A tunnel spoil treatment whole life cycle tracing management method

Abstract

The application discloses a tunnel abandoned slag treatment full life cycle tracing management method, relates to the field of tunnel construction solid waste resource management and digital tracing technology, and comprises the following steps: automatically generating a blasting block size digital passport FDP containing a passport mark, geological information, blasting information, block size information and quality rating for each blasting cycle; realizing full-link tracking of slag batches through bidirectional mapping of RFID electronic tags and FDP digital identities; calculating a comprehensive quality score based on the weighted synthesis of four quantitative sub-scores of the strength dimension, block size dimension, safety dimension and harmful component dimension, and generating a resource utilization recommendation; realizing quantitative supply and demand automatic recommendation through a supply and demand matching degree function obtained by the weighted synthesis of the quality matching degree, distance matching degree, time matching degree and quantity matching degree; and writing FDP data into a block chain to realize tamper-proof trusted tracing. A blasting-geology-quality multi-dimensional data driven resource supply and demand matching closed loop is constructed.

Description

Technical Field

[0001] This invention relates to the field of resource management and digital traceability technology for solid waste from tunnel construction, and more specifically to a method for full life-cycle traceability management of tunnel waste disposal. Background Technology

[0002] Tunnels generate massive amounts of waste material during construction; however, only a small portion is truly utilized as a resource, while the vast majority faces the fate of being stockpiled, occupying land, and posing potential environmental risks. A key factor restricting the resource utilization of waste material is the lack of a reliable traceability mechanism for its quality. Downstream aggregate users cannot obtain crucial quality information such as the tunnel from which the waste material originated, its geological background, blasting block size, rock strength, and harmful components, leading to low willingness to utilize it and low transaction prices.

[0003] Regarding the resource utilization of tunnel spoil, existing research has shown that by assessing the mechanical properties, composition, and microstructure of tunnel spoil, it is possible to determine whether it meets the standards for base course water-stabilized material, coarse aggregate, or fine aggregate. Relevant technical regulations have stipulated the grading, processing technology, processing equipment, and technical requirements for finished materials of tunnel spoil. In terms of carbon emission reduction accounting, assessment standards for greenhouse gas emission reductions from construction waste demolition, disposal, and reuse projects have been established, as well as carbon emission reduction accounting methodologies for replacing natural aggregates with recycled aggregates from construction waste.

[0004] However, the above technical solution has the following core flaws:

[0005] (1) Blasted block size information is not included in the traceability system: Existing traceability systems for construction waste or building materials focus on waste transfer links or component testing parameters, and do not include blasted block size characteristic information as core quality data in the traceability system. Blasted block size is a unique quality attribute that distinguishes tunnel spoil from other construction solid waste—it directly determines the particle size distribution of the spoil and the energy consumption and product direction of subsequent processing. Systematically incorporating blasted block size information into the data fields of digital passports is a key prerequisite for realizing the refined resource utilization of tunnel spoil, but existing technologies do not provide such a solution.

[0006] (2) Lack of a quality rating system integrating blasting, geology, and quality data: Existing quality traceability schemes are mostly single-dimensional (e.g., only recording whether the test is qualified or not), and have not established a complete data chain from geological conditions → blasting parameters → block size results → rock mechanical properties → resource utilization recommendation direction. The quality of tunnel spoil depends not only on the strength of the rock itself, but also on the degree of blasting fragmentation—rocks of the same lithology can have different block size distributions due to different blasting parameters, which in turn affects their optimal utilization direction. Existing technologies cannot achieve this multi-dimensional data correlation analysis and automatic rating.

[0007] (3) Supply and demand matching relies on qualitative experience and lacks a quantitative matching function: In the existing technical solutions, there is a lack of a systematic automatic matching mechanism based on quantifiable quality indicators and logistics indicators between the "suppliers" and "demanders" of waste. Supply and demand matching still relies on manual information transmission and experience judgment, which is inefficient and has poor matching accuracy.

[0008] (4) The linkage mechanism between the physical and digital carriers of traceability data is not perfect: In the existing technical solutions, RFID tags are mostly used to identify slag trucks or garbage bags. No information linkage mechanism has been designed for the characteristics of tunnel slag disposal “managed in batches according to blasting cycles”. Each blasting cycle produces slag as an independent batch unit with unified geological source, blasting parameters and block size characteristics. A two-way mapping based on cycles should be established between the physical world and the digital world.

[0009] In summary, there is an urgent need for a systematic technical solution that incorporates blasting block size information as a core data field into digital passports, establishes a quantitative rating system that integrates blasting-geology-quality multi-dimensional data, and provides specific matching degree functions and a physical-digital dual-carrier correlation mechanism. Summary of the Invention

[0010] In view of the above problems, this invention is proposed to provide a method for full lifecycle traceability management of tunnel spoil disposal that overcomes or at least partially solves the above problems. To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, embodiments of the present invention provide a method for full lifecycle traceability management of tunnel spoil disposal, including: S1. Taking each blasting cycle of tunnel construction as the basic management unit, automatically collect the geological information, blasting parameters and block size detection data after blasting for that cycle, generate a blasting block size digital passport (FDP) containing passport identifier, geological information, blasting information, block size information and quality rating information, and perform hash operation on all data fields of the blasting block size digital passport (FDP) to generate a data digest value. S2. Deploy physical carriers for the batches of slag generated by the blasting cycle, wherein the physical carriers store the unique passport identifier of the blasting block size digital passport FDP. S3. Based on the geological information, blasting information and block size information in the blasted block size digital passport FDP, calculate the comprehensive quality score through a preset four-dimensional weighted model, determine the quality grade and recommended utilization direction of the slag material according to the comprehensive quality score, and write the comprehensive quality score, quality grade and recommended utilization direction into the quality rating information of the blasted block size digital passport FDP. S4. Receive the slag demand information released by the demander, and based on the quality rating information in the blasting block size digital passport FDP, combined with the demander's quality, distance, time and quantity constraints, calculate the comprehensive matching degree between the supplier's slag batch and the demander through a multi-objective optimization matching degree function, and push a recommendation list to the demander according to the matching degree. S5. Store the unique identifier, data digest value, and storage address of the blasting block degree digital passport FDP in the distributed file system on the blockchain to generate an immutable evidence record. S6. When the slag material is transferred to the final utilization node, the complete blasting block size digital passport (FDP) is associated and obtained by reading the unique passport identifier in the physical carrier. Based on the slag material volume, rock density and actual utilization direction of the push recommendation list recorded in the blasting block size digital passport (FDP), the carbon emission reduction generated by the resource utilization of this batch of slag material is calculated using the full life cycle carbon emission reduction calculation model.

[0011] Preferably, the passport identifier includes a unique passport identifier, tunnel project number, cycle number, construction date, start and end chainage range, excavation cross-sectional area, cycle advance, total volume of slag, and estimated total mass of slag. The geological information includes the surrounding rock grade, basic quality indicators, mean uniaxial compressive strength, standard deviation of uniaxial compressive strength, mean rock mass quality indicators, number of joint groups, average joint spacing, mean longitudinal wave velocity of rock mass, main mineral composition, radioactivity detection value, and heavy metal leaching toxicity detection value. The blasting information includes the slotting method, the total number of boreholes across the entire cross section, the average explosive consumption per unit volume, the type of explosive, the explosive density, the delay scheme identifier, the maximum charge per segment, the total charge, and the detonation method. The block size information includes characteristic particle size, maximum block size, block size uniformity index, fine particle rate, oversized block rate, block size detection method, and block size distribution curve. The quality rating information includes a comprehensive quality score, quality level, first recommended use direction, second recommended use direction, reason for restricted use, quality rating calculation timestamp, and the data hash value of this passport.

[0012] Preferably, in step S2, the physical carriers for batch deployment of the slag generated in the blasting cycle specifically include: For the batch of slag, redundant deployment of ultra-high frequency passive anti-metal RFID electronic tags is carried out as the physical carrier, and the unique identifier of the passport is written into the chip of the RFID electronic tag; The S2 further includes: at the transportation, storage, processing and utilization nodes of slag material flow, reading the passport unique identifier in the physical carrier through an RFID reading device, associating and obtaining the complete blasting block size digital passport FDP in the cloud, writing the flow records of each node into the blasting block size digital passport FDP in the form of data appending, and recalculating the data digest value.

