Building waste classification and cyclic utilization tracking method oriented to carbon neutralization

Abstract

The invention belongs to the technical field of carbon footprint tracking management, and relates to a carbon neutralization-oriented building waste classification and cyclic utilization tracking method, which comprises the following steps of generating a physical-digital anchoring voucher bound with a source tracing data packet based on the obtained source tracing data packet; based on the physical-digital anchoring voucher, generating a waste tracing data object which records theoretical carbon emission reduction potential and is in a non-activated state; generating a transportation process consistency log based on the waste tracing data object; generating a terminal cross validation report based on the transportation process consistency log and the waste tracing data object; on the basis of the terminal cross validation report, updating the waste traceability data object by calculating the verified actual carbon emission reduction, and generating a verified emission reduction verification data packet; according to the method, the problems that the whole process lacks an anti-counterfeiting and tracing mechanism and the standardized development of the construction waste recycling industry is restricted due to the dispersity and tampering easiness of related information of the construction waste are solved.

Description

Technical Field

[0001] This invention belongs to the technical field of carbon footprint tracking and management, and relates to a method for classifying and recycling construction waste for carbon neutrality. Background Technology

[0002] The construction industry is currently facing the dual pressures of construction waste management and carbon emission reduction. During urbanization, a massive amount of construction waste is generated annually. The entire process of its processing, transportation, and reuse often lacks effective information transparency and quality monitoring mechanisms. Waste is prone to adulteration, substitution, or misreporting during its circulation, making it impossible to accurately verify the quantity and quality of waste actually recycled. Accurate data quantification and verification of carbon emission reductions generated by the recycling of construction waste has become an urgent need. However, existing certification systems mainly rely on self-reported data from processing plants and simple document records, which are unreliable and easily tampered with.

[0003] Currently, the industry generally uses a combination of traditional paper documents and manual reports. The entire process of waste from the source to the treatment plant is mainly recorded by printing waybills, weighbridge slips and treatment reports. The transportation process relies on drivers' manual records or simple odometer data. The calculation of carbon emission reduction is reported by the treatment plant to the upstream department based on its own weighing and sorting results. The relevant information is stored in the data systems of different departments, making it difficult to form a unified and traceable information chain.

[0004] The fundamental drawback of this traditional approach is that the dispersed and easily tampered nature of information means that the entire process lacks necessary anti-counterfeiting and traceability mechanisms, which restricts the standardized development of the construction waste recycling industry. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for classifying and recycling construction waste for carbon neutrality.

[0006] A carbon-neutral approach to classifying and recycling construction waste includes the following steps: S1. Obtain the source tracing data packet containing the identity identifier and theoretical carbon emission reduction value; S2. Obtain the digital twin model feature data of the physical sample of the waste, and activate the one-time smart lock and radio frequency identification chip on the sealed chamber of the physical sample, bind the chip identity information with the source traceability data packet, and generate a physical-digital anchoring certificate. S3. Based on physical-digital anchored credentials, generate waste traceability data objects that record theoretical carbon reduction potential and are inactive. S4. Generate a consistent log of the transportation process based on the waste traceability data object; S5. Generate terminal cross-validation reports based on transportation process consistency logs and waste traceability data objects; S6. Based on the terminal cross-validation report, update the waste traceability data object by calculating the verified actual carbon emission reduction, and generate a verified emission reduction verification data package.

[0007] A further aspect of this invention involves generating a source tracing data packet, comprising the following steps: For pre-classified batches of construction waste to be processed, collect their associated spatiotemporal stamp-environmental fingerprint composite information; Based on the pre-selected waste type and estimated quantity, and combined with spatiotemporal stamp-environmental fingerprint composite information, a source traceability data package is generated.

[0008] A further aspect of the present invention generates waste traceability data objects, including the following steps: Extract pairing information and digital twin model feature data from physical-digital anchored credentials; The pairing information and digital twin model feature data are associated with the source tracing data package and written together into the data ledger entry; Based on the data ledger entries, construct waste traceability data objects.

