink speed 100Mbps.

[0194] The connection between the edge computing layer and the cloud platform includes:

[0195] 5G network transmission: Uplink bandwidth: 50Mbps; Network latency: <20ms; Transmission protocol: MQTT+TLS;

[0196] Downlink control commands, i.e., the connection between the cloud platform and the sensor layer, adopt the OPC UA protocol: command response time: <100ms; control accuracy: ±0.5%; security authentication: two-way digital certificate.

[0197] The data processing flow for collaborative computing between the edge computing layer and the cloud platform has been upgraded, with dynamic compensation implemented at the edge computing layer and algorithm enhancements: In addition to the existing temperature and humidity compensation, vibration compensation and waste rock deduction logic have been added.

[0198] Where ΔE 废石 Carbon emission reduction from waste rock resource utilization (using Ansteel's waste rock accounting formula). Communication protocol: The edge gateway uploads data via the 5G-MQTT protocol. The data packet includes a timestamp, location hash value, and self-checking code.

[0199] This device solves the problems of large lag in traditional accounting, high sensitivity to environmental interference, and insufficient data reliability, realizing a technological leap from manual statistics to real-time accurate measurement of carbon emissions, and providing highly reliable data support for mine carbon emission reduction management and carbon trading.

[0200] Example 2

[0201] This embodiment uses a granite mine as a case study to provide the monitoring process of a real-time carbon emission monitoring device for sand and gravel aggregate mines based on Embodiment 1, as follows:

[0202] Phase 1: Hardware Installation

[0203] Blasting zone sensor: Drilled and anchored installation, impact resistance test meets 30g instantaneous acceleration (referencing GRG5H sensor impact resistance standard);

[0204] Mining truck terminal: Powered by OBD interface, electromagnetic shielding reduces engine interference (field strength < 0.1T).

[0205] Phase Two: Algorithm Optimization

[0206] Vibration compensation coefficient calibration: K3 = 0.28 by fitting formula (2) or (3) through blasting experiment (verified by Ansteel vibration monitoring data); Edge gateway stress test: response delay < 200ms under 128 concurrent nodes (Huawei Mining Hong system certification).

[0207] Phase 3: System Integration and Testing

[0208] Blockchain evidence storage interval: adjusted from 10 seconds to 5 seconds (China Energy Construction framework latency optimization);

[0209] Carbon sink deduction error rate: After satellite-ground data fusion, the absorption rate calculation error is ±2.1% (actual measurement in Anshan mining area).

[0210] The results of the implementation are shown in the table below:

[0211] index Before deployment After deployment Monitoring timeliness Manual statistics (7-day cycle) Real-time generation (5-second delay) Transportation accounting error Tonnage estimation error > 15% Load matching error ≤3% Carbon credit Not dynamically integrated <![CDATA[Annual deduction volume: 12,000 tons of CO2]]>

[0212] Example 3

[0213] This embodiment provides a metering method based on the monitoring device of Embodiment 1, including the following steps:

[0214] S1: Multi-source sensing, real-time data acquisition throughout the entire process.

[0215] The mining blasting monitoring unit collects CO2 concentration data using an impact-resistant CO2 sensor array (deployed in a 200m radius ring around the blasting point), and records the blasting coordinates using a GPS module (±0.1m), transmitting the data via the LoRa protocol.

[0216] (470MHz, 2km transmission) transmitted to the edge computing layer;

[0217] The ore transportation monitoring unit synchronously detects CH4 / CO2 concentration through mid-infrared dual gas sensors on the vehicle terminal, and calculates ton-kilometer load through a load pressure sensor (0-50 tons, ±0.5%) linked with the GPS module, and transmits the data via 5G-MQTT protocol (uplink rate 100Mbps).

[0218] The aggregate processing monitoring unit monitors the flow rate of the turbine at the crusher inlet (0-500m³). 3 Data from the NDIR gas analyzer and the dustproof particulate counter (IP68) at the outlet of the screening machine are uploaded via RS-485 industrial bus.