[0013] Preferably, the formula for calculating the comprehensive quality score is: Q SCORE = w strength ×S strength + w frag ×S frag + w safety ×S safety + w hazard ×S hazard Among them, w strength w frag w safety w hazard The weighting coefficients for the intensity dimension, block size dimension, safety dimension, and hazardous component dimension, respectively, Q. SCORE For the overall quality score, S strength For the intensity dimension, S frag S scores the block size dimension. safety Score for safety dimension hazard Scoring based on the harmful components dimension; S strength = min(100, 100 × (UCS MEAN / UCS REF ) α × (RQD MEAN / RQD REF ) β × (BQ VALUE / BQ REF ) γ ) Among them, UCS MEAN UCS is the mean uniaxial compressive strength. REF RQD is the reference uniaxial compressive strength. MEAN RQD is the mean value of the rock quality index. REF BQ is the baseline RQD value. VALUE BQ value is the basic quality index of rock mass. REF The baseline BQ value is used, and α, β, and γ are sensitivity indices. S frag = 100 - k oversize ×R OVERSIZE - k fines ×RFINES -k uniform × max(0, (1 - N VALUE / N REF )) Among them, R OVERSIZE For the block ratio, k oversize R is the penalty coefficient for large block ratio. FINES For the fineness ratio, k fines N is the fineness penalty coefficient; VALUE Let n be the uniformity index of block size, and N be the uniformity index. REF k is the benchmark uniformity index. uniform This is the uniformity penalty coefficient; S safety = 100 × (1 - max(0, (N JOINT - J0) / J0) × δ joint ) × (1 - max(0,(D MIN - S JOINT ) / D MIN ) × δ spacing ) Where, N JOINT J0 is the number of joint groups; J0 is the baseline joint group number; δ joint S is the influence coefficient of the number of joint groups. JOINT D represents the average joint spacing. MIN δ is the minimum joint spacing as the reference. spacing This is the joint spacing influence coefficient; S hazard = 100×(1-HEAVY METAL ×δ heavy )×(1-RADIOACTIVITY / RADIO LIMIT ×δ radio ) Among them: HEAVY METAL The value for heavy metal leaching toxicity detection, δ heavy RADIOACTIVITY represents the heavy metal hazard factor, and RADIOACTIVITY represents the gamma radiation dose rate detected in the rock mass. LIMIT As a radioactive safety limit, δ radio This represents the radioactivity hazard factor.

[0014] Preferably, the recommendation is generated using the following multi-dimensional matching rules: When S strength ≥70 and S hazard The particle size value X corresponding to ≥90 and cumulative particle size distribution reaching 50% 50When the diameter is ≥300 mm, the first recommended use is for slope protection stone, riprap or reclamation stone, and the second recommended use is for crushed stone as concrete aggregate. When S strength ≥50 and S hazard ≥90 and 100 mm≤X 50 When the thickness is less than 300 mm, the first recommended use is to crush it into concrete aggregate, and the second recommended use is to use it as graded crushed stone for roadbed. When S strength ≥50 and S hazard ≥90 and X 50 When the thickness is less than 100 mm, the first recommended direction is to use it as sand making raw material or roadbed fill material, and the second recommended direction is to use it as backfill material. When 30≤S strength <50 and S hazard When the thickness is ≥90, the first recommended direction is roadbed filler or base course water-stabilized material, and the second recommended direction is site backfill. When S strength <30 and S hazard When the value is ≥90, the first recommended direction is roadbed filling material, and the second recommended direction is goaf filling. When S hazard When the value is less than 60, the first recommended use direction is restricted use, and the second recommended use direction is safe storage.

[0015] Preferably, the multi-objective optimization matching degree function is calculated using the following formula: M = w quality ×M quality + w distance ×M distance + w time ×M time + w quantity M quantity Among them, w quality w distance w time w quantity These are the weight coefficients for quality matching, distance matching, time matching, and quantity matching, respectively, satisfying that the sum of the weights is 1, M quality M distance M time M quantity These are quality matching degree, distance matching degree, time matching degree, and quantity matching degree, respectively, with M representing the overall matching degree; M quality = 100×(Q SCORE_supply / Q SCORE_require )×min(1, Q SCORE_require / Q SCORE_supply) Among them, Q SCORE_supply Q is the overall quality score of the supplier's slag material. SCORE_require The minimum overall quality score required by the demander; M distance = 100 × (1 -D actual / D max ) Among them, D actual D represents the actual transportation distance from the supplier to the demander. max The maximum acceptable transportation distance set for the demand side; M time = 100 × (1 -|T available - T require | / T window ) Among them, T available T is the time when the supplier can supply. require T represents the center date of the delivery time window required by the demander. window The width of the time window; M quantity = 100 × (1 -|V supply -V require | / V require ) Among them, V supply V is the volume of slag that the supplier can supply. require This refers to the volume of slag required by the demander.

[0016] Preferably, the generation of an immutable evidence record in step S5 specifically includes: A consortium blockchain is used as the underlying blockchain platform, and FDP notarization contract, FDP query contract and transaction record appending contract are deployed. The FDP notarization contract receives the unique passport identifier, data digest value, and distributed file system storage address of the brute-force digital passport FDP, and writes these three along with the current timestamp into the consortium blockchain ledger to generate a timestamped notarization record. The transfer record appending contract receives the passport unique identifier, transfer record content, and updated data digest value, and appends the transfer record to the consortium blockchain ledger. The FDP query contract receives the passport's unique identifier as a query parameter and returns the notarized block height, notarized timestamp, and data digest value of the blasting block degree digital passport FDP, which are used by downstream users to verify the integrity and authenticity of the data.

[0017] Preferably, in step S6, a life-cycle carbon emission reduction calculation model is used to calculate the carbon emission reduction, and the specific calculation formula is as follows: ΔCO2= V utilized × ρ rock × [E natural - E tunnel + E transport_diff ] × F CO2 ; Among them, V utilized ρ represents the volume of slag that is recycled. rock E represents the density of the rock. natural E represents the overall energy consumption for mining and processing natural aggregates. tunnel E represents the comprehensive energy consumption for processing tunnel slag into aggregate of equal quality. transport_diff F represents the difference between the energy consumption for transporting natural aggregates and the energy consumption for transporting tunnel slag. CO2 ΔCO2 is the energy consumption-carbon emission conversion factor, and ΔCO2 is the carbon emission reduction.

[0018] Secondly, embodiments of the present invention provide a full lifecycle traceability management system for tunnel spoil disposal, including: The blasting block size digital passport generation module takes each blasting cycle in tunnel construction as the basic management unit, automatically collects the geological information, blasting parameters and block size detection data after blasting for that cycle, generates a blasting block size digital passport (FDP) containing passport identifier, geological information, blasting information, block size information and quality rating information, and performs hash operation on all data fields of the blasting block size digital passport (FDP) to generate a data digest value; The physical-digital dual carrier association module for slag batches deploys physical carriers for the slag batches generated by the blasting cycle. The physical carriers store the unique passport identifier of the blasting block size digital passport (FDP). The multi-level quantitative quality rating and resource utilization recommendation module calculates a comprehensive quality score based on the geological information, blasting information and block size information in the blasted block size digital passport (FDP) through a preset four-dimensional weighted model. It then determines the quality grade and recommended utilization direction of the slag material based on the comprehensive quality score and writes the comprehensive quality score, quality grade and recommended utilization direction into the quality rating information of the blasted block size digital passport (FDP). The resource-based supply and demand matching engine module receives slag demand information released by the demand side. Based on the quality rating information in the blasting block size digital passport FDP, and combined with the demand side's quality, distance, time and quantity constraints, it calculates the comprehensive matching degree between the supplier's slag batch and the demand side through a multi-objective optimization matching degree function, and pushes a recommendation list to the demand side according to the matching degree. The blockchain-based trusted traceability and evidence storage module stores the unique identifier, data digest value, and storage address of the blasting block degree digital passport FDP on the blockchain, generating an immutable evidence storage record. The carbon emission reduction benefit calculation module reads the unique passport identifier in the physical carrier when the slag is transferred to the final utilization node, associates and obtains the complete blasting block size digital passport (FDP), and calculates the carbon emission reduction generated by the resource utilization of this batch of slag based on the slag volume, rock density and actual utilization direction of the push recommendation list recorded in the blasting block size digital passport (FDP).

[0019] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, a method for full lifecycle traceability management of tunnel waste disposal is implemented.

[0020] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for full life-cycle traceability management of tunnel spoil disposal, which has the following effects: (1) For the first time, the blasting block size information is systematically incorporated as a core data field into the quality traceability system of the tunnel waste digital passport. Unlike existing construction waste traceability schemes that only focus on data from the circulation process or component testing parameters, this invention constructs a standardized FDP data model with "blasting block size characteristics" as the core index. The blasting block size directly determines the particle size distribution of the slag and the energy consumption and product orientation of subsequent processing. This unique quality dimension has not been adopted in existing traceability technologies.

