A multi-source fusion dynamic dry basis net weight machine self-certification method based on a biochar type channel, an edge control cabinet, a system, electronic equipment and a storage medium

Abstract

The application discloses a multi-source fusion dynamic dry basis net weight machine self-verification method based on a biochar type channel, an edge control cabinet, a system, electronic equipment and a storage medium. The method comprises the following steps: acquiring or generating data share identification and biochar type channel records; acquiring wet weight evidence data and dry basis mass correction evidence data; generating and verifying dry basis mass candidate values; performing consistency verification, anomaly identification or fusion estimation in the corresponding channel to obtain a dynamic dry basis net weight result; generating a degradation processing record under abnormal conditions; and generating a dynamic dry basis net weight self-verification object and outputting. Therefore, the traceability, verifiability and anomaly processing consistency of the dry basis net weight result are improved.

Description

Technical Field

[0001] This invention relates to the field of digital measurement, reporting and verification (dMRV) of carbon removal, and particularly to a multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type and channel, executed by a carbon removal dMRV data processing system, as well as an edge control cabinet, system, electronic device and non-transitory computer-readable storage medium for implementing the method.

[0002] This invention further relates to source-side data acquisition, wet weight measurement, moisture deduction, sampling calibration, operating condition attribution, equipment dynamic uncertainty handling, anomaly downgrade handling, generation of monitoring cycle verification evidence packages, and data share status updates in the biochar preparation process by biomass pyrolysis. Background Technology

[0003] As a carbon removal product, biochar is typically used in the digital measurement, reporting, and verification (dMRV) process of carbon removal projects. This process usually requires recording and verification of biochar yield, moisture content, dry weight, detection parameters, operating conditions, and final status. Dry weight is crucial foundational data for subsequent carbon removal calculations, parameter write-back, uncertainty propagation, generation of monitoring cycle evidence packages, and updates to asset share status.

[0004] In existing technologies, biochar production yield can typically be obtained by using weighing sensors, scales, packaging weighing devices, silo weighing devices, or discharge flow meters to obtain wet weight data, which is then converted by deducting moisture based on moisture detection results. However, in continuous production, continuous discharge, or multi-batch mixing production scenarios, the moisture state of biochar is affected by factors such as raw material composition, pyrolysis conditions, discharge time window, spraying or cooling methods, inventory exposure, re-moistening, packaging unit circulation, and the representativeness of test samples. A single wet weight deduction path is insufficient to reliably express the true dry basis net weight for different output proportions.

[0005] Existing technologies also suffer from issues such as inconsistencies between online moisture detection data and sampled moisture data, the online moisture meter being affected by dust obstruction or abnormal material layers, fluctuations or missing weighing data, communication data interruptions, equipment calibration nearing or drifting, and abnormal operating condition assignment windows. These issues may force the verification system to use overly conservative deductions, complete exclusions, or manual review during audits, reducing the utilization rate of verifiable data.

[0006] On the other hand, biochar production share does not naturally have clear batch boundaries. If different raw material compositions, different pyrolysis conditions, different moisture behaviors, or different representative biochar production shares are incorrectly subjected to the same moisture parameter, historical deviation distribution, or degradation rules, it will lead to untraceable or unverifiable dry basis net weight results.

[0007] Therefore, there is an urgent need for a technical solution for carbon removal production processes that can use data share identifiers and biochar type channel records as basic indexes to perform multi-source fusion, verification, or correction of wet weight evidence data and dry weight quality correction evidence data within the corresponding biochar type channel, and generate traceable degradation processing records under abnormal conditions, thereby forming a dynamic dry weight self-certification object that can be written into the monitoring cycle verification evidence package, data share state machine, or readable output record. Summary of the Invention

[0008] This invention aims to address the challenges of dynamically, systematically, traceably, and machine-verified dry basis net weight in biochar carbon removal production processes. Specifically, this invention addresses at least one of the following issues: a single wet weight deduction result cannot adequately represent the dynamic moisture state; parameter write-back errors exist between different biochar types; online moisture detection, sampling calibration, operating condition attribution, and equipment status are difficult to integrate into dry basis quality assessment; structured degradation processing records are lacking under abnormal data conditions; and dry basis net weight results are difficult to interface stably with monitoring cycle verification evidence packages, data share state machines, or readable output records.

[0009] To address the aforementioned technical problems, this invention provides a multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling, executed by a carbon removal digital measurement, reporting, and verification (dMRV) data processing system. The carbon removal (dMRV) data processing system may include one or more computing nodes, which may be source-side computing nodes, edge computing gateways, edge control cabinets, local servers, cloud servers, industrial control computers, embedded processors, or combinations thereof.

[0010] A multi-source fusion dynamic dry basis net weight machine self-verification method based on biochar type channeling, executed by a dMRV data processing system, is characterized in that the dMRV data processing system includes one or more computing nodes, and the method includes: S1, share identification and channel determination: acquiring or generating a data share identifier and a biochar type channel record corresponding to the biochar output share, wherein the biochar type channel record is used to characterize whether the corresponding biochar output share shares share the same dry basis quality processing channel; S2, dry basis quality evidence acquisition: acquiring or receiving the wet weight related to the biochar output share. Evidence data and dry basis quality correction evidence data, wherein the wet weight evidence data is used as a quality benchmark to characterize the biochar production share; S3, candidate value generation and verification correction: based on the wet weight evidence data and the dry basis quality correction evidence data, a first dry basis quality candidate value is generated according to the wet weight deduction path, and a second dry basis quality candidate value is generated based on at least one other dry basis quality generation or verification path, or the dry basis quality candidate value is verified or corrected; S4, channel-specific verification and fusion: based on the biochar type channel record, within the corresponding biochar type channel, the first dry basis quality candidate value is verified and corrected. The selected value, the second dry basis quality candidate value, the verification result or correction result are subjected to consistency verification, quality status assessment, anomaly identification or fusion estimation to obtain dynamic dry basis net weight result and its result status data; S5, anomaly downgrading and dynamic update: when the data related to the biochar production share meets the anomaly conditions, a downgrading processing record is generated, and based on at least one of statistical confidence constraints, physical conservation constraints, equipment dynamic uncertainty status, sampling calibration data, and historical deviation data of the same biochar type channel, a downgraded dry basis quality candidate value or conservative adjustment status is determined; and based on the downgrading processing Changes in the recorded result status data or the dry basis quality correction evidence data are used to update, freeze, or mark the dynamic dry basis net weight result as pending verification; S6, Self-certification object generation and output: A dynamic dry basis net weight self-certification object is generated, which includes the data share identifier, the biochar type channel record, and the dynamic dry basis net weight result, and is associated with or points to the result status data or the downgrade processing record; The dynamic dry basis net weight self-certification object is written to, associated with, or output to the monitoring cycle verification evidence package, data share state machine, or readable output record.

[0011] In some implementations, biochar type channel records are used to characterize whether corresponding biochar production shares share the same dry basis quality processing channel. The dry basis quality processing channel may be associated with raw material boundaries, operating condition boundaries, production time windows, moisture parameter channels, detection parameter write-back channels, representative samples or representative test reports, historical deviation distribution within the channel, channel-specific uncertainty status, downgrade processing rules, or parameter write-back status.