[0219] S2: Edge computing, firstly, multi-source fusion correction

[0220] The edge computing layer performs Kalman filtering, data normalization, and preliminary outlier detection through the data preprocessing module, and performs dynamic compensation calculation through the dynamic compensation algorithm module, where K3 = 0.28, and ΔT and ΔH are provided and calculated in real time by the temperature and humidity sensors in the sensing layer.

[0221] Next, anomaly detection is performed. The anomaly detection mechanism is a machine learning model (Isolated Forest + LOF algorithm) deployed on the edge layer FPGA chip to analyze the data stream in real time. When the difference between adjacent sensor data is >15% (such as the deviation between two points in a ring-shaped CO2 array):

[0222] S3: Cloud platform processing. S3.1 blockchain traceability and evidence storage on the cloud platform. The blockchain evidence storage module adopts dual-chain evidence storage. The dual-chain evidence storage process is as follows: Main chain (consortium chain): Generates data packet hash value (SHA-256 algorithm) every 5 seconds and synchronizes it to judicial nodes (court / notary office); Side chain (private chain): Stores encrypted original data packets (AES-256 encryption) and supports decryption authorized by judicial institutions;

[0223] After the evidence is stored, a self-calibration process can be used to verify the evidence.

[0224] S3.2: Dynamic carbon credit deduction. The deduction process in the carbon credit deduction calculation module is as follows:

[0225] S3.2.1 Satellite data access: NDVI index is acquired every 30 minutes (Landsat-9 satellite, resolution 10m);

[0226] S3.2.2 Perform dynamic carbon offset calculation, i.e., perform the calculation using formula (6).

[0227] S3.2.3 Calculation of waste rock resource utilization deduction, that is, to calculate using formula (7), to obtain C 净排放 .

[0228] S3.3: Early Warning Feedback Control

[0229] The multi-level early warning mechanism of the early warning control center in this embodiment is as follows:

[0230] Level 1 warning: When carbon emissions during the processing stage exceed the threshold by 20%, an audible and visual alarm (120dB buzzer + red LED) will be triggered.

[0231] Level 2 warning: When the standard is exceeded for 3 consecutive times or the change rate is >10% / min, the feed speed of the crusher will be automatically reduced and the dust removal and pressurization will be activated.

[0232] Level 3 warning: When carbon emissions during transportation exceed the threshold by 50%, the mining truck engine is locked and an emission reduction plan is sent to the dispatch center.

[0233] The early warning control center reaches the sensing layer device via the OPC UA protocol (response time ≤ 200ms).

[0234] Example 4

[0235] This embodiment provides a self-calibration process for step S3 based on embodiment 3, used for verifying the on-chain evidence storage data during the blockchain evidence storage process. The blockchain evidence storage module is used to perform the self-calibration process, such as... Figure 3 Specifically, it includes the following steps:

[0236] S3.1.1 Abnormal data identification: When the data is abnormal, such as temperature and humidity deviation ΔT>5℃ or ΔH>10% (reported from the edge computing layer), self-calibration is automatically initiated.

[0237] The core algorithms for anomaly detection include Isolation Forest and Local Anomaly Factor, as follows:

[0238] Isolation Forest: By randomly segmenting the feature space, it can quickly locate outliers (such as a sudden increase in dust concentration >50% or a temperature drift exceeding ±5%).

[0239] Local anomaly factor (LOF): Calculates the local density deviation of data points to identify low-density anomalies (such as continuous zero-value output caused by sensor failure).