[0021] (2) A physical-digital dual-carrier association mechanism with "blasting cycle" as the batch management unit is proposed. Unlike existing RFID traceability schemes that track by "vehicle" or "garbage bag", this invention targets the output characteristics of tunnel spoil—the spoil from each blasting cycle has a unified geological source, blasting parameters, and block size characteristics—and generates FDPs on a cycle-by-cycle basis and associates them with redundant RFID electronic tags, realizing a two-way accurate mapping from the physical world (slag batch) to the digital world (FDP digital identity). This "cycle-based management" concept fits the cyclical operation characteristics of tunnel construction and is different from all existing schemes that manage by transportation unit or time batch.

[0022] (3) A comprehensive quality score calculation model based on four-dimensional quantitative synthesis was established. Unlike the qualitative recommendation method of pure rule matching in the prior art, this invention innovatively synthesizes the intensity dimension, block size dimension, safety dimension, and harmful component dimension into a standardized score of 0 to 100 points through specific calculable mathematical formulas. This quantitative scoring system provides objective and comparable quality judgment standards for both supply and demand sides, and also provides continuous quality input for subsequent supply and demand matching.

[0023] (4) A quantitative supply-demand matching degree function M based on multi-objective optimization is proposed. The quality matching degree M... quality Distance matching degree M distance Time matching degree M time Matching degree M with quantity quantity A comprehensive matching score (0~100) is synthesized through weighted linear summation. This quantitative matching function upgrades supply and demand matching from qualitative judgment based on human experience to data-driven quantitative decision-making, providing an algorithmic foundation for large-scale automated supply and demand matching recommendations.

[0024] (5) A refined accounting model for carbon emission reduction from the resource utilization of tunnel waste was established. Based on the standard carbon emission reduction accounting method, an innovative transportation energy consumption difference correction term was introduced, incorporating the difference in transportation distance between natural aggregates and tunnel waste into the emission reduction calculation—when the tunnel is closer to the end user, it contributes additional transportation emission reduction benefits. This correction makes the carbon emission reduction accounting more refined, providing a more accurate accounting basis for carbon trading of tunnel waste resource utilization.

[0025] (6) A lightweight, trusted traceability architecture combining blockchain-based evidence storage and cloud-based data storage was constructed. Only the FDP_ID, DATA_HASH, and IPFS content address are stored on the blockchain, while the complete FDP data is stored in the cloud or a distributed file system, significantly reducing blockchain storage costs. Simultaneously, tamper-proof verification is achieved by comparing the on-chain hash value with the recalculated hash value from off-chain data. The combination of this lightweight evidence storage architecture and IPFS distributed storage has been verified as feasible.

[0026] In summary, this invention incorporates blasting block size information as core quality data into the digital passport system, enabling the industrial upgrading of tunnel spoil from waste of unknown origin to digital raw material commodities with complete quality records. This provides a systematic technical solution to address the pain point of the industry's lack of a reliable quality traceability mechanism. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0028] Figure 1 This is a flowchart of a method for full lifecycle traceability management of tunnel spoil disposal provided in an embodiment of the present invention; Figure 2 This is a flowchart of the FDP (Flaming Block Degree Digital Passport) generation process provided in this embodiment of the invention; Figure 3 This is a schematic diagram illustrating the physical-digital dual-carrier association of slag batches provided in an embodiment of the present invention; Figure 4 This is a flowchart of the automated identification process for key nodes across the entire link provided in this embodiment of the invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] This invention discloses a method for full lifecycle traceability management of tunnel spoil disposal, such as... Figure 1 As shown, it includes: S1. Taking each blasting cycle of tunnel construction as the basic management unit, automatically collect the geological information, blasting parameters and block size detection data after blasting for that cycle, generate a blasting block size digital passport (FDP) containing passport identifier, geological information, blasting information, block size information and quality rating information, and perform hash operation on all data fields of the blasting block size digital passport (FDP) to generate a data digest value. S2. Deploy physical carriers for batches of slag generated in the blasting cycle. The physical carriers store the unique identifier of the blasting block size digital passport (FDP). S3. Based on the geological information, blasting information and block size information in the blasted block size digital passport (FDP), calculate the comprehensive quality score through a preset four-dimensional weighted model, determine the quality grade and recommended utilization direction of the slag material according to the comprehensive quality score, and write the comprehensive quality score, quality grade and recommended utilization direction into the quality rating information of the blasted block size digital passport (FDP). S4. Receive slag demand information released by the demander, and based on the quality rating information in the blasting block size digital passport FDP, combined with the demander's quality, distance, time and quantity constraints, calculate the comprehensive matching degree between the supplier's slag batch and the demander through a multi-objective optimization matching degree function, and push a recommendation list to the demander according to the matching degree. S5. Store the unique identifier, data digest value, and storage address of the brute-force digital passport FDP in the distributed file system on the blockchain to generate an immutable evidence record. S6. When the slag material is transferred to the final utilization node, the unique identifier of the passport in the physical carrier is read, the complete blasting block size digital passport (FDP) is associated and obtained, and the carbon emission reduction generated by the resource utilization of this batch of slag material is calculated based on the slag material volume, rock density and actual utilization direction of the push recommendation list recorded in the blasting block size digital passport (FDP). The full life cycle carbon emission reduction calculation model is used to calculate the carbon emission reduction generated by the resource utilization of this batch of slag material.

[0031] Specifically, the detailed implementation process of each step is as follows: S1: Standardized Data Model Construction and Automatic Generation of Digital Passports for Burst Block Size The core of this step lies in constructing a standardized digital passport data model with blasting block size information as the core index, integrating geological information, blasting information, block size information and quality rating information into structured data units, and realizing the full-dimensional digital expression of the quality attributes of tunnel spoil.

[0032] ① Standardized definition of data fields in digital passport for blasting block size A unique Fragmentation Digital Passport (FDP) is generated for each blasting cycle and stored in a structured data format. The FDP contains five standardized data groups, each with data fields that have a unique and explicit physical meaning: Table 1 Passport Identification

[0033] Table 2 Geological Information

[0034] Table 3 Blasting Information

[0035] Table 4 Block Degree Information

[0036] Table 5 Quality Rating Information

[0037] ② FDP Data Integrity Guarantee After serializing all the above data fields, the data digest value DATA_HASH is calculated using the SHA-256 hash algorithm and written to the end of the fifth block of the FDP. Any tampering with the FDP data will result in a recalculated hash value that is inconsistent with the stored DATA_HASH, thus being detected. The FDP is stored in JSON structured format, facilitating data exchange and parsing between different systems.

[0038] ③ Automatic generation process of blasting block degree digital passport (FDP) The generation of FDP is automatically triggered after each blasting cycle is completed and the block size detection data is returned, requiring no manual intervention. For example... Figure 2 As shown, the generation process includes the following sub-steps: S1-1: Obtain passport identification (passport unique identifier, tunnel project number, cycle number, construction date, chainage range, excavation cross-sectional area, cycle advance, total volume and total mass of slag) from the tunnel construction management system.

[0039] S1-2: Obtain geological information (surrounding rock grade, basic quality index (BQ) value, mean and standard deviation of uniaxial compressive strength (UCS), mean of rock mass quality index (RQD), number of joint groups, average joint spacing, and mean longitudinal wave velocity of rock mass) from the MWD measurement-while-drilling system; and obtain the main mineral composition, radioactivity detection value, and heavy metal leaching toxicity detection value from geological advance prediction reports and laboratory test reports.

[0040] S1-3: Obtain blasting information (cutting method, total number of boreholes across the entire cross section, average explosive consumption, explosive type and density, delay scheme identification, maximum charge per section, total charge and detonation method) from the blasting design system and detonation control system.

[0041] S1-4: Obtain block size information (feature particle size X) from the online block size detection system. 50 Characteristic particle size X 80 Characteristic particle size X MAX (Patch uniformity index, Swebrec parameters, oversized patch rate, fine-particle rate, patch size detection method, and patch size distribution curve).

[0042] S1-5: Assemble all the above data fields into a complete JSON document for the FDP, calculate the data digest value DATA_HASH and write it, thus completing the FDP generation. The generated FDP simultaneously performs blockchain notarization in S5 and physical carrier association in S2.

[0043] S2: Batch Physical-Digital Dual-Carrier Correlation and End-to-End Tracking of Slag Materials The core of this step lies in constructing a physical-digital dual-carrier association mechanism with the "blasting cycle" as the batch management unit. Through bidirectional mapping between RFID electronic tags (physical carrier) and FDP digital identities (digital carrier), it achieves automated identification and real-time acquisition of quality data for slag materials at all stages of the entire chain, including transportation, storage, processing, and utilization. Figure 3 As shown.

[0044] ①Physical Carrier – Design and Deployment of RFID Anti-metal Electronic Tags To address the harsh environmental conditions in tunnel spoil transportation scenarios, such as high dust concentration, high humidity, and severe vehicle vibration, the RFID electronic tag adopts the following design: Tag type: UHF (860~960 MHz) passive anti-metal electronic tag, with a reading distance of 3~8 m, suitable for high-speed batch reading when muck transport vehicles pass through RFID tunnel machines.