[0009] A further aspect of this invention involves generating a consistent log of the transportation process, comprising the following steps: Retrieve the estimated weight information of waste recorded in the waste traceability data object, and combine it with vehicle model and planned route to establish a multi-dimensional energy consumption prediction model that includes expected fuel consumption curve, load change curve and normal tire pressure range. During transportation, real-time data on the vehicle's engine load, cargo box weight, and tire pressure are collected. The actual energy consumption data is continuously compared with the multidimensional energy consumption prediction model, and the comparison results, geographical location information along the route, and abnormal fluctuation events are recorded as a transportation process consistency log.

[0010] A further aspect of this invention involves generating a terminal cross-validation report, comprising the following steps: Verify the status of the smart lock with the physical-digital anchoring credential and read its radio frequency identification chip information; Physical samples inside the cabin were scanned, and their morphology was compared with the digital twin model recorded in the waste traceability data object; By combining the data recorded in the consistency log of the transportation process, the integrity and authenticity of the waste batch flow process are comprehensively evaluated, and a terminal cross-validation report is generated.

[0011] A further aspect of the present invention generates a verified emissions reduction verification data package, comprising the following steps: After the terminal cross-validation report confirms that there are no errors, the waste batch is weighed, sorted and recycled to obtain the actual type and quantity of recycled materials produced. The system queries a pre-defined carbon reduction factor database and calculates the verified actual carbon reduction based on the type and quantity of recycled materials actually produced. Update the emission reduction values ​​of the waste traceability data object with verified actual carbon emission reductions and change its status to activated to generate a verified emission reduction verification data package.

[0012] In a further embodiment of the present invention, the spatiotemporal stamp includes geographic coordinate data obtained through a satellite positioning system and current date and time data generated by the system; the environmental fingerprint includes the appearance of the batch of construction waste to be processed itself.

[0013] A further aspect of the present invention involves forming pairing information, including the following steps: Read the identity information of the activated radio frequency identification chip; After receiving the chip's identity information, it is bound to the unique identifier of the source tracing data packet, and the pairing relationship is recorded to generate pairing information.

[0014] A further aspect of the present invention involves scanning and comparing physical samples inside the cabin, including the following steps: Using 3D scanning equipment with the same precision as that used during source acquisition, the physical samples inside the cabin were scanned to generate a new 3D model; The morphological features of the new 3D model are compared with the original digital twin model corresponding to the waste traceability data object previously stored. Analyze the key geometric feature points, curvature, and surface texture distribution of the two models, and calculate their similarity.

[0015] In summary, the present invention has the following beneficial technical effects: 1. By establishing a mandatory collection mechanism of spatiotemporal stamps and environmental fingerprints at the source of waste, and introducing a combination of digital twin models of physical samples and one-time smart locks, each batch of waste is given a unique and anti-counterfeiting identity certificate. This identity certificate can accompany the waste throughout the entire process from source to end, making it difficult for any link in the chain to be adulterated or tampered with, thus establishing a waste management system that is traceable from source to end.

[0016] 2. By collecting multi-dimensional energy consumption data of vehicles in real time during transportation and continuously comparing it with a pre-established theoretical model, abnormal situations during transportation are accurately recorded and marked. These transportation logs, combined with the integrity verification of physical samples, form a multi-verification mechanism for the authenticity of waste batches, reducing violations such as tampering during transportation and ensuring the consistency between the waste entering the treatment plant and the waste recorded at the source.

[0017] 3. The calculation of carbon emission reductions has been transformed from traditional one-way reporting to accounting based on actual processing results. By matching the actual quantity of recycled materials after weighing and sorting with the carbon emission reduction factor database, the objectivity and credibility of the verified actual carbon emission reductions have been improved. This value is then written into an immutable data ledger and a verified emission reduction verification data package is generated. This gives the carbon emission reduction data a reliable and immutable evidentiary basis that can be trusted by the participants, thus providing a credible, transparent, and standardized operating direction for the carbon-neutral construction waste recycling industry. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings are used to provide a further understanding of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating an embodiment of this application is disclosed.

[0020] Figure 2 Structural schematic diagrams of embodiments of this application are disclosed. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The following is in conjunction with the appendix Figure 1 - Figure 2 A preferred description of the present invention is provided below.