[0240] S3.1.2 Redundancy verification: Redundancy verification enables the three-sensor ring array to perform a two-out-of-three voting (supported by the mining unit hardware);

[0241] S3.1.3 Data Reconstruction, Performing Data... 重构 The calculation is as follows:

[0242] Data 重构 =α·SensorA +β·SensorB +γ·Satellite; (8)

[0243] In the formula, Data is the reconstructed effective data, kg CO2 / h; Sensor A / B are the measurements of redundant sensors, kg CO2 / h; Satellite is the satellite-retrieved carbon emission reference value, kg CO2 / h; α, β are the sensor weighting coefficients; γ is the satellite data weighting coefficient.

[0244] S3.1.4 Blockchain notarization encrypts the reconstructed data to generate a new hash value, which is then synchronously updated to the main chain and side chain.

[0245] Therefore, the self-calibration process realizes a closed-loop self-calibration of "anomaly identification → redundancy verification → data reconstruction → judicial evidence preservation", breaking through the limitations of traditional single-stage calibration (such as the Zhejiang CEMS system which only supports data cleaning), and providing a full-cycle reliable data chain for mine carbon monitoring that complies with ISO 14064-2 standard certification.

[0246] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines, characterized in that: Includes the following steps: S1. Multi-source sensing, including mining blasting monitoring, ore transportation monitoring, and aggregate processing monitoring. Mining blasting monitoring employs an impact-resistant CO2 sensor array, a triaxial vibration sensor, and a laser scattering particle counter. Auxiliary modules include an active dust removal system and a temperature and humidity compensation module. Ore transportation monitoring involves installing an infrared dual-gas sensor, a load pressure sensor, a GPS positioning module, and an OBD interface power supply module on the vehicle-mounted terminal. Aggregate processing monitoring includes installing a turbine flow meter at the crusher inlet, a dustproof laser scattering particle counter at the screening machine outlet, and a pH sensor in the wastewater recovery tank. S2, Edge computing, includes data preprocessing, dynamic compensation calculation, and local storage and communication. Data preprocessing involves Kalman filtering, data normalization, and preliminary outlier detection based on the sensor data collected in step S1. Dynamic compensation calculation outputs corrected CO2 concentration in real time through a dynamic compensation algorithm, which includes compensation for particulate matter dust settling, temperature and humidity deviation, and vibration. Local storage and communication are used for local storage of carbon sequestration data and communication with the cloud platform. S3. Cloud platform processing includes blockchain notarization, carbon offset calculation, and early warning control. Blockchain notarization generates data hash values ​​in real time and synchronizes them to a distributed ledger, dynamically offsetting net emissions based on satellite remote sensing carbon data. This includes hash value storage, timestamp services, and digital signatures, with self-verification of the notarized data. Carbon offset calculation calculates and offsets the carbon absorption of vegetation and carbon emission reduction from waste rock resource utilization based on satellite data. Early warning control determines whether carbon emissions exceed standards based on monitored carbon emission data and issues corresponding early warnings according to a multi-level early warning mechanism.

2. The method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines according to claim 1, characterized in that: In step S1, the sensors are arranged in an anti-interference manner, specifically as follows: A laser scattering particulate counter is used to separate large dust particles through spiral airflow, avoiding clogging of the sampling pipeline; an integrated constant temperature control module maintains the baseline stability of the sensor under temperature fluctuations of -30℃ to 70℃, with a temperature drift error ≤ ±0.5%; a redundant CO2 sensor array is deployed, with three sets of laser sensors arranged in a 120° ring, and a majority voting mechanism is used to eliminate single-point failures; an active dust removal system automatically scrapes off attached dust every 30 minutes, maintaining a gas permeability >95%; During the blasting process, a GPS positioning module is used to synchronously record the blasting coordinates and timestamps, and vibration sensors are used to quantify the carbon emission intensity of a single blast. During transportation, the mining truck's onboard terminal obtains real-time fuel consumption E via the OBD interface. 运输 .