[0012] In some implementations, wet weight evidence data serves as the quality benchmark, while dry weight quality correction evidence data is used for moisture deduction, sampling calibration, soft measurement, model estimation, mass conservation or heat balance verification, equipment dynamic uncertainty correction, operating condition attribution correction, anomaly identification, or downgrade processing.

[0013] In some embodiments, the other dry basis quality generation or verification paths are selected from: wet weight deduction path, online moisture path based on online moisture detection, sampling calibration path based on sampling calibration data, feed dry basis soft measurement path based on feed dry basis estimation data and operating condition attribution data, char yield model path based on char yield model parameters and pyrolysis operating condition data, mass conservation path based on inventory increases / decreases and packaging unit turnover, or heat balance path based on heat balance constraints. These paths are used to generate a second dry basis quality candidate value, or to verify or correct the first dry basis quality candidate value.

[0014] In some implementations, abnormal degradation processing includes generating a degradation processing record, which may include fields such as degradation rollback path type identifier, statistical or physical conservative benchmark, degradation dry basis quality candidate value, conservative adjustment status, manual review mark, version snapshot hash or evidence chain identifier.

[0015] In some embodiments, the present invention also provides an edge control cabinet for machine self-certification of dynamic dry basis net weight of biochar, the edge control cabinet including an industrial data interface module, a trusted time module, a signature module, a processing module and a communication module.

[0016] In some embodiments, the present invention also provides a multi-source fusion dynamic dry basis net weight machine self-certification system based on biochar type and channel, including source-side data acquisition equipment, edge computing device, carbon removal digital measurement, reporting and verification data processing server, and status management module.

[0017] In some embodiments, the present invention also provides an electronic device and a non-transitory computer-readable storage medium for implementing the above-described methods.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: First, by using data share identifiers and biochar type channel records, the dynamic dry basis net weight calculation is limited to the corresponding biochar type channel, avoiding incorrect parameter write-back between different raw materials, different operating conditions, different test representativeness, or different equipment uncertainty states.

[0019] Second, by fusing, verifying, or correcting multi-source evidence data of wet weight and dry weight quality correction evidence data, verification paths such as online moisture content, sampling calibration, soft measurement, char yield model, mass conservation, or heat balance are introduced while retaining the wet weight physical quality benchmark, thereby improving the verifiability of dynamic dry weight net weight results.

[0020] Third, under abnormal conditions such as weighing, moisture detection, communication, equipment status, operating condition attribution, inventory exposure, spray cooling, damping, or manual verification, the downgrade processing record and conservative adjustment status can achieve mechanized downgrade rollback processing, reducing excessive manual judgment and complete elimination.

[0021] Fourth, by using the dynamic dry basis net weight self-certification object, the data share identifier, biochar type channel record, dynamic dry basis net weight result, result status data and downgrade processing record are associated or linked, and written into the monitoring cycle verification evidence package, data share status machine or readable output record, thereby forming machine-readable, traceable and auditable verification evidence.

[0022] Fifth, by combining edge control cabinets, PLC / DCS / IPC interfaces, trusted time modules, signature modules, and communication modules, a trusted evidence entry point can be formed at the source-side production site, improving the authenticity, integrity, and non-repudiation of industrial production data in carbon removal verification. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall structure of the dMRV data processing system provided in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the process for a multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type and channel provided in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram illustrating the relationship between biochar type channel records, data share identifiers, and output shares provided in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the process for generating, verifying, correcting, and merging candidate values ​​for dry basis quality provided in an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram of the abnormal degradation processing record generation and dynamic update process provided in an embodiment of the present invention.

[0028] Figure 6 This is a schematic diagram of the interface for the dynamic dry basis net weight self-certification object and monitoring cycle verification evidence package provided in an embodiment of the present invention.

[0029] Figure 7This is a schematic diagram of the edge control cabinet structure provided in an embodiment of the present invention.

[0030] Figure 8 This is a schematic diagram of an electronic device or computing node structure provided in an embodiment of the present invention.

[0031] Figure reference numerals: 100-dMRV data processing system; 110-source-side data acquisition equipment; 120-edge computing device or edge control cabinet; 121-industrial data interface module; 122-trusted time module; 123-signature module; 124-processing module; 125-communication module; 130-dMRV data processing server; 140-state management module; 150-readable output record generation module; 200-data share identifier; 210-biochar type channel record; 220-wet weight evidence data; 230-dry basis quality correction evidence data; 240-dry basis quality candidate value; 250-dynamic dry basis net weight result; 260-downgraded processing record; 270-dynamic dry basis net weight self-certification object; 300-monitoring cycle verification evidence package; 310-data share state machine; 400-electronic equipment. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the following embodiments are used to explain the present invention, and not to limit the scope of protection of the present invention. For those skilled in the art, various substitutions, combinations or modifications can be made to the following embodiments without departing from the concept of the present invention.

[0033] In this invention, the "digital carbon removal measurement, reporting, and verification data processing system" can be referred to as the dMRV data processing system. The dMRV data processing system can consist of one or more computing nodes. These computing nodes may include source-side computing nodes, edge control cabinets, edge computing gateways, local servers, cloud servers, industrial control computers, embedded processors, or combinations thereof.

[0034] In this invention, "biochar production share" can refer to the portion of physical biochar produced that is anchored by a data share identifier during continuous production, intermittent production, packaging, storage, discharge, warehousing, delivery, transfer, or verification processes. The biochar production share can correspond to one or more packaging units, discharge time windows, weighing time windows, storage units, sample representativeness intervals, or data share identifiers.

[0035] In this invention, the “data share identifier” is used to characterize or anchor the biochar production share, the status of evidence objects, the dry basis quality results, the abnormal degradation record, the status of the monitoring cycle evidence package, or the state unit in the data share state machine.

[0036] In this invention, the "Biochar Type Channel Record" is used to characterize whether one or more biochar production shares share the same dry basis quality processing channel. The dry basis quality processing channel is not a general commodity classification, but rather a data processing channel used to control the generation, verification, correction, quality status assessment, anomaly identification, parameter write-back, historical deviation recall, and the application of degradation rules for dry basis quality candidate values.

[0037] In this invention, "dynamic dry basis net weight" can refer to a verifiable dry basis quality result dynamically determined based on wet weight evidence data, combined with moisture evidence, sampling calibration, equipment dynamic uncertainty status, anomaly downgrade handling, or verification rules. The dynamic dry basis net weight can be updated, frozen, or marked as pending verification as dry basis quality correction evidence data, calibration data, anomaly status, or downgrade handling records change.

[0038] In some implementations, if two biochar production shares meet preset common conditions in terms of raw material composition, raw material source boundary, furnace input, pyrolysis conditions, production time window, moisture state, representativeness of test reports, equipment uncertainty or historical deviation distribution, they can be classified into the same biochar type channel; otherwise, the system can split, update, merge or freeze the corresponding biochar type channel records.

[0039] Example 1: Overall Method Flow Example like Figure 2 As shown, the dMRV data processing system 100 executes a multi-source fusion dynamic dry basis net weight machine self-verification method based on biochar type and channel. This method includes S1 to S6.