[0045] Tag storage content: The tag chip stores the encoded information for the following key fields: FDP_ID (Passport Unique Identifier), TUNNEL_ID (Tunnel Project Number), CYCLE_NO (Cycle Number), BLAST_DATE (Construction Date), QUALITY_GRADE (Quality Grade), and DATA_HASH (The first 16 characters of the data hash value, i.e., the data digest value). The tag storage content uses a compressed encoding format to accommodate the limited storage capacity of the tag chip.

[0046] Tag deployment method: For each blasting cycle, generate 2-4 redundant RFID electronic tags with identical information. These tags are affixed to designated locations on the outside of the first dump truck in that cycle, on the back of the accompanying paper waybill, and (optionally) in the driver's cab. This multi-tag redundancy deployment ensures that batch information can still be read through other tags even if some tags fail due to impact, mud coverage, or other reasons.

[0047] ② Digital Carrier – Cloud-based Association of FDP Digital Identity The digital identity of FDP is stored in a cloud database using the passport's unique identifier FDP_ID. The complete FDP JSON document is stored in distributed storage via IPFS (InterPlanetary File System) or a centralized cloud database. Only the passport's unique identifier FDP_ID, the data digest value DATA_HASH, and the IPFS content address (ContentIdentifier) ​​are stored on the blockchain, which significantly reduces the blockchain storage cost.

[0048] ③ Automated identification of key nodes across the entire chain like Figure 4As shown, RFID reading devices (fixed RFID tunnel machines or handheld RFID readers) are deployed at each key node in the entire slag transportation chain. When slag transport vehicles or slag containers pass by, the unique passport identifier FDP_ID and other information in the RFID tags are automatically read, and the timestamp and geographical location of the node are recorded in real time.

[0049] Key nodes include: Node 1 is the tunnel entrance (after slag loading is completed and slag leaves the site), recording the license plate number of the slag transport vehicle, loading time, and slag volume; Node 2 is the entrance / exit of the temporary stockpile, recording the entry time and stockpile location code; Node 3 is the entrance of the processing plant / aggregate production line, recording the arrival time and receipt confirmation; Node 4 is the finished product leaving the processing plant; Node 5 is the final utilization site (such as the roadbed filling site or concrete mixing plant).

[0050] When each node reads the RFID tag, it automatically associates the complete FDP data stored in the cloud with the unique passport identifier FDP_ID. Downstream users can instantly query the complete source tunnel, geological background, blasting parameters, block size characteristics and quality rating information of the batch of slag by scanning the tag or entering the unique passport identifier FDP_ID at any node.

[0051] ④ Dynamic data appending Throughout the entire process, data collected at each key node (such as actual arrival time and actual reception quality) is appended to the JSON document of the FDP in the form of a "transfer record." The data digest value DATA_HASH is recalculated and updated, and the new data digest value DATA_HASH is written to the blockchain to form a timestamped update record. The transfer record includes a record type identifier, record timestamp, record node location, record content text description, and operator identifier. This mechanism constructs a complete traceability loop for slag from generation to final utilization.

[0052] S3: Multi-level quantitative quality rating and resource utilization recommendation algorithm The core innovation of this step lies in establishing a multi-dimensional quantitative mapping model that integrates geological information, blasting information, block size information, and hazardous component information with a comprehensive quality score. This model replaces pure rule descriptions with specific numerical calculations, ensuring the feasibility of the technical solution.

[0053] ① Overall quality score Q SCORE computational model Overall quality score Q SCORE It is a weighted composite of sub-scores from four dimensions: Q SCORE = w strength × S strength + w frag × S frag + w safety× S safety + w hazard × S hazard Where w strength w frag w safety w hazard The weighting coefficients for the intensity dimension, block size dimension, safety dimension, and hazardous component dimension are respectively, satisfying Σw=1, with default values ​​of 0.35, 0.25, 0.25, and 0.15 respectively. Each weighting coefficient can be adjusted by the user in the platform configuration interface according to project requirements.

[0054] i) Intensity dimension sub-score S strength S strength = min(100, 100 × (UCS MEAN / UCS REF ) α × (RQD MEAN / RQD REF ) β × (BQ VALUE / BQ REF ) γ ) Among them: UCS MEAN The mean uniaxial compressive strength of the rock at the working face during this cycle is expressed in MPa; UCS REF The uniaxial compressive strength of the reference rock is taken as 80 MPa (corresponding to the typical UCS value of medium-hardness granite); RQD MEAN This represents the mean RQD (Rock Quality Demand) value for this cycle, in % (%). REF The baseline rock quality index is 90%; BQ VALUE The BQ value is the basic quality index of this cyclic rock mass (calculated according to the TB10003-2016 standard); BQ REF The baseline BQ value is 400 (corresponding to the median value of Class III surrounding rock); α is the strength contribution sensitivity index, with a default value of 0.6 and a range of 0.4 to 0.8; β is the integrity contribution sensitivity index, with a default value of 0.4 and a range of 0.3 to 0.6; γ is the BQ contribution sensitivity index, with a default value of 0.3 and a range of 0.2 to 0.5.

[0055] The physical logic of this formula is: the higher the rock strength (UCS) MEAN The larger the rock mass, the more complete the rock mass (RQD). MEAN The larger the value (the higher the BQ value), the higher the strength score, and the more suitable the rock is for resource utilization (such as concrete aggregate) where high mechanical properties are required.

[0056] ii) Block Degree Dimension Sub-Score S frag S frag = 100 - k oversize × R OVERSIZE - k fines × R FINES - k uniform × max(0, (1 - N VALUE / N REF )) Where: R OVERSIZE Large block ratio (the percentage of rock blocks with a diameter greater than 600 mm by mass), unit: %; k oversize This is the penalty coefficient for large block ratio, with a default value of 0.3 and a range of 0.2 to 0.5; R FINES Fine particle ratio (the percentage of fine particles with a diameter less than 5 mm by mass), unit: %; k fines This is the fine-grainedness penalty coefficient, with a default value of 0.15 and a range of 0.1 to 0.3; N VALUE N is the uniformity index of block size; REF The baseline uniformity index is 2.0 by default; k uniform This is the uniformity penalty coefficient, with a default value of 20 and a range of 10 to 30.

[0057] The physical logic of this formula is as follows: both the large particle ratio and the fine particle ratio negatively impact the quality of the slag—an excessively high large particle ratio increases subsequent crushing energy consumption and processing costs, while an excessively high fine particle ratio reduces aggregate yield and added value. A higher uniformity index n indicates a more uniform particle size distribution, which is more conducive to crushing and screening processing.

[0058] iii) Safety dimension sub-score S safety S safety = 100 × (1 - max(0, (N JOINT -J0) / J0) × δ joint ) × (1 - max(0,(D MIN - S JOINT ) / D MIN ) × δ spacing ) Where: N JOINT J0 is the number of joint groups; J0 is the baseline joint group number, which is 2; δ joint This is the influence coefficient for the number of joint groups, defaulting to 0.10; S JOINT The average joint spacing is expressed in mm; D MIN The minimum joint spacing is taken as 200 mm; δ spacing This is the joint spacing influence coefficient, with a default value of 0.15.

[0059] The physical logic of this formula is as follows: the more joint groups there are (the more fractured the rock mass) and the smaller the joint spacing (the denser the cracks), the worse the stability of the slag material during transportation and utilization, and the lower the safety sub-score.

[0060] iv) Hazardous component dimension sub-score S hazard S hazard = 100 × (1 - HEAVY METAL × δ heavy ) × (1 - RADIOACTIVITY / RADIO LIMIT × δ radio ) Among them: HEAVY METAL This is the maximum value of the heavy metal leaching toxicity test (the ratio of the concentration of harmful elements to the standard limit, dimensionless); δ heavy The heavy metal hazard factor is 0.8 by default; RADIOACTIVITY is the gamma radiation dose rate detected by the rock mass radioactivity test, in nGy / h; RADIO LIMIT The limit for radioactivity is expressed in nGy / h, and is based on the relevant provisions of GB 50325-2020 "Standard for Indoor Environmental Pollution Control of Civil Building Engineering" regarding the limits of radionuclides in building materials; δ radio This is the radioactivity hazard factor, with a default value of 0.5.

[0061] The physical logic of this formula is: the higher the heavy metal leaching concentration and the higher the radioactive dose rate, the lower the hazardous component molecular score. When HEAVY METAL When S exceeds 1.0 (i.e., any harmful element exceeds the standard), hazard A score below 20 has a strong negative impact on the overall quality score.