[0023] See attached document Figure 1 This invention proposes a method for classifying and recycling construction waste for carbon neutrality, including the following steps: S1. Obtain the source tracing data packet containing the identity identifier and theoretical carbon emission reduction value; S2. Obtain the digital twin model feature data of the physical sample of the waste, and activate the one-time smart lock and radio frequency identification chip on the sealed chamber of the physical sample, bind the chip identity information with the source traceability data packet, and generate a physical-digital anchoring certificate. S3. Based on physical-digital anchored credentials, generate waste traceability data objects that record theoretical carbon reduction potential and are inactive. S4. Generate a consistent log of the transportation process based on the waste traceability data object; S5. Generate terminal cross-validation reports based on transportation process consistency logs and waste traceability data objects; S6. Based on the terminal cross-validation report, update the waste traceability data object by calculating the verified actual carbon emission reduction, and generate a verified emission reduction verification data package. In one embodiment of the present invention, step S1 includes the following steps: For pre-classified batches of construction waste to be processed, collect their associated spatiotemporal stamp-environmental fingerprint composite information; based on the pre-selected waste type and estimated quantity, combine the spatiotemporal stamp-environmental fingerprint composite information to generate a source traceability data package.

[0024] Specifically, data credentials recording the initial state of construction waste that has already undergone preliminary sorting at the source will be generated—this is known as a source traceability data package. During implementation, on-site operators are first required to reach the designated physical location where the sorted construction waste has been placed, such as concrete blocks, scrap steel bars, or timber. Operators need to use a terminal device with a built-in positioning system module, a high-definition camera, and a specific application, such as a smartphone or industrial tablet.

[0025] Once the operator opens a specific application, the application will force the data collection process. This process includes the application automatically acquiring and recording a composite information of the current time point: a spatiotemporal stamp and an environmental fingerprint. This spatiotemporal stamp consists of two parts: one is the geographic coordinates obtained through the terminal device's positioning system module, i.e., GPS coordinates; the other is the current date and time recorded by the terminal device system, i.e., the operation timestamp. Simultaneously, the application will activate the camera, forcing the operator to record a short video as part of the environmental fingerprint. This short video must clearly capture the appearance of the batch of construction waste to be processed, such as the shape, color, and general composition of the waste pile. It must also be a panoramic shot, recording the surrounding environmental features of the waste pile, such as specific buildings and ground conditions in the background.

[0026] After collecting the spatiotemporal stamp-environmental fingerprint composite information, the application interface displays a list, allowing the operator to select the type of waste (e.g., concrete or scrap metal) based on the site conditions and input the estimated quantity (in tons or cubic meters). Once the operator confirms the input, the application's backend system performs the generation process. First, the system generates a globally unique string as a unique identifier to ensure this data packet is not confused with others. Next, based on the waste type selected by the operator, the system automatically queries the pre-set carbon reduction standard library within the system to find the maximum carbon reduction per unit mass achievable when recycling the corresponding type of waste. This value is then multiplied by the estimated quantity input by the operator to calculate the theoretical carbon reduction value. Finally, the system integrates this unique identifier, the theoretical carbon reduction value, and the previously collected spatiotemporal stamp-environmental fingerprint composite information, packaging them into a structured data file. This file is the final output source traceability data packet.

[0027] The term "batch of construction waste to be processed" refers to a certain amount of waste that has been initially separated and piled up at a construction or demolition site according to its material properties, such as concrete, bricks, metal, and wood. The terminal device is a portable computing device that integrates data acquisition, processing, and network communication capabilities, such as a smartphone with customized software. The spatiotemporal stamp-environmental fingerprint composite information is a data combination with two fields: a spatiotemporal stamp and an environmental fingerprint. The spatiotemporal stamp records the specific time and location of the operation, and its data structure consists of GPS coordinates and a time string in a standard format. The environmental fingerprint records visual information about the waste and its surrounding environment, and its data structure consists of a short video file in a specific encoding format. The waste type is a predefined classification label used to identify the main material of the waste; the data comes from industry-standard construction waste classification criteria, such as "C&D Waste-01" representing concrete fragments.