[0040] In S1, the system acquires or generates a data share identifier 200 and a biochar type channel record 210 corresponding to the biochar production share. The data share identifier 200 can be generated from at least one of the following: discharge time window, packaging unit number, weighing event number, production unit number, inventory unit number, source-side evidence object number, or monitoring cycle number.

[0041] In S2, the system acquires or receives wet weight evidence data 220 and dry basis quality correction evidence data 230. Wet weight evidence data 220 can come from weighing sensors, scales, packaging weighing equipment, discharge flow meters, silo weighing equipment, etc. Dry basis quality correction evidence data 230 can come from moisture detection equipment, sampling records, laboratory moisture reports, raw material moisture content estimates, char production model parameters, environmental exposure records, spray or cooling event records, inventory status, equipment dynamic uncertainty status, or operating condition attribution records.

[0042] In S3, the system generates a first dry basis mass candidate value 240 based on wet weight evidence data 220 and dry basis mass correction evidence data 230. The generation of the first dry basis mass candidate value can include generation according to the wet weight deduction path. For example, the system can generate a candidate dry basis mass M_dry_candidate=M_wet based on wet weight data M_wet and moisture ratio W. (1-W). This expression is for illustrative purposes only; the actual implementation can be modified based on the definition of moisture, data units, equipment calibration status, or rule configuration.

[0043] In S3, the system can also generate a second candidate dry basis quality value based on at least one other dry basis quality generation or verification path, or verify or correct the first candidate dry basis quality value. The other dry basis quality generation or verification path is selected from: a wet weight deduction path, an online moisture path based on online moisture detection, a sampling calibration path based on sampling calibration data, a soft measurement path for feed dry basis based on feed dry basis estimation data and operating condition attribution data, a char yield model path based on char yield model parameters and pyrolysis operating condition data, a mass conservation path based on inventory increases / decreases and packaging unit turnover, or a heat balance path based on heat balance constraints.

[0044] In S4, the system performs consistency verification, quality status assessment, anomaly identification, or fusion estimation on the first dry basis quality candidate value, the second dry basis quality candidate value, the verification result, or the correction result within the corresponding biochar type channel based on the biochar type channel, to obtain the dynamic dry basis net weight result 250 and its result status data.

[0045] In S5, if the data related to biochar production share meets the abnormal conditions, the system generates a downgrade processing record 260 and determines a candidate value for downgraded dry basis quality or a conservative adjustment state based on at least one of statistical confidence constraints, physical conservation constraints, equipment dynamic uncertainty status, sampling calibration data, and historical deviation data of the same biochar type channel. The system can also update, freeze, or mark the dynamic dry basis net weight result as pending review based on changes in the downgrade processing record, result status data, or dry basis quality correction evidence data.

[0046] In S6, the system generates a dynamic dry basis net weight self-certification object 270. The dynamic dry basis net weight self-certification object 270 includes a data share identifier 200, a biochar type channel record 210, and a dynamic dry basis net weight result 250, and is associated with or points to result status data or a downgrade processing record 260. The system writes, associates, or outputs the dynamic dry basis net weight self-certification object 270 to the monitoring cycle verification evidence package 300, the data share state machine 310, or a readable output record.

[0047] Example 2: Biochar-type channel recording example The biochar type channel record 210 may include at least one of the following: biochar type identifier, associated data share identifier set, raw material boundary, operating condition boundary, production time window, moisture parameter channel, detection parameter write-back channel, representative sample identifier, representative test report identifier, historical deviation distribution within the channel, channel specific uncertainty status, downgrade processing rules, parameter write-back status, version snapshot hash, previous status hash, or evidence chain identifier.

[0048] Biochar type identifiers can be generated from one or more fields, including raw material type, raw material source, raw material composition, pyrolysis temperature range, residence time range, reactor number, production time window, cooling method, moisture status, representative detection status, or channel version number.

[0049] The associated data share identifier set is used to record a list of data share identifiers belonging to the same biochar type channel. The raw material boundary can record information such as raw material source, batch number, furnace affiliation, composition, moisture content, and qualification status. The operating condition boundary can record pyrolysis temperature, residence time, feed rate, discharge rate, reactor status, abnormal operating condition windows, or operating condition affiliation data.

[0050] The moisture parameter channel indicates the sources of moisture parameters that can be shared within this biochar type channel, or the historical distribution of moisture deviations. The test parameter write-back channel indicates whether test reports, sampling calibration results, representative sample or laboratory moisture reports can be written back to this channel.

[0051] The system can generate, update, split, merge, or freeze biochar type channel records based on raw material source data, raw material composition data, furnace input data, pyrolysis condition data, discharge time window, moisture detection data, sampling calibration data, and representative or abnormal conditions in the test report.

[0052] For example, when the first half of the output in the same monitoring period is cooled naturally and the second half is cooled by spraying, and there is a significant difference in the moisture distribution, the system can split the original biochar type channel into two channels and generate historical deviation distribution and downgrade treatment rules for each channel.

[0053] Example 3: Example of wet weight evidence data and dry basis quality correction evidence data The wet weight evidence data 220 includes at least one of the following: weighing sensor data, weighbridge data, packaging weighing data, discharge flow meter data, silo weighing data, weighing time window, weighing equipment calibration status, and weighing equipment data quality status.

[0054] Wet weight evidence data can carry the weighing equipment identifier, weighing timestamp, weighing value, weighing unit, weighing window start time, weighing window end time, equipment calibration certificate number, calibration validity period, weighing data quality status, data signature, hash value or previous status hash.

[0055] The dry basis quality correction evidence data 230 includes at least one of the following: moisture evidence data, sampling calibration data, feed dry basis estimation data, char yield model parameters, environmental exposure event data, spray or cooling event data, inventory status data, equipment dynamic uncertainty status data, and operating condition attribution data.

[0056] Moisture evidence data can include online moisture detection data, near-infrared moisture detection data, microwave moisture detection data, moisture data estimated by images or spectra, sampling moisture detection data, laboratory moisture report data, historical moisture distribution data of the same biochar type channel, etc.

[0057] The sampling calibration data may include the sample number, sampling time, sampling location, sample sealing status, laboratory report number, detection method, moisture result, sampling deviation, representativeness status, report hash or electronic signature verification status.

[0058] Equipment dynamic uncertainty status data may include equipment identification, initial uncertainty, most recent calibration time, cumulative running time, cumulative material throughput, dust status, humidity status, vibration status, drift status, dynamic measurement uncertainty, or equipment data availability status.

[0059] Example 4: Dry Basis Quality Generation or Verification Path Example The wet weight deduction path generates dry basis quality candidate values ​​based on wet weight evidence data and moisture content evidence data. If the wet weight is M_wet and the moisture content is W, then M_dry_candidate=M_wet can be generated. (1-W). This path can be used as a basis for generating candidate values.

[0060] The online moisture path generates another candidate dry basis mass value based on online moisture detection data, wet weight evidence data, or weighing time windows. For example, the online moisture meter outputs a moisture sequence at a frequency of minutes. The system weights and aggregates this moisture sequence according to the weighing time window, and then combines it with the wet weight data to generate another candidate dry basis mass value.