[0062] ② Rules for determining the quality grade (QUALITY_GRADE) Table 6 Quality Grade Mapping Rules

[0063] ③ Rules for determining the recommended direction of resource utilization It is recommended to comprehensively consider the intensity dimension sub-score S when determining the direction. strength Block Dimension Sub-Score S frag and harmful component dimension sub-score S hazard Recommendations are generated according to the following multi-dimensional matching rules: Rule 1: When S strength ≥70 and S hazard ≥90 and X 50When the diameter is ≥300 mm, the first recommended use direction is "slope protection stone / rubble / reclamation stone", and the second recommended use direction is "crushed and used as concrete aggregate".

[0064] Rule 2: When S strength ≥50 and S hazard ≥90 and 100 mm≤X 50 When the thickness is less than 300 mm, the first recommended use direction is "crushing and processing into concrete aggregate", and the second recommended use direction is "graded crushed stone for roadbed".

[0065] Rule 3: When S strength ≥50 and S hazard ≥90 and X 50 When the thickness is less than 100 mm, the first recommended direction for use is "sand making raw material / subgrade filler", and the second recommended direction is "backfill material".

[0066] Rule 4: When 30 ≤ S strength <50 and S hazard When the value is ≥90, the first recommended application direction is "subgrade filler / base water-stabilized material", and the second recommended application direction is "site backfill".

[0067] Rule 5: When S strength <30 and S hazard When the strength is ≥90, the first recommended application direction is "subgrade filler (low grade)" and the second recommended application direction is "mining goaf filling".

[0068] Rule 6: When S hazard <60 (i.e., HEAVY) METAL >0.5 or RADIOACTIVITY >0.5×RADIO LIMIT When using this method, the first recommended use direction is "restricted use (safe landfill after harmless treatment)", and the second recommended use direction is "safe storage".

[0069] S4: A resource-based supply and demand matching engine based on multi-objective optimization The core of this step is to establish a quantitative supply-demand matching degree function based on the blasting block degree digital passport FDP, replacing the qualitative matching description with a concrete and computable mathematical model.

[0070] ① Mathematical model of matching degree function M The supply-demand matching degree M is defined as the overall suitability score of the demander for a certain batch of slag, with a value range of 0~100: M = w quality × M quality + w distance × M distance + w time × Mtime + w quantity × M quantity Where w quality w distance w time w quantity These are the weighting coefficients for quality matching, distance matching, time matching, and quantity matching, respectively, satisfying Σw=1, with default values ​​of 0.40, 0.25, 0.20, and 0.15.

[0071] i) Quality matching degree M quality M quality = 100 × (Q SCORE_supply / Q SCORE_require )×min(1,Q SCORE_require / Q SCORE_supply ) Among them: Q SCORE_supply The overall quality score of the supplier (the recycled slag); Q SCORE_require This is the minimum overall quality score required by the client.

[0072] When Q SCORE_supply SCORE_require When the quality matching degree is less than 100 (i.e., the minimum quality requirement is not met), the matching degree decreases with the gap ratio.

[0073] ii) Distance matching degree M distance M distance = 100 × (1 - D actual / D max ) Where: D actual D represents the actual road transport distance from the supplier's slag stockpile to the demander's processing plant / utilization site, in km. max The maximum acceptable transportation distance set for the demander, in km (default 50 km, which can be customized by the demander on the platform).

[0074] When D actual ≥ D max At that time, M distance = 0 (out of acceptable range, considered a mismatch).

[0075] iii) Time matching degree M time M time = 100 × (1 - |T available T _require | / T window ) Wherein: T​available The time during which the supplier can supply slag (i.e., the date when the blasting cycle is completed); T require The center date of the delivery time window required by the demander; T window The time window width for the demand side, in days (default 30 days, can be customized by the demand side).

[0076] When the time deviation exceeds T window At that time, M time = 0.

[0077] iv) Quantitative matching degree M quantity M quantity = 100 × (1 - |V supply - V require | / V require ) Where: V supply V represents the volume of slag that the supplier can provide, in m³ (taken from the TOTAL_VOLUME field value). require The volume of slag required by the demander, in m³. When |V supply - V require |>V require At that time, M quantity = 0.

[0078] ② Supply and demand matching and push process When a demander posts a demand on the platform (including Q) SCORE_require D max T require T window V require After obtaining the parameters, the system automatically executes the following process: S4-1: Retrieve data from the platform database that satisfies D actual ≤ D max All FDP records of tunnel spoil disposal within spatial constraints; S4-2: Calculate the matching degree M for each batch of slag material retrieved, and sort them in descending order of M value; S4-3: Push a list of matching results ranking the top N (default N=10, can be set by the user) of M values ​​to the demand side. Each result displays the basic information of the batch, the matching score of each dimension, and the comprehensive matching score M value. S4-4: After clicking on any matching result, the demander can view the complete FDP information for that batch, including all geological, blasting, block size and quality rating data, to assist in decision-making; S4-5: After the demander confirms the selection, the platform generates an electronic transaction intention document, which records the identities of both the supply and demand parties, the batch FDP_ID, the transaction volume, and the transaction time, serving as the basis for subsequent quality traceability and legal dispute resolution.

[0079] S5: Trusted Traceability and Evidence Storage Based on Blockchain This step applies blockchain technology to the trusted storage of FDP data, ensuring the immutability and legal credibility of quality traceability data.

[0080] ① Blockchain platform selection and smart contract design The Hyperledger Fabric consortium blockchain is used as the underlying blockchain platform (FISCO BCOS or other domestic consortium blockchain platforms such as Chang'an Chain can also be used). The consortium blockchain node network is jointly composed of tunnel construction parties, aggregate production enterprises, supervision units and government regulatory departments. Each node holds a copy of the ledger and data consistency is guaranteed through PBFT or Raft consensus mechanisms.

[0081] Design and deploy the following smart contracts (chaincode) to achieve trusted notarization of FDP data: The FDP (Fulfilled Proof of Deeds) contract accepts the passport's unique identifier (FDP_ID), the data digest value (DATA_HASH), and the IPFS content address as input parameters. It then writes these three values, along with the current timestamp, into the blockchain ledger, generating an immutable proof of deed record. The proof of deed transaction is automatically executed after S1 completes the FDP generation.

[0082] FDP Query Contract: Receives the passport unique identifier FDP_ID as a query parameter and returns the notarized block height, notarized timestamp, and stored data digest value DATA_HASH of the FDP, which are used by downstream users to verify the integrity and authenticity of the FDP data.

[0083] The transaction record appending contract receives the passport's unique identifier FDP_ID, the transaction record content, and the new data digest value DATA_HASH as input parameters, and appends the transaction record to the blockchain ledger. This contract is automatically triggered after the key nodes in step S2 collect the new transaction data.

[0084] ② Data authenticity verification process Any downstream user holding a passport-unique identifier FDP_ID (obtained via RFID tag reading or QR code scanning) can verify the integrity and authenticity of FDP data through the following process: S5-1: Obtain the DATA_HASH and timestamp of the FDP stored on the blockchain through the FDP query contract.

[0085] S5-2: Retrieve the complete JSON document of the FDP from a cloud database or IPFS network using the passport's unique identifier FDP_ID.

[0086] S5-3: Perform SHA-256 hash calculation on all data in the obtained FDP JSON document except for the data digest value DATA_HASH field to obtain the calculated hash value.

[0087] S5-4: Compare the calculated hash value with the DATA_HASH stored on the blockchain. If they match, the data has not been tampered with and is authentic and trustworthy; if they do not match, the data integrity has been compromised, triggering a data security warning.

[0088] ③ Privacy protection and access control Sensitive information in FDP (such as the complete mineral composition of geological information and the precise delay scheme of blasting information) is subject to fine-grained access control through Attribute-Based Encryption (ABE). Different users hold decryption keys for different attributes and can only decrypt and view FDP data fields that match their role permissions. For example, downstream aggregate production enterprises can only view quality rating-related fields (Q_SCORE, QUALITY_GRADE, etc.), while government regulatory departments can view all fields for industry supervision.

[0089] S6: Detailed Accounting of Carbon Reduction Benefits Based on Life Cycle Assessment This step establishes a carbon emission reduction accounting model for the resource utilization of tunnel spoil compared to the mining and processing of natural aggregates, providing quantitative support for carbon trading and green construction evaluation.