3. The method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines according to claim 2, characterized in that: The dynamic compensation algorithm in step S2 outputs the correction concentration value C in real time. 校正 The calculation formula is as follows: In the above formula, C 校正 The corrected CO2 concentration is in ppm. C 原始 The original CO2 concentration from the sensor is in ppm. ΔT represents the temperature deviation, calculated as measured value minus calibration value, in °C. ΔH represents the humidity deviation, calculated as measured value minus calibration value, in percentage (%). P 颗粒物 The concentration of particulate matter is μg / m³. 3 ; V 振动 The peak value of the vibration acceleration is g, and the gravitational acceleration is g. ΔE 废石 The carbon emission reduction from waste rock recycling, expressed as kg CO2 / h, is shown in the following formula: ΔE 废石 =(E 原生开采 -E 废石运输 )× Waste rock utilization rate (7) In the above formula, E 原生开采 Carbon emissions from primary ore mining, kg CO2 / ton of ore; E 废石运输 Carbon emissions from waste rock transportation: kg CO2 / ton-km; Waste rock utilization rate: the proportion of waste rock replacing primary ore: %. K1 is the basic calibration coefficient, calibrated at the sensor factory, with a value range of [0.98, 1.02]. K2 is the particulate matter compensation coefficient, ppm·m 3 / μg; K3 is the vibration compensation coefficient, in ppm / g; α, β, ε are temperature and humidity coupling coefficients, ℃⁻¹, %⁻¹.

4. The method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines according to claim 2, characterized in that: The dynamic compensation algorithm in step S2 outputs the correction concentration value C in real time. 校正 The calculation formula is as follows: C 校正 =1+αΔT+βΔHC 原始 ·K1+K2·P 颗粒物 -K3·V 振动 (3) In the above formula, C 校正 The corrected CO2 concentration is in ppm. C 原始 The original CO2 concentration from the sensor is in ppm. ΔT represents the temperature deviation, calculated as measured value minus calibration value, in °C. ΔH represents the humidity deviation, calculated as measured value minus calibration value, in percentage (%). P 颗粒物 The concentration of particulate matter is μg / m³. 3 ; V 振动 The peak value of the vibration acceleration is g, and the gravitational acceleration is g. K1 is the basic calibration coefficient, calibrated at the sensor factory, with a value range of [0.98, 1.02]. K2 is the particulate matter compensation coefficient, ppm·m 3 / μg; K3 is the vibration compensation coefficient, in ppm / g; α, β, ε are temperature and humidity coupling coefficients, ℃⁻¹, %⁻¹.

5. The method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines according to claim 3 or 4, characterized in that: K3＝0.28。 6. The method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines according to claim 5, characterized in that: Step S3 includes the following steps: S3.1 Blockchain traceability and evidence storage on the cloud platform. The blockchain evidence storage adopts dual-chain storage. The dual-chain storage process is as follows: the main chain generates a data packet hash value every 5 seconds and synchronizes it to the judicial node; the side chain stores the encrypted original data packet and supports decryption authorized by the judicial institution; after storage, evidence storage self-verification is performed. S3.2 Dynamic carbon offsetting utilizes access to satellite data, including NDVI index acquisition and vegetation cover analysis. The carbon sink calculation model is as follows: S 碳汇 =NDVI×A×ρ×γ (4) Among them, S 碳汇 The carbon sequestration capacity of vegetation is expressed in kg CO2 / h. NDVI is the normalized vegetation index, [0,1]; A represents the vegetation cover area, in meters. 2 ; ρ is the vegetation density coefficient, i.e., biomass per unit area, kg / m². 2 ; γ is the carbon conversion efficiency coefficient, i.e. CO2 fixation rate, kg CO2 / kg biomass; Satellite remote sensing data is used to retrieve vegetation carbon sink S using the NDVI index. 碳汇 The net carbon emission value Cnet is dynamically adjusted, as shown in the following formula: C 净排放 =C 校正 -S 碳汇 ·or 吸收率 (5) Where η 吸收率 The vegetation CO2 absorption rate, with a value range of [0,1]; C 净排放 Net carbon emissions after deduction, kg CO2 / h; η 吸收率 Dynamic matching based on vegetation type in the mining area; S3.3 Early Warning Control employs a multi-level early warning mechanism for multi-level early warning response, as follows: Level 1 warning: When carbon emissions during the processing stage exceed the threshold by 20%, an audible and visual alarm will be triggered. Level 2 warning: When the standard is exceeded for 3 consecutive times or the change rate is >10% / min, the feed speed of the crusher will be automatically reduced and the dust removal and pressurization will be activated. Level 3 warning: When carbon emissions during transportation exceed the threshold by 50%, the mining truck engine is locked and an emission reduction plan is sent to the dispatch center.