[0061] The sampling calibration path verifies or corrects candidate values ​​for dry basis quality based on sampling moisture data, laboratory moisture report data, or sampling deviation data. For example, when there is a stable deviation between the online moisture detection mean and the sampling moisture result, the system corrects the candidate values ​​under the online moisture path according to the sampling deviation, or marks the online moisture path as degraded for use.

[0062] The feed dry basis soft measurement path generates another candidate dry basis mass value based on the feed dry basis estimation data, feed moisture content, feed mass, or operating condition attribution data. For example, the system estimates the theoretically produced biochar dry basis mass based on the feed mass, feed moisture content, and operating condition attribution data, and uses it as a reference candidate value for comparison with the wet weight deduction water candidate value.

[0063] The char yield model path generates another candidate or reference value for dry basis quality based on char yield model parameters, pyrolysis temperature, residence time, feed rate, discharge rate, or historical data from similar operating conditions. The char yield model can be a rule-based model, regression model, mechanistic model, machine learning model, or empirical calibration model.

[0064] The mass conservation path or heat balance path verifies or corrects candidate dry basis mass values ​​based on inventory increases / decreases, packaging unit turnover, incoming mass, outgoing mass, and mass conservation or heat balance constraints. For example, the system can determine whether candidate dry basis masses exceed the physically closed range within a certain time window by considering total incoming and outgoing volumes and inventory changes.

[0065] Example 5: Channel-by-Channel Verification Fusion Example The system calculates the difference between candidate dry-basis quality values, another candidate dry-basis quality value, and verification or correction results within the same biochar type channel. The difference can be absolute difference, relative difference, standardized difference, weighted error, confidence interval overlap, or model residual.

[0066] The system can determine fusion weights, selection rules, conservative value rules, or dynamic thresholds based on candidate value source type, equipment dynamic uncertainty status, sampling and calibration results, operating condition attribution status, data integrity status, historical deviation distribution, or biochar type channel status.

[0067] For example, if the wet weight deduction path and the sampling calibration path differ only slightly, and both the weighing equipment and the moisture detection equipment are available, the system can generate a dynamic dry basis net weight result according to the preset fusion weights. If the online moisture path is affected by dust obstruction, the system can reduce the weight of the online moisture path and increase the weight of the sampling calibration path or the mass conservation path.

[0068] When the difference exceeds a preset threshold or a dynamic threshold determined based on the device's dynamic uncertainty state, the system generates a candidate value conflict state, a manual review state, a downgraded use state, or a write-back rejection state.

[0069] Quality status assessment can include confidence status assessment, data integrity assessment, data availability assessment, equipment status assessment, anomaly level assessment, channel representativeness assessment, or manual review requirement assessment. Result status data can include statuses such as normal, available, degraded use, conservative write-back, pending review, write-back rejected, version frozen, data gap, or anomaly conflict.

[0070] Example 6: Abnormal Conditions and Degradation Handling Example Abnormal conditions include missing, fluctuating, repeated, out-of-range, or low-confidence weighing data; fluctuating, continuously low-confidence, dust obstruction, abnormal material layer influence, deviation from sampled moisture data or deviation from soft measurement results on feed dry basis; sensor drift, calibration failure, calibration nearing expiration, equipment dynamic uncertainty exceeding the threshold; communication interruption, missing data packets, abnormal timestamps, abnormal signatures; and at least one of the following: spraying event, cooling event, inventory exposure event, re-dampening event, mixing event, or manual verification triggering event.

[0071] When abnormal conditions are met, the system generates a degradation processing record 260. The degradation processing record 260 may include a degradation record identifier, a set of affected data share identifiers, an affected biochar type identifier, an abnormal signal type, a degradation rollback path type identifier, a statistical or physical conservative benchmark, a candidate value for degradation dry basis quality, a conservative adjustment status, result status data, a manual review mark, a previous status hash, a version snapshot hash, or an evidence chain identifier.

[0072] The degradation fallback path type identifier is used to characterize which degradation path the system switches to from the normal path. For example, when the online moisture content is abnormal, it can switch to the sampling calibration path; when the weighing data has low confidence, it can switch to the mass conservation path; when the equipment dynamic uncertainty is too high, it can switch to the conservative value rule; when the operating condition is abnormal, it can switch to the manual review state or the version freeze state.

[0073] Statistical confidence constraints may include lower limits of confidence intervals, historical deviation distributions, conservative quantile values, expanded standard deviations, or conservative corrections based on sample size. Physical conservation constraints may include mass conservation constraints, inventory closure constraints, packaging unit flow constraints, inbound / outbound closure constraints, or heat balance constraints.

[0074] The system updates, freezes, or marks the dynamic dry basis net weight result as pending review based on changes in downgrade processing records, result status data, or dry basis quality correction evidence data. For example, after supplementing communication data or adding sampling calibration data, the system can re-evaluate the dynamic dry basis net weight result; when the anomaly cannot be eliminated, the system can freeze the result and mark it as pending manual review.

[0075] Table 1 shows several examples of degrade rollback decision trees. The degrade rollback path type identifiers in Table 1 are only examples. The system can determine other semantically equivalent degrade rollback path types based on the abnormal signal type, data quality status, biochar type channel record, historical deviation distribution within the channel, sampling calibration data, equipment dynamic uncertainty status, or manual review results.

[0076] Table 1 Example of a Degradation / Rollback Decision Tree Example 7: Example of Self-Verification Object and Evidence Package for Dynamic Dry Basis Net Weight The dynamic dry basis net weight self-certification object 270 may include at least one of the following: data share identifier, biochar type channel record, dynamic dry basis net weight result, result status data, downgrade processing record, evidence chain identifier, version snapshot hash, previous status hash, generation timestamp, computing node identifier, signature value, or output target identifier.

[0077] Readable output records may include at least one of the following: text reports, tabular records, electronic documents, audit export files, display interface records, printout records, verification summary records, and versioned report files.

[0078] The monitoring cycle verification evidence package 300 may include monitoring cycle identifier, associated facility audit evidence package identifier, data share identifier sequence, dynamic dry basis net weight summary data, downgrade processing record, uncertainty propagation record, detection parameter write-back record or version snapshot hash.

[0079] When the system writes the dynamic dry basis net weight self-certification object into the monitoring cycle verification evidence package, it can simultaneously update the state variables in the data share state machine 310. The state variables can include states such as eligible for inclusion, downgraded inclusion, adjusted inclusion, pending supplementary certification, pending review, frozen, excluded, rejected write-back, or version frozen.

[0080] Example 8: PLC / DCS / IPC Industrial Control Data Source Example Wet weight evidence data, dry basis quality correction evidence data, weighing data, equipment status data, or operating condition attribution data are at least partially collected, generated, or forwarded in real time by programmable logic controllers (PLCs), distributed control systems (DCSs), or industrial control computers (IPCs) in the pyrolysis facility control system, and are associated with data share identifiers or biochar type channel records.