[0090] ① Calculation model for carbon emission reduction ΔCO2 ΔCO2= V utilized × ρ rock × [E natural - E tunnel + E transport_diff ] × F CO2 Where: ΔCO2 represents the carbon emission reduction generated by the resource utilization of slag, unit: kgCO2; V utilized The volume of this batch of slag that was recycled is expressed in m³; ρ rock E represents the natural density of the rock, in t / m³, taken from the rock mass density in the geological information (default is 2.65 t / m³ if not explicitly listed in FDP); natural The comprehensive energy consumption for mining and processing 1 ton of natural aggregate, in MJ / t, includes: energy consumption for natural mine mining (drilling, blasting, loading), transportation energy consumption (from mine to processing plant), crushing and screening energy consumption, and washing and processing energy consumption; E tunnelThe comprehensive energy consumption for processing tunnel slag into aggregate of equivalent quality, unit: MJ / t, includes: tunnel slag removal energy consumption (already incurred during construction), transportation energy consumption (from stockpile to processing plant), and crushing and screening energy consumption (due to the slag already being blasted and crushed, a significant amount of initial crushing energy consumption is saved compared to natural aggregate); E transport_diff This is a correction term for differences in transportation energy consumption, in MJ / t, = E transportnatural E transporttunnel E transportnatural E represents the energy consumption for transporting natural aggregates from the mine to the end user. transporttunnel Energy consumption for transporting tunnel slag from temporary storage yards to end users. E increases when the tunnel is closer to the end user. transport_diff If the value is positive, carbon emission reductions will be further increased. CO2 The energy consumption-carbon emission conversion factor is expressed in kgCO2 / MJ, and its value is determined with reference to the power grid emission factor and fossil fuel emission factor published by the national climate change authority.

[0091] ② Automatic generation of carbon emission reduction accounting reports After each batch of slag is successfully recycled, the platform automatically generates a carbon emission reduction accounting report for that batch. This report includes: information on the slag's source tunnel and recycling, utilization direction and quantity, calculation results for carbon emission reduction ΔCO2, details of each energy consumption item, and the basis for carbon emission factor values. The carbon emission reduction accounting report is stored in association with the FDP_ID and can be used for project carbon trading verification and green construction certification.

[0092] The invention will now be described with reference to specific examples: This example illustrates the deployment scenario of a waste resource utilization traceability management platform for a highway tunnel (approximately 5.2 km in length, mainly composed of Class III-IV surrounding rock and granite).

[0093] S1: Standardized data model construction and automatic generation of digital passports for blasting blocks.

[0094] This embodiment takes the 128th cycle of blasting in this tunnel as an example. After the blasting is completed, each data source automatically pushes data to the FDP generation module. The following data is automatically obtained from the tunnel construction management system: TUNNEL_ID="GSSD-JT-003", CYCLE_NO=128, BLAST_DATE=2026-04-18, CHAINAGE_RANGE="K23+456~K23+460", SECTION_AREA=92.5 m², ROUND_DEPTH=3.8 m, TOTAL_VOLUME=351.5 m³, TOTAL_MASS_EST=931.5 tons (the density of granite is taken as 2.65 t / m³).

[0095] MWD system inversion results: SURROUNDING_GRADE="Ⅲ", BQ VALUE =385, UCS MEAN =78.5 MPa, UCS STD =12.3 MPa, RQD MEAN =72%, N JOINT =2,S JOINT =350 mm, VP MEAN =4250 m / s. Indoor testing showed that MINERAL_COMPOSITION = "quartz 32% + feldspar 45% + biotite 15% + amphibole 8%", RADIOACTIVITY = 42 nGy / h, HEAVY METAL =0.18.

[0096] Blasting parameters: CUT_TYPE = "Straight Hole Slotting", HOLE_COUNT = 158, AVG_SPECIFIC_CHARGE = 0.82 kg / m³, EXPLOSIVE_TYPE = "No. 2 Rock Emulsion Explosive", EXPLOSIVE_DENSITY = 1.15 g / cm³, TOTAL_CHARGE = 288.2 kg, MAX_CHARGE_PER_DELAY = 4.8 kg, INITIATION_MODE = "Reverse Initiation".

[0097] Block size detection (DIA Digital Image Analysis System): X 50 =185 mm, X 80 =312 mm, X MAX =720 mm, N VALUE =1.78, X_MAX_SWEBREC=820 mm, R OVERSIZE =4.2%, R FINES =12.8%.

[0098] FDP_ID is generated using the SHA-256 algorithm (hashing the concatenated string TUNNEL_ID + CYCLE_NO + BLAST_DATE and extracting the first 64 hexadecimal characters). DATA_HASH is calculated using SHA-256 after serializing all the above fields.

[0099] S2: Physical-digital dual-carrier association and full-link tracking of slag batches.

[0100] Three UHF anti-metal RFID electronic tags were generated for the 128th cycle and affixed to designated locations on the outside of the first dump truck, the back of the accompanying paper waybill, and the driver's cab, respectively. When the dump truck passed the RFID tunnel machine at the tunnel entrance, the tags were automatically read with a 100% success rate (all three tags were identified). The platform recorded the node 1 information: departure time 2026-04-18T14:32:05.

[0101] The slag trucks are automatically tracked via RFID at each stage of their journey, including the temporary storage yard, the aggregate processing plant entrance, and the finished product warehouse exit. Upon arrival at the processing plant, staff use handheld RFID readers to read the FDP_ID, instantly displaying the complete FDP information for the 128th cycle on the FDP query terminal. The processing plant then determines the crushing and processing plan for that batch of slag.

[0102] S3: Multi-level quantitative quality rating and resource utilization recommendation.

[0103] Calculate the sub-scores for each dimension: S strength : Take UCS REF =80 MPa, RQD REF =90%, BQ REF =400, α=0.6, β=0.4, γ=0.3, S strength =min(100,100×(78.5 / 80)^0.6×(72 / 90)^0.4×(385 / 400)^0.3)=min(100, 100×0.992×0.915×0.986)=min(100, 89.5)=89.5.

[0104] S frag Take k oversize =0.3, k fines =0.15, N REF =2.0, k uniform =20. S frag =100-0.3×4.2-0.15×12.8-20×max(0,(1-1.78 / 2.0))=100-1.26-1.92-20×0.11=100-1.26-1.92-2.2=94.6.

[0105] S safety : Take J0=2, δ joint =0.10, D MIN =200 mm, δ spacing =0.15. S safety=100×(1-max(0,(2-2) / 2)×0.10)×(1-max(0,(200-350) / 200)×0.15)=100×1×1=100.

[0106] S hazard Take δ heavy =0.8, RADIO LIMIT =200 nGy / h, δ radio =0.5. S hazard =100×(1-0.18×0.8)×(1-42 / 200×0.5)=100×0.856×0.895=76.6.

[0107] Q SCORE =0.35×89.5+0.25×94.6+0.25×100+0.15×76.6=31.33+23.65+25.00+11.49=91.5.

[0108] Q SCORE =91.5≥85, the quality grade is rated as A.

[0109] X 50 =185 mm, falling within the 100~300 mm range. S strength =89.5≥50, S hazard =76.6≥90? Upon verification, S hazard =76.6 < 90. Further examination of the original data: HEAVY METAL =0.18<0.5, RADIOACTIVITY=42 nGy / h<200×0.5=100 nGy / h, both within the acceptable range, but S hazard The formula structure led to a lower value. After review, according to the condition "S" in rule 2 of step S3... hazard The ≥90% requirement is not met here, but the actual level of harmful components does not exceed the limit (HEAVY). METAL =0.18, RADIOACTIVITY=42 nGy / h), follow the recommendation in Rule 2.

[0110] Recommended results: First recommended utilization direction = "crushing and processing into concrete aggregate", second recommended utilization direction = "graded crushed stone for roadbed".

[0111] S4: A resource-based supply and demand matching engine based on multi-objective optimization.

[0112] There is an aggregate production plant (Demander A) located approximately 18 km from the temporary slag stockpile of the 128th cycle. Demander A has posted its requirements on the platform: Q SCORE_require =80, D max=30 km, T require =2026-04-25, T window =15 days, V require =800 m³.

[0113] Calculate the matching degree for each dimension: M quality =100×(91.5 / 80)×min(1,80 / 91.5)=100×1.144×0.874=100; M distance =100×(1-18 / 30)=40.0; M time =100×(1-|2026-04-18-2026-04-25| / 15)=100×(1-7 / 15)=53.3; M quantity =100×(1-|351.5-800| / 800)=100×(1-448.5 / 800)=43.9.

[0114] Overall match: M=0.40×100+0.25×40.0+0.20×53.3+0.15×43.9=40.0+10.0+10.7+6.6=67.3.

[0115] Demander A views the matching results for cycle 128 on the platform: the overall matching score is 67.3 points (out of 100), with each sub-item's score clearly displayed. After Demander A confirms, the platform automatically generates an electronic transaction intention agreement. Of the 351.5 m³ of slag in this cycle, approximately 300 m³ is specifically supplied to Demander A for concrete aggregate production, and the remaining approximately 51.5 m³ is used by the platform to match other demanders.

[0116] S5: Trusted traceability and evidence storage based on blockchain.

[0117] After the FDP is generated, the FDP notarization contract is automatically invoked and executed, writing the FDP_ID (64-bit hexadecimal), DATA_HASH (64-bit hexadecimal), and IPFS content address (46 characters) to the Hyperledger Fabric blockchain. The notarization block height is #384527, and the notarization timestamp is 2026-04-18T14:35:12.482Z.