7. The method for real-time monitoring and measurement of carbon emissions from sand and gravel aggregate mines according to claim 6, characterized in that: Step S3.1, the self-verification of evidence storage, includes the following steps: S3.1.1 Abnormal data identification: When the data is abnormal, such as temperature and humidity deviation ΔT>5℃ or ΔH>10%, self-calibration is automatically started and the process proceeds to step S3.1.

2. Algorithms for anomaly identification include Isolation Forest and Local Anomaly Factor, as follows: Isolation Forest: Quickly locates outliers by randomly segmenting the feature space; Local anomaly factor: Calculates the local density deviation of data points to identify low-density anomalies; S3.1.2 Redundancy verification, wherein the redundancy verification is to enable the three-sensor ring array to perform a two-out-of-three voting; S3.1.3 Data Reconstruction, Performing Data... 重构 The calculation is as follows: Data 重构 α·SensorA +β·SensorB +γ·Satellite; (8) In the formula, Data 重构 The data is the reconstructed effective data, kg CO2 / h; Sensor A / B are the redundant sensor measurements, kg CO2 / h; Satellite is the satellite-retrieved carbon emission reference value, kg CO2 / h; α, β are sensor weighting coefficients; γ is the satellite data weighting coefficient; S3.1.4 Blockchain notarization encrypts the reconstructed data to generate a new hash value, which is then synchronously updated to the main chain and side chain.

8. A real-time carbon emission monitoring device for sand and gravel aggregate mines, characterized in that: include: The sensing layer is used for monitoring mining blasting, ore transportation, and aggregate processing. Mining blasting monitoring employs an impact-resistant CO2 sensor array, a triaxial vibration sensor, and a laser-scattering particulate counter. Auxiliary modules include an active dust removal system and a temperature and humidity compensation module. Ore transportation monitoring involves installing an infrared dual-gas sensor, a load pressure sensor, a GPS positioning module, and an OBD interface power supply module on the vehicle-mounted terminal. Aggregate processing monitoring includes installing a turbine flow meter at the crusher inlet, a dustproof laser-scattering particulate counter at the screening machine outlet, and a pH sensor in the wastewater recovery tank. The edge computing layer is used for data preprocessing, dynamic compensation calculation, and local storage and communication. Data preprocessing involves Kalman filtering, data normalization, and preliminary outlier detection based on the sensor data collected in step S1. Dynamic compensation calculation outputs corrected CO2 concentration in real time through a dynamic compensation algorithm, which includes compensation for particulate matter dust settling, temperature and humidity deviation, and vibration. Local storage and communication are used for local storage of carbon sequestration data and communication with the cloud platform. The cloud platform is used for blockchain-based evidence storage, carbon offset calculation, and early warning control. Blockchain evidence storage involves generating data hash values ​​in real time and synchronizing them to a distributed ledger. It dynamically offsets net emissions by combining satellite remote sensing carbon data. Specifically, this includes hash value storage, timestamp services, and digital signatures, and performs self-verification of the evidence. Carbon offset calculation involves calculating and offsetting carbon absorption by vegetation and carbon emission reduction through waste rock resource utilization based on satellite data. Early warning control determines whether carbon emissions exceed standards based on monitored carbon emission data and issues corresponding warnings according to a multi-level early warning mechanism.

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