[0081] PLCs can collect or forward data from weighing sensors, conveyor status, discharge valve status, spray valve status, reactor temperature, feed rate, and discharge rate. DCSs can collect or forward data on multiple reactor operating conditions, temperature profiles, pressure status, equipment operating status, and alarm status. IPCs can perform industrial protocol conversion, data caching, edge computing, hash processing, signature processing, or communicate with cloud servers.

[0082] Industrial control data can be accessed by the edge control cabinet via OPC UA, Modbus, Profinet, EtherNet / IP, serial communication, industrial Ethernet, message queues, or local database interfaces. The above communication protocols are examples; other industrial interfaces capable of accessing source-side physical signals can also be used.

[0083] Example 9: Edge Control Cabinet Example like Figure 7 As shown, in some embodiments, the edge control cabinet 120 for machine self-certification of dynamic dry basis net weight of biochar includes an industrial data interface module 121, a trusted time module 122, a signature module 123, a processing module 124, and a communication module 125.

[0084] The industrial data interface module 121 is used to receive source-side physical signals from weighing equipment, moisture detection equipment, environmental sensors, inventory management equipment, PLC, DCS, or IPC. The source-side physical signals may include wet weight signals, moisture signals, ambient temperature and humidity signals, inventory status signals, spray cooling event signals, operating condition signals, equipment calibration status signals, or communication status signals.

[0085] The trusted time module 122 is used to time-bind source-side physical signals, wet weight evidence data, dry basis quality correction evidence data, or dry basis quality-related evidence objects. The trusted time module may include a local clock, a satellite time synchronization module, a network time synchronization module, a hardware security module time source, or a combination thereof.

[0086] The signature module 123 is used to perform signature processing, hash processing, or pre-hash processing on source-side physical signals, wet weight evidence data, dry basis quality correction evidence data, or dry basis quality related evidence objects. The signature module may include a hardware security module, a trusted execution environment, a key storage module, a signature algorithm module, or a hash chain generation module.

[0087] In specific industrial implementation scenarios, the signature module 123 may include at least one of a security chip, a Trusted Platform Module (TPM), a Hardware Security Module (HSM), an encryption card, a Trusted Execution Environment (TEA), or a key-isolated storage module. The signature module 123 may support SM2, SM3, ECDSA, SHA series algorithms, or other digital signature and digest calculation algorithms. After the industrial data interface module 121 acquires wet weight physical signals, moisture detection signals, equipment status signals, or operating condition signals, the processing module 124 can combine the source-side physical signals or the data segments generated therefrom with the timestamp, device identifier, data share identifier, or preceding state hash output by the trusted time module 122, and then call the signature module 123 to perform hash processing, pre-hash processing, or digital signature processing. The private key or key material used for signing can be stored in a physically or logically isolated secure storage area to reduce the risk of software-layer forgery, replay, or unauthorized rewriting of source-side evidence data.

[0088] The processing module 124 is used to acquire or generate data share identifiers and biochar type channel records corresponding to the biochar production share, and generate wet weight evidence data, dry basis quality correction evidence data or dry basis quality related evidence objects associated with the data share identifiers or biochar type channel records based on the source-side physical signals.

[0089] The processing module 124 is also used to generate candidate values, verification results or correction results of dry basis quality in the corresponding biochar type channel based on wet weight evidence data and dry basis quality correction evidence data, and to perform consistency verification or fusion estimation.

[0090] The processing module 124 is also used to generate candidate value conflict status, degradation trigger flag, quality status assessment result or local verification pass status locally in the edge control cabinet, and output them to local storage, cloud server, monitoring cycle verification evidence package interface, data share state machine interface or readable output record through the communication module 125.

[0091] The communication module 125 is used to output wet weight evidence data, dry basis quality correction evidence data, dry basis quality related evidence objects, dry basis quality candidate values, verification results or correction results to local storage, cloud server, monitoring cycle verification evidence package interface, data share state machine interface or readable output records.

[0092] Example 10: System Implementation (corresponding to claim 12) like Figure 1 As shown, in some embodiments, the multi-source fusion dynamic dry basis net weight machine self-certification system based on biochar type and channel includes a source-side data acquisition device 110, an edge computing device 120, a dMRV data processing server 130, and a status management module 140.

[0093] The source-side data acquisition device 110 is used to collect wet weight data, moisture detection data, environmental exposure event data, spray or cooling event data, inventory status data, equipment status data, or operating condition data. The source-side data acquisition device 110 may include load cells, weighbridges, packaging weighing equipment, discharge flow meters, silo weighing equipment, online moisture meters, near-infrared moisture meters, microwave moisture meters, environmental sensors, inventory management equipment, PLCs, DCSs, or IPCs.

[0094] Edge computing device 120 is communicatively connected to source-side data acquisition device 110 and is used to perform time binding, data quality judgment, anomaly identification, hash processing, pre-hash processing or signature processing on wet weight data, moisture detection data, environmental exposure event data, spray or cooling event data, inventory status data, equipment status data or operating condition data, and generate wet weight evidence data, dry basis quality correction evidence data or dry basis quality related evidence objects.

[0095] The dMRV data processing server 130 is used to acquire or receive wet weight evidence data, dry basis quality correction evidence data, data share identifiers, and biochar type channel records. Based on the wet weight evidence data and the dry basis quality correction evidence data, it generates a first dry basis quality candidate value, a second dry basis quality candidate value, a verification result, or a correction result. In the corresponding biochar type channel, it performs consistency verification, quality status assessment, anomaly identification, or fusion estimation on the first dry basis quality candidate value, the second dry basis quality candidate value, the verification result, or the correction result to generate a dynamic dry basis net weight result.

[0096] The status management module 140 is used to generate dynamic dry basis net weight self-certification objects and write, associate, or output the dynamic dry basis net weight self-certification objects to the monitoring cycle verification evidence package, data share state machine, or readable output record.

[0097] The system also includes a readable output record generation module 150. The readable output record generation module 150 is used to generate text reports, tabular records, electronic documents, audit export files, display interface records, printout records, verification summary records, or versioned report files based on at least one of the dynamic dry basis net weight self-certification object, dynamic dry basis net weight result, result status data, downgrade processing records, monitoring cycle verification evidence packages, or data share state machines.

[0098] Example 11: Example of an electronic device and storage medium like Figure 8 As shown, in some embodiments, the electronic device 400 includes a processor and a memory. The memory stores a computer program that, when executed by the processor, causes the processor to perform any of the method steps described above.

[0099] The processor can be a central processing unit, a graphics processing unit, a microcontroller, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an industrial control processor, an embedded processor, or a combination thereof. The memory can be volatile memory, non-volatile memory, a solid-state drive (SSD), flash memory, read-only memory (ROM), random access memory (RAM), a database, an edge cache, or a combination thereof.

[0100] A non-transitory computer-readable storage medium can store a computer program that, when executed by a processor, implements the methods described above. The storage medium can be a hard disk, optical disk, flash memory, solid-state drive, memory card, read-only memory, random access memory, server storage medium, edge control cabinet firmware storage medium, or cloud mirror storage medium.