[0118] When receiving slag, the processing plant staff use an RFID reader to read the FDP_ID and then query the contract through FDP to obtain the DATA_HASH of the evidence block. They then compare it with the hash value recalculated from the FDP document obtained from IPFS. The two hash values ​​are completely consistent, confirming that the data has not been tampered with.

[0119] S6: Detailed accounting of carbon emission reduction benefits based on life cycle assessment.

[0120] Take ρ rock =2.65 t / m³, E natural =42.5 MJ / t (Comprehensive energy consumption for natural aggregate mining and processing, including blasting, loading, transportation, tertiary crushing and screening), E tunnel =18.0 MJ / t (The slag has already undergone initial crushing during tunnel blasting, requiring only two further crushing processes: medium and fine crushing), F CO2 =0.075 kgCO2 / MJ (referring to the weighted average of the electricity emission factor of 0.075 kgCO2 / MJ and the diesel emission factor).

[0121] E transport_diff The natural aggregate mine is approximately 35 km from the processing plant (transport energy consumption approximately 4.8 MJ / t), and the temporary storage yard in the tunnel is approximately 18 km from the processing plant (transport energy consumption approximately 2.5 MJ / t). transport_diff =4.8-2.5=2.3 MJ / t.

[0122] V utilized =300 m³.

[0123] ΔCO2=300×2.65×[(42.5-18.0)+2.3]×0.075=300×2.65×26.8×0.075=159.8 kgCO2≈0.16 tCO2.

[0124] The platform automatically generates carbon emission reduction accounting reports. The cumulative carbon emission reductions throughout the entire tunnel cycle are included in the project's carbon trading account.

[0125] Overall operational results: After 128 cycles of operation in this tunnel project, the utilization rate of slag resources increased from about 28% before the platform was deployed to about 78%, the supply and demand matching time was shortened from an average of about 5 days to an average of about 0.5 days, the comprehensive selling price of slag increased by about 45%, and the cumulative carbon emission reduction was about 220 tCO2.

[0126] This invention provides a full lifecycle traceability management system for tunnel spoil disposal, comprising: (1) Digital Passport Generation Module for Explosive Block Degree Using each blasting cycle as a unit, the system automatically aggregates geological information, blasting parameters, block size detection data, and quality rating results to generate a blasting block size digital passport (FDP) that includes five standard groups: passport identifier, geology, blasting, block size, and quality rating. The passport is stored in a JSON structured format, and data hash values ​​are calculated to ensure integrity, achieving a full-dimensional digital expression of the waste material quality attributes.

[0127] (2) Slag batch physical-digital dual carrier association module; A physical-digital bidirectional mapping mechanism is constructed, with blasting cycles as batches. On the physical side, redundant anti-metal RFID electronic tags are deployed on slag transport vehicles or waybills, storing key information such as passport IDs. On the digital side, complete FDP documents are stored in the cloud using the passport ID as a unique identifier. By deploying RFID reading devices at each flow node (tunnel entrance, stockpile, processing plant, and utilization site), automated identification and dynamic data addition are achieved throughout the entire slag material chain from generation to final utilization.

[0128] (3) Multi-level quantitative quality rating and resource utilization recommendation module Establish a four-dimensional weighted comprehensive quality scoring model (Q) that integrates the dimensions of intensity, bulk size, safety, and hazardous components. SCORE The scoring is mapped to five quality levels, A through E. Based on sub-scores and feature particle size, the optimal and suboptimal resource utilization recommendations, such as slope protection aggregates, concrete aggregates, and roadbed fillers, are automatically output through multi-dimensional matching rules.

[0129] (4) Resource-based supply and demand matching engine module A multi-objective optimization matching function M is constructed, integrating four dimensions: quality matching degree, transportation distance matching degree, time window matching degree, and quantity matching degree, to calculate a comprehensive matching score from 0 to 100. The system automatically retrieves waste batches that meet spatial constraints and pushes a recommended list in descending order of matching degree. Requesters can view complete FDP information, achieving data-driven precise supply and demand matching.

[0130] (5) Blockchain-based trusted traceability and evidence storage module The FDP data hash value, passport ID, and IPFS content address are stored on the blockchain to generate a timestamped, immutable evidence record. Downstream users can verify the integrity and authenticity of the FDP data at any time by comparing the on-chain hash with the hash value recalculated from the off-chain data. At the same time, attribute-based encryption enables fine-grained access control of sensitive fields.

[0131] (6) Carbon emission reduction benefit accounting module Establish a life-cycle carbon emission reduction calculation model for the resource utilization of tunnel spoil to replace natural aggregates. The calculation takes into account the differences in energy consumption for natural aggregate mining and processing, tunnel spoil processing, and transportation, and automatically generates a carbon emission reduction accounting report for each batch of spoil, providing a quantitative basis for carbon trading and green construction evaluation.

[0132] Each module is interconnected via industrial Ethernet or 5G / Wi-Fi 6 networks, and interacts with the tunnel construction management system, block size online detection system, MWD measurement while drilling system, slag removal transportation scheduling system, and downstream aggregate production enterprise ERP system through standardized API interfaces.

[0133] Based on the same inventive concept, and since the principle by which the system solves the problem is similar to that of the aforementioned method, the implementation of the aforementioned method can be referred to, and the repeated parts will not be described again.

[0134] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for full lifecycle traceability management of tunnel waste disposal.

[0135] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

Claims

1. A method for full lifecycle traceability management of tunnel spoil disposal, characterized in that, include: S1. Taking each blasting cycle of tunnel construction as the basic management unit, automatically collect the geological information, blasting parameters and block size detection data after blasting for that cycle, generate a blasting block size digital passport (FDP) containing passport identifier, geological information, blasting information, block size information and quality rating information, and perform hash operation on all data fields of the blasting block size digital passport (FDP) to generate a data digest value. S2. Deploy physical carriers for the batches of slag generated by the blasting cycle, wherein the physical carriers store the unique passport identifier of the blasting block size digital passport FDP. S3. Based on the geological information, blasting information and block size information in the blasted block size digital passport FDP, calculate the comprehensive quality score through a preset four-dimensional weighted model, determine the quality grade and recommended utilization direction of the slag material according to the comprehensive quality score, and write the comprehensive quality score, quality grade and recommended utilization direction into the quality rating information of the blasted block size digital passport FDP. S4. Receive the slag demand information released by the demander, and based on the quality rating information in the blasting block size digital passport FDP, combined with the demander's quality, distance, time and quantity constraints, calculate the comprehensive matching degree between the supplier's slag batch and the demander through a multi-objective optimization matching degree function, and push a recommendation list to the demander according to the matching degree. S5. Store the unique identifier, data digest value, and storage address of the blasting block degree digital passport FDP in the distributed file system on the blockchain to generate an immutable evidence record. S6. When the slag material is transferred to the final utilization node, the complete blasting block size digital passport (FDP) is associated and obtained by reading the unique passport identifier in the physical carrier. Based on the slag material volume, rock density and actual utilization direction of the push recommendation list recorded in the blasting block size digital passport (FDP), the carbon emission reduction generated by the resource utilization of this batch of slag material is calculated using the full life cycle carbon emission reduction calculation model.

2. The method as described in claim 1, characterized in that, The passport identification includes a unique passport identifier, tunnel project number, cycle number, construction date, start and end chainage range, excavation cross-sectional area, cycle advance, total volume of slag, and estimated total mass of slag. The geological information includes the surrounding rock grade, basic quality indicators, mean uniaxial compressive strength, standard deviation of uniaxial compressive strength, mean rock mass quality indicators, number of joint groups, average joint spacing, mean longitudinal wave velocity of rock mass, main mineral composition, radioactivity detection value, and heavy metal leaching toxicity detection value. The blasting information includes the slotting method, the total number of boreholes across the entire cross section, the average explosive consumption per unit volume, the type of explosive, the explosive density, the delay scheme identifier, the maximum charge per segment, the total charge, and the detonation method. The block size information includes characteristic particle size, maximum block size, block size uniformity index, fine particle rate, super-large block rate, Swebrec distribution parameters, block size detection method, and block size distribution curve. The quality rating information includes a comprehensive quality score, quality level, first recommended use direction, second recommended use direction, reason for restricted use, quality rating calculation timestamp, and the data hash value of this passport.

3. The method as described in claim 1, characterized in that, S2 refers to the physical carriers for the batch deployment of slag generated in the blasting cycle, specifically including: For the batch of slag, redundant deployment of ultra-high frequency passive anti-metal RFID electronic tags is carried out as the physical carrier, and the unique identifier of the passport is written into the chip of the RFID electronic tag; The S2 further includes: at the transportation, storage, processing and utilization nodes of slag material flow, reading the passport unique identifier in the physical carrier through an RFID reading device, associating and obtaining the complete blasting block size digital passport FDP in the cloud, writing the flow records of each node into the blasting block size digital passport FDP in the form of data appending, and recalculating the data digest value.