[0101] Example 12: Complete Production Scenario A continuous pyrolysis facility produces biochar within a monitoring cycle. This monitoring cycle can be any period of time, including a day, a shift, a production batch, or a certification cycle. Taking a continuous production day as an example, the continuous pyrolysis facility continuously feeds and discharges biochar from 08:00 to 20:00. The raw material is lignocellulosic biomass from the same supplier. The reactor operating temperature is maintained between approximately 480°C and 520°C, the feed rate is maintained between approximately 1.8 tons / hour and 2.2 tons / hour, and the discharge rate is maintained between approximately 0.45 tons / hour and 0.60 tons / hour. The edge control cabinet receives wet weight data and moisture detection data from the weighbridge, discharge flow meter, and online moisture meter, and receives reactor temperature, feed rate, discharge rate, discharge valve status, spray cooling event data, and equipment alarm status data from the PLC.

[0102] In one example, the system uses the discharge time window from 10:00 to 12:00 as a calculation window corresponding to the biochar production share. Within this time window, the weighbridge records a total wet weight of 1,250 kg for incoming packaging, while the discharge flow meter records a cumulative wet weight of 1,238 kg for discharge within the same time window, a difference of 12 kg, or 0.96% of the wet weight recorded by the weighbridge. The weighing equipment is within its calibration validity period; the weighbridge calibration certificate number, calibration validity period, weighing timestamp, weighing window start and end times, and weighing equipment identification are written into the wet weight evidence data. The online moisture meter outputs moisture detection values ​​every 5 minutes, forming 24 moisture detection points between 10:00 and 12:00, with moisture values ​​ranging from 8.6% to 10.4%, and a weighted average moisture percentage of 9.3%.

[0103] The system generates a data share identifier for the discharge time window in S1. For example, the system can generate a data share identifier DS-20260604-1000-1200-001 and associate it with the corresponding packaging unit number, discharge time window, weighing event number, inventory unit number, and monitoring cycle number. The system further generates a biochar type channel record based on the raw material source, raw material composition, pyrolysis temperature range, residence time range, discharge time window, cooling method, moisture status, and representative status of the test report. For example, if the raw material source, reactor number, pyrolysis temperature range, cooling method, and representative status of moisture detection within the time window all meet preset common conditions, the system will assign the data share identifier to the biochar type channel BC-CH-A; if subsequent issues such as spray cooling, inventory damping, or inconsistent test representativeness occur, the system can split, update, or freeze the channel record.

[0104] The system acquires wet weight evidence data and dry basis quality correction evidence data in S2. Wet weight evidence data may include the weighbridge weighing value (1,250 kg), the cumulative discharge flow meter value (1,238 kg), the weighing time window (10:00 to 12:00), the weighing equipment identifier, the weighing equipment calibration status, and the weighing data quality status. Dry basis quality correction evidence data may include the online moisture sequence, the online moisture weighted average (9.3%), the sample number, the laboratory moisture report number, the spray cooling event record, the inventory status record, the equipment dynamic uncertainty status, the operating condition attribution data, and historical deviation data for the same biochar type channel. The above data may carry timestamps, data source identifiers, signature values, hash values, or previous status hashes.

[0105] In S3, the system generates candidate dry basis mass values ​​according to the wet weight deduction path. For example, based on a wet weight of 1,250 kg from the weighbridge and an online moisture weighted average of 9.3%, the first candidate dry basis mass value is 1,250 × (1 - 9.3%) = 1,133.75 kg. The system can also generate another candidate dry basis mass value based on the online moisture path. For example, based on a cumulative wet weight of 1,238 kg from the discharge flow meter and the weighted result of the online moisture sequence within the same time window, the second candidate dry basis mass value is 1,238 × (1 - 9.3%) = 1,122.87 kg. If the sample is obtained at 11:00 and the laboratory report indicates a moisture content of 9.8%, the system can verify or correct the candidate values ​​based on the sampling calibration path. For example, based on a wet weight of 1,250 kg from the weighbridge and a sampling moisture content of 9.8%, the reference dry basis mass is calculated to be 1,127.50 kg, which is then used as a sampling calibration candidate value or verification reference value.

[0106] In S4, the system calculates the difference in candidate values ​​within the corresponding biochar type channel BC-CH-A. For example, the difference between the first dry basis mass candidate value of 1,133.75 kg and the second dry basis mass candidate value of 1,122.87 kg is 10.88 kg, with a relative difference of approximately 0.96%; the difference between the first dry basis mass candidate value of 1,133.75 kg and the sampling calibration candidate value of 1,127.50 kg is 6.25 kg, with a relative difference of approximately 0.55%. If the preset difference threshold within this biochar type channel is 2.0%, and both the weighing equipment and the online moisture meter are currently available, the system can determine that there are no abnormal conflicts between the candidate values. The system further determines the fusion weight based on the candidate value source type, sampling calibration results, equipment dynamic uncertainty status, and historical deviation distribution within the channel. For example, the system can assign a higher weight to the wet weight deduction path of the weighbridge, an auxiliary weight to the discharge flow meter path, and a verification weight to the sampling calibration path, thereby generating a dynamic dry basis net weight result. For ease of explanation, the system can output a dynamic dry basis net weight result of 1,130 kg and mark the result status data as "normal and usable" or "can be included".

[0107] In another example, if the online moisture meter is affected by dust obstruction between 11:20 and 11:40, and the confidence level of four consecutive detection points is lower than the preset threshold, and the online moisture reading jumps from 9.5% to 15.8% in a short period of time, the system can identify the online moisture detection anomaly in S5. The system generates a degradation processing record, recording the affected data share identifier set as including DS-20260604-1000-1200-001, recording the abnormal signal type as "continuous low confidence and jump in online moisture", and setting the degradation fallback path type identifier to "online moisture to sampling calibration path". The system can call the sampling calibration data, the historical deviation distribution of the same biochar type channel BC-CH-A, and the dynamic uncertainty status of the equipment to determine the candidate value for degradation dry basis quality or the conservative adjustment state. For example, when the sampling moisture ratio is 9.8% and the conservative correction value of the upper quantile of the historical moisture deviation of the channel is 0.5 percentage points, the system can use a conservative moisture ratio of 10.3% for downgrading calculation. With a wet weight of 1,250 kg on the weighbridge, the candidate value of the downgraded dry basis mass is calculated to be 1,121.25 kg, and the result status data is marked as "conservative write-back" or "downgrade inclusion".

[0108] If the system further determines that the anomaly only affects the local time window from 11:20 to 11:40, while the data quality from 10:00 to 11:20 and from 11:40 to 12:00 is normal, the system can divide this discharge time window into multiple sub-windows and calculate the dry basis quality results for each sub-window separately. For example, the system can treat 10:00 to 11:20 as the normal window, 11:20 to 11:40 as the degradation window, and 11:40 to 12:00 as the recovery window, and record the wet weight, online moisture status, sampling calibration basis, degradation path, and result status for each sub-window. In this way, the system avoids excluding the biochar production share within the entire 10:00 to 12:00 time window due to localized online moisture anomalies.

[0109] The system generates a dynamic dry basis net weight self-certification object in S6. This dynamic dry basis net weight self-certification object may include a self-certification object identifier, a data share identifier DS-20260604-1000-1200-001, a biochar type channel record BC-CH-A, wet weight evidence data references, dry basis quality correction evidence data references, dynamic dry basis net weight result, result status data, downgrade processing record references, generation timestamp, computation node identifier, version snapshot hash, previous state hash, and signature value. The system writes this dynamic dry basis net weight self-certification object into the monitoring cycle verification evidence package and updates the status of the corresponding data share identifier in the data share state machine. For example, when the difference in candidate values ​​does not exceed the threshold and there is no abnormal trigger, the status can be updated to "can be included"; when the online moisture is abnormal but the sampling calibration downgrade process has been completed, the status can be updated to "downgraded inclusion" or "adjusted inclusion"; when there is a lack of sampling reports or the abnormality cannot be eliminated through statistical confidence constraints, physical conservation constraints or manual review, the status can be updated to "pending review" or "frozen".

[0110] During the audit or verification process, readable output records can present the calculation basis of the data share in the form of tables, electronic documents, or audit export files. For example, a readable output record could show: the discharge time window is 10:00 to 12:00, the wet weight evidence is 1,250 kg, the online moisture weighted average is 9.3%, the sampled moisture ratio is 9.8%, the dynamic dry basis net weight result under the normal path is 1,130 kg, the conservative dry basis weight result under the abnormal downgrade path is 1,121.25 kg, the result status is "conservative write-back" or "downgraded inclusion", and list the sampling report number, equipment calibration status, downgrade processing record identifier, and version snapshot hash. Thus, auditors can trace the source of wet weight, the basis for moisture correction, the difference in candidate values, the downgrade processing process, and the final dynamic dry basis net weight result along the data share identifier.

[0111] Example 13: Interface Example with Other Verification Modules The dynamic dry basis net weight self-certification object can be configured to interface with the test report representative module, uncertainty propagation module, life cycle state machine module, deduction mutual exclusion module, or end-use evidence module.

[0112] The representativeness module of the test report can provide the present invention with sampling calibration data, laboratory moisture report data, representative sample identification, or test parameter write-back records. The uncertainty propagation module can provide the present invention with equipment dynamic uncertainty status, parameter uncertainty status, or uncertainty propagation records. The life cycle state machine module can receive dynamic dry basis net weight self-certification objects and update the data share status.

[0113] The deduction exclusion module can prevent the same data share from being deducted repeatedly based on the set of affected data share identifiers, abnormal signal types, conservative adjustment status, or version snapshot hash in the degradation processing record. The end-use evidence module can receive dynamic dry basis net weight results for subsequent packaging unit transfer, outbound quality closure, or end-use evidence generation.

[0114] The above interfaces are only used to illustrate that the present invention can work in conjunction with other carbon removal verification data processing modules, and do not require that the above other modules be an essential component of any independent claim of the present invention.

[0115] Example 14: Data Structure Example To facilitate software engineering implementation, several exemplary fields of data objects are given below. It should be understood that field names can be replaced according to the specific system naming, as long as they represent the same or similar technical meaning.

[0116] Table 2 Data Structure Examples The data objects and field names mentioned above are based on their Chinese technical meanings. In software engineering implementation, English variable names, abbreviations, or camelCase naming conventions can be used in database fields, interface fields, program variables, or message bodies, as long as these English names, abbreviations, or camelCase conventions correspond to the technical meanings of the Chinese fields mentioned above. For example, the fallback path type can use a field identifier that corresponds to its semantics in the program implementation, but this does not change its technical meaning as a demotion path category in the fallback processing record.

[0117] Industrial applicability This invention can be applied to carbon removal projects for biochar production from biomass pyrolysis, and is suitable for continuous, semi-continuous or intermittent production lines. It can be deployed on source-side edge control cabinets, local servers, cloud-based dMRV platforms or combinations thereof.

[0118] This invention can be used in conjunction with weighing equipment, moisture detection equipment, PLC, DCS, IPC, inventory management equipment, laboratory testing systems, monitoring cycle verification systems, data share state machines, or audit export systems, and has good industrial applicability.

Claims

1. A multi-source fusion dynamic dry basis net weight machine self-verification method based on biochar type-based channeling, characterized in that, The dMRV data processing system includes one or more computing nodes, and the method includes: S1, Share Identifier and Channel Determination: Obtain or generate a data share identifier and biochar type channel record corresponding to the biochar output share. The biochar type channel record is used to characterize whether the corresponding biochar output share shares share the same dry basis quality treatment channel. S2, Dry basis quality evidence acquisition: Acquire or receive wet weight evidence data and dry basis quality correction evidence data related to the biochar production share, wherein the wet weight evidence data is used to characterize the quality benchmark of the biochar production share; S3, Candidate value generation and verification correction: Based on the wet weight evidence data and the dry weight correction evidence data, generate a first dry weight candidate value according to the wet weight deduction path, and generate a second dry weight candidate value based on at least one other dry weight generation or verification path, or verify or correct the first dry weight candidate value. S4, Channel-based verification and fusion: Based on the biochar type channel record, within the corresponding biochar type channel, perform consistency verification, quality status assessment, anomaly identification, or fusion estimation on the first dry basis quality candidate value, the second dry basis quality candidate value, the verification result, or the correction result to obtain the dynamic dry basis net weight result and its result status data. S5, Abnormal Downgrading and Dynamic Update: When the data related to the biochar production share meets the abnormal conditions, a downgrading processing record is generated, and a candidate value for downgraded dry basis quality or a conservative adjustment state is determined based on at least one of statistical confidence constraints, physical conservation constraints, equipment dynamic uncertainty status, sampling calibration data, and historical deviation data of the same biochar type channel; and the dynamic dry basis net weight result is updated, frozen, or marked as pending review based on changes in the downgrading processing record, the result status data, or the dry basis quality correction evidence data; S6, Self-certification object generation and output: Generate a dynamic dry basis net weight self-certification object, which includes the data share identifier, the biochar type channel record, and the dynamic dry basis net weight result, and associates or points to the result status data or the downgrade processing record; write, associate, or output the dynamic dry basis net weight self-certification object to the monitoring cycle verification evidence package, the data share state machine, or a readable output record.

2. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The biochar type channel record includes at least one of the following: biochar type identifier, associated data share identifier set, raw material boundary, operating condition boundary, production time window, moisture parameter channel, detection parameter write-back channel, representative sample identifier, representative test report identifier, historical deviation distribution within the channel, channel-specific uncertainty status, downgrade processing rules, parameter write-back status, version snapshot hash, previous status hash, and evidence chain identifier.

3. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The biochar type channel record is generated, updated, split, merged, or frozen based on at least one of the following: raw material source data, raw material composition data, furnace input data, pyrolysis condition data, discharge time window, moisture detection data, sampling calibration data, representative status of test report, or abnormal operating condition status.

4. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The wet weight evidence data includes at least one of the following: weighing sensor data, weighbridge data, packaging weighing data, discharge flow meter data, silo weighing data, weighing time window, weighing equipment calibration status, and weighing equipment data quality status; the dry basis quality correction evidence data includes at least one of the following: moisture content evidence data, sampling calibration data, feed dry basis estimation data, char production rate model parameters, environmental exposure event data, spraying or cooling event data, inventory status data, equipment dynamic uncertainty status data, and operating condition attribution data.

5. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, Other dry basis quality generation or verification paths are selected from: wet weight deduction path, online moisture path based on online moisture detection, sampling calibration path based on sampling calibration data, feed dry basis soft measurement path based on feed dry basis estimation data and operating condition attribution data, char production rate model path based on char production rate model parameters and pyrolysis operating condition data, quality conservation path based on inventory increase / decrease and packaging unit turnover, or heat balance path based on heat balance constraints.

6. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 5, characterized in that, The wet weight deduction path generates the candidate dry basis mass value based on wet weight evidence data and moisture evidence data; the online moisture path generates the second candidate dry basis mass value based on online moisture detection data, wet weight evidence data, or a weighing time window. The sampling calibration path verifies or corrects the candidate dry basis quality value based on sampling moisture data, laboratory moisture report data, or sampling deviation data; the feed dry basis soft measurement path generates a second candidate dry basis quality value based on feed dry basis estimation data, raw material moisture content, feed quality, or operating condition attribution data; the char production rate model path generates a second candidate dry basis quality value or reference value based on char production rate model parameters, pyrolysis temperature, residence time, feed rate, discharge rate, or historical data of similar operating conditions; the mass conservation path or heat balance path verifies or corrects the candidate dry basis quality value based on inventory increases / decreases, packaging unit turnover, inbound quality, outbound quality, mass conservation constraints, or heat balance constraints.

7. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The S4 process includes performing consistency checks, quality status assessments, anomaly identification, or fusion estimations, including: The difference between the first dry basis quality candidate value, the second dry basis quality candidate value, the verification result or the correction result within the same biochar type channel is calculated. Based on the candidate value source type, equipment dynamic uncertainty status, sampling and calibration results, operating condition status, data integrity status, historical deviation distribution or biochar type channel status, determine the fusion weight, selection rules, conservative value rules or dynamic threshold. When the difference exceeds a preset threshold or a dynamic threshold determined based on the device's dynamic uncertainty state, a candidate value conflict state, a manual review state, a downgraded use state, or a write-back rejection state is generated.

8. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The abnormal conditions include at least one of the following: missing, fluctuating, repeated, out-of-range, or low-confidence weighing data; fluctuating, continuously low-confidence, obstructed by dust, affected by abnormal material layer, deviation from the sampled moisture data or the soft measurement result of the feed dry basis; sensor drift, calibration failure, calibration nearing expiration, equipment dynamic uncertainty exceeding the threshold; communication interruption, missing data packet, abnormal timestamp, abnormal signature; spraying event, cooling event, inventory exposure event, damping event, mixing event, or manual verification triggered event.

9. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The degradation processing record includes at least one of the following: degradation record identifier, set of affected data share identifiers, affected biochar type identifier, abnormal signal type, degradation rollback path type identifier, statistical or physical conservative benchmark, candidate value of degradation dry basis quality, conservative adjustment status, result status data, manual review mark, previous status hash, version snapshot hash, and evidence chain identifier.

10. The multi-source fusion dynamic dry basis net weight machine self-certification method based on biochar type-based channeling according to claim 1, characterized in that, The wet weight evidence data, the dry basis quality correction evidence data, the weighing data, the equipment status data, or the operating condition attribution data are at least partially acquired, generated, or forwarded in real time by a programmable logic controller (PLC), a distributed control system (DCS), or an industrial control computer (IPC) in the pyrolysis facility control system, and are associated with the data share identifier or the biochar type channel record; the readable output records include at least one of the following: text reports, tabular records, electronic documents, audit export files, display interface records, printout records, verification summary records, and versioned report files; the monitoring cycle verification evidence package includes at least one of the following: monitoring cycle identifier, associated facility audit evidence package identifier, data share identifier sequence, dynamic dry basis net weight summary data, downgrade processing record, uncertainty propagation record, detection parameter write-back record, or version snapshot hash.

11. An edge control cabinet for self-certification of dynamic dry basis net weight of biochar, characterized in that, It includes an industrial data interface module, a trusted time module, a signature module, a processing module, and a communication module; The industrial data interface module is used to receive source-side physical signals from weighing equipment, moisture detection equipment, environmental sensors, inventory management equipment, programmable logic controllers (PLCs), distributed control systems (DCSs), or industrial control computers (IPCs). The trusted time module is used to time-bind the source-side physical signal, wet weight evidence data, dry basis quality correction evidence data, or dry basis quality related evidence objects. The signature module is used to perform signature processing, hash processing, or pre-hash processing on the source-side physical signal, wet weight evidence data, dry basis quality correction evidence data, or dry basis quality related evidence objects. The processing module is used to acquire or generate a data share identifier and a biochar type channel record corresponding to the biochar production share, and generate wet weight evidence data, dry basis quality correction evidence data or dry basis quality related evidence objects associated with the data share identifier or the biochar type channel record based on the source-side physical signal. The processing module is also used to generate candidate values ​​for dry basis quality, verification results, correction results, quality status assessment results or degradation trigger flags in the corresponding biochar type channel based on the wet weight evidence data and the dry basis quality correction evidence data, and to perform local consistency verification or pre-fusion estimation. The communication module is used to output the wet weight evidence data, the dry basis quality correction evidence data, the dry basis quality related evidence objects, the dry basis quality candidate values, the verification results, or the correction results to local storage, cloud server, monitoring cycle verification evidence package interface, data share state machine interface, or readable output records.

12. A multi-source fusion dynamic dry basis net weight machine self-certification system based on biochar type-based channelization, characterized in that, This includes source-side data acquisition equipment, edge computing devices, digital carbon removal measurement, dMRV data processing server for reporting and verification, and a status management module; The source-side data acquisition device is used to collect wet weight data, moisture detection data, environmental exposure event data, spray or cooling event data, inventory status data, equipment status data, or operating condition data. The edge computing device is communicatively connected to the source-side data acquisition device and is used to perform time binding, data quality judgment, anomaly identification, hash processing, pre-hash processing or signature processing on the wet weight data, moisture detection data, environmental exposure event data, spray or cooling event data, inventory status data, equipment status data or operating condition data, and generate wet weight evidence data, dry basis quality correction evidence data or dry basis quality related evidence objects. The dMRV data processing server is used to acquire or receive the wet weight evidence data, the dry basis quality correction evidence data, the data share identifier, and the biochar type channel record. Based on the wet weight evidence data and the dry basis quality correction evidence data, it generates a first dry basis quality candidate value, a second dry basis quality candidate value, a verification result, or a correction result. In the corresponding biochar type channel, it performs consistency verification, quality status assessment, anomaly identification, or fusion estimation on the first dry basis quality candidate value, the second dry basis quality candidate value, the verification result, or the correction result to generate a dynamic dry basis net weight result. The state management module is used to generate dynamic dry basis net weight self-certification objects, and write, associate, or output the dynamic dry basis net weight self-certification objects to the monitoring cycle verification evidence package, data share state machine, or readable output record.

13. An electronic device, characterized in that, The device includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the method according to any one of claims 1 to 10.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 10.

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