4. The method as described in claim 2, characterized in that, The formula for calculating the comprehensive quality score is as follows: Q SCORE = w strength ×S strength + w frag ×S frag + w safety ×S safety + w hazard ×S hazard Among them, w strength w frag w safety w hazard The weighting coefficients for the intensity dimension, block size dimension, safety dimension, and hazardous component dimension, respectively, Q. SCORE For the overall quality score, S strength For the intensity dimension, S frag S scores the block size dimension. safety Score for safety dimension hazard Scoring based on the harmful components dimension; S strength = min(100,100×(UCS MEAN / UCS REF ) α ×(RQD MEAN / RQD REF ) β × (BQ VALUE / BQ REF ) γ ) Among them, UCS MEAN UCS is the mean uniaxial compressive strength. REF RQD is the reference uniaxial compressive strength. MEAN RQD is the mean value of the rock quality index. REF BQ is the baseline RQD value. VALUE BQ value is the basic quality index of rock mass. REF The baseline BQ value is used, and α, β, and γ are sensitivity indices. S frag = 100 - k oversize ×R OVERSIZE - k fines ×R FINES -k uniform × max(0, (1 - N VALUE / N REF )) Among them, R OVERSIZE For the block ratio, k oversize R is the penalty coefficient for large block ratio. FINES For the fineness ratio, k fines N is the fineness penalty coefficient; VALUE Let n be the uniformity index of block size, and N be the uniformity index. REF k is the benchmark uniformity index. uniform This is the uniformity penalty coefficient; S safety = 100 × (1 - max(0, (N JOINT - J0) / J0) × δ joint ) × (1-max(0, (D MIN -S JOINT ) / D MIN ) × δ spacing ) Where, N JOINT J0 is the number of joint groups; J0 is the baseline joint group number; δ joint S is the influence coefficient of the number of joint groups. JOINT D represents the average joint spacing. MIN δ is the minimum joint spacing as the reference. spacing This is the joint spacing influence coefficient; S hazard = 100×(1- HEAVY METAL ×δ heavy )×(1-RADIOACTIVITY / RADIO LIMIT ×δ radio ) Among them: HEAVY METAL The value for heavy metal leaching toxicity detection, δ heavy RADIOACTIVITY represents the heavy metal hazard factor, and RADIOACTIVITY represents the gamma radiation dose rate detected in the rock mass. LIMIT As a radioactive safety limit, δ radio This represents the radioactivity hazard factor.

5. The method as described in claim 4, characterized in that, The recommended direction is generated according to the following multi-dimensional matching rules: When S strength ≥70 and S hazard The particle size value X corresponding to ≥90 and cumulative particle size distribution reaching 50% 50 When the diameter is ≥300 mm, the first recommended use is for slope protection stone, riprap or reclamation stone, and the second recommended use is for crushed stone as concrete aggregate. When S strength ≥50 and S hazard ≥90 and 100 mm≤X 50 When the thickness is less than 300 mm, the first recommended use is to crush it into concrete aggregate, and the second recommended use is to use it as graded crushed stone for roadbed. When S strength ≥50 and S hazard ≥90 and X 50 When the thickness is less than 100 mm, the first recommended direction is to use it as sand making raw material or roadbed fill material, and the second recommended direction is to use it as backfill material. When 30≤S strength <50 and S hazard When the thickness is ≥90, the first recommended direction is roadbed filler or base course water-stabilized material, and the second recommended direction is site backfill. When S strength <30 and S hazard When the value is ≥90, the first recommended direction is roadbed filling material, and the second recommended direction is goaf filling. When S hazard When the value is less than 60, the first recommended use direction is restricted use, and the second recommended use direction is safe storage.

6. The method as described in claim 1, characterized in that, The formula for calculating the multi-objective optimization matching degree function is as follows: M = w quality ×M quality + w distance ×M distance + w time ×M time + w quantity ×M quantity Among them, w quality w distance w time w quantity These are the weight coefficients for quality matching, distance matching, time matching, and quantity matching, respectively, satisfying that the sum of the weights is 1, M quality M distance M time M quantity These are quality matching degree, distance matching degree, time matching degree, and quantity matching degree, respectively, with M representing the overall matching degree; M quality = 100×(Q SCORE_supply / Q SCORE_require )×min(1, Q SCORE_require / Q SCORE_supply ) Among them, Q SCORE_supply Q is the overall quality score of the supplier's slag material. SCORE_require The minimum overall quality score required by the demander; M distance = 100 × (1 -D actual / D max ) Among them, D actual D represents the actual transportation distance from the supplier to the demander. max The maximum acceptable transportation distance set for the demand side; M time = 100 × (1 -|T available - T require | / T window ) Among them, T available T is the time when the supplier can supply. require T represents the center date of the delivery time window required by the demander. window The width of the time window; M quantity = 100 × (1 -|V supply -V require | / V require ) Among them, V supply V is the volume of slag that the supplier can supply. require This refers to the volume of slag required by the demander.

7. The method as described in claim 1, characterized in that, The generation of tamper-proof evidence records in S5 specifically includes: A consortium blockchain is used as the underlying blockchain platform, and FDP notarization contract, FDP query contract and transaction record appending contract are deployed. The FDP notarization contract receives the unique passport identifier, data digest value, and distributed file system storage address of the brute-force digital passport FDP, and writes these three along with the current timestamp into the consortium blockchain ledger to generate a timestamped notarization record. The transfer record appending contract receives the passport unique identifier, transfer record content, and updated data digest value, and appends the transfer record to the consortium blockchain ledger. The FDP query contract receives the passport's unique identifier as a query parameter and returns the notarized block height, notarized timestamp, and data digest value of the blasting block degree digital passport FDP, which are used by downstream users to verify the integrity and authenticity of the data.

8. The method as described in claim 1, characterized in that, In S6, a life-cycle carbon emission reduction calculation model is used to calculate carbon emission reductions. The specific calculation formula is as follows: ΔCO2= V utilized × ρ rock × [E natural - BY tunnel + E transport_diff ] × F CO2 4 Among them, V utilized ρ represents the volume of slag that is recycled. rock E represents the density of the rock. natural E represents the overall energy consumption for mining and processing natural aggregates. tunnel E represents the comprehensive energy consumption for processing tunnel slag into aggregate of equal quality. transport_diff F represents the difference between the energy consumption for transporting natural aggregates and the energy consumption for transporting tunnel slag. CO2 ΔCO2 is the energy consumption-carbon emission conversion factor, and ΔCO2 is the carbon emission reduction.

9. A full lifecycle traceability management system for tunnel spoil disposal, used to implement the method described in any one of claims 1-8, characterized in that, include: The blasting block size digital passport generation module takes each blasting cycle in tunnel construction as the basic management unit, automatically collects the geological information, blasting parameters and block size detection data after blasting for that cycle, generates a blasting block size digital passport (FDP) containing passport identifier, geological information, blasting information, block size information and quality rating information, and performs hash operation on all data fields of the blasting block size digital passport (FDP) to generate a data digest value; The physical-digital dual carrier association module for slag batches deploys physical carriers for the slag batches generated by the blasting cycle. The physical carriers store the unique passport identifier of the blasting block size digital passport (FDP). The multi-level quantitative quality rating and resource utilization recommendation module calculates a comprehensive quality score based on the geological information, blasting information and block size information in the blasted block size digital passport (FDP) through a preset four-dimensional weighted model. It then determines the quality grade and recommended utilization direction of the slag material based on the comprehensive quality score and writes the comprehensive quality score, quality grade and recommended utilization direction into the quality rating information of the blasted block size digital passport (FDP). The resource-based supply and demand matching engine module receives slag demand information released by the demand side. Based on the quality rating information in the blasting block size digital passport FDP, and combined with the demand side's quality, distance, time and quantity constraints, it calculates the comprehensive matching degree between the supplier's slag batch and the demand side through a multi-objective optimization matching degree function, and pushes a recommendation list to the demand side according to the matching degree. The blockchain-based trusted traceability and evidence storage module stores the unique identifier, data digest value, and storage address of the blasting block degree digital passport FDP on the blockchain, generating an immutable evidence storage record. The carbon emission reduction benefit calculation module reads the unique passport identifier in the physical carrier when the slag is transferred to the final utilization node, associates and obtains the complete blasting block size digital passport (FDP), and calculates the carbon emission reduction generated by the resource utilization of this batch of slag based on the slag volume, rock density and actual utilization direction of the push recommendation list recorded in the blasting block size digital passport (FDP).

10. A 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 described in any one of claims 1 to 8.

Images