[0028] The estimated quantity is a preliminary estimate of the volume or weight of the waste batch based on the operator's experience, set according to on-site visual inspection, for example, an estimate of 10 tons. The unique identifier is a string ensuring it is unique throughout the system, generated by combining the current timestamp, device ID, and a random number, for example, "Trace20231026143055-Dev007-Rand5892". The theoretical carbon emission reduction value is a numerical value representing the maximum environmental benefit this batch of waste can contribute under ideal recycling conditions. It is set based on a carbon emission factor database published by an authoritative organization. For example, if the database stipulates that recycling 1 ton of concrete reduces emissions by 0.1 tons of CO2 equivalent, then the theoretical carbon emission reduction value for 10 tons of waste is 1 ton. The source traceability data package is a structured collection of data that serves as the starting archive for the waste batch's lifecycle. Its data structure is a JSON or XML file containing all the above information.

[0029] For example, suppose there is a batch of construction waste to be processed on site, such as discarded concrete blocks. An operator uses a terminal device to collect an environmental fingerprint at a specific time and geographical coordinates, say 2:30 PM on October 26, 2023, at a location of 116.4 degrees East longitude and 39.9 degrees North latitude. A 15-second video clip is collected as the environmental fingerprint. This video shows the shape of the concrete blocks and certain features in the background, such as a blue tower crane. This information together constitutes the spatiotemporal stamp-environmental fingerprint composite information. Next, the operator can select concrete as the waste type on the terminal device and enter an estimated quantity of 10 tons. The system then generates a unique identifier, such as "Trace20231026143055-Dev007-Rand5892," and calculates the theoretical carbon emission reduction value, such as 1 ton of CO2 equivalent, based on the built-in carbon emission reduction standard. Finally, this information is integrated to generate a source tracing data package.

[0030] In one embodiment of the present invention, step S2 includes the following steps: Physical samples are randomly selected from batches of construction waste to be processed, and digital twin models containing feature data of their shape contours and surface textures are created. The physical samples are placed in a sealed chamber with a one-time smart lock and locked to activate the radio frequency identification chip embedded in the smart lock. The identity information of the chip is then paired with the source tracing data packet to form pairing information. The combination of an activated one-time smart lock, its internal physical sample, the associated digital twin model, and pairing information constitutes a physical-digital anchoring credential.

[0031] Specifically, after generating the source traceability data package, the next step is to create an anti-counterfeiting credential combining physical and digital information for this batch of waste materials. This credential is the physical-digital anchored credential. During creation, the operator first randomly and unmodifiedly selects a physical sample from the batch of construction waste to be processed, such as an irregularly shaped concrete fragment. Next, the operator uses a device with 3D scanning capabilities—either a terminal device integrated with a depth camera or a dedicated handheld 3D scanner—to perform a comprehensive scan of the physical sample. This captures the 3D coordinates of data points on the sample's surface, which constitute the sample's shape and outline. Simultaneously, the high-definition camera on the device photographs the sample's surface, recording details such as color, cracks, and attachments, forming the surface texture. The scanning software processes and merges this shape and texture data, ultimately generating a 3D model file—the digital twin model of the physical sample—containing feature data that uniquely identifies the sample. This digital twin model is temporarily stored in the terminal device.

[0032] The operator then retrieves a specialized sealed compartment, typically a sturdy container designed to prevent tampering of its contents, featuring a key component: a one-time-use smart lock. The operator places the recently scanned physical sample into the compartment, closes and locks the door. This locking action is irreversible; once locked, the lock's structure is permanently altered, making it impossible to reopen without physical damage. Furthermore, this locking action triggers internal circuitry, powering and activating a pre-installed RFID chip. Once activated, the chip begins broadcasting its unique identification information. The operator then brings the previously used terminal device close to the locked smart lock. The terminal device, using its near-field communication (NFC) capability, reads the activated RFID chip's identification information. Upon receiving this chip identification information, the application binds it to the unique identifier in the source tracing data packet used at the beginning of this step and records this pairing in the system.

[0033] Finally, this locked, sealed compartment containing the physical sample serves as physical proof, its core being a one-time smart lock and an activated RFID chip. Firmly bound to it in the digital world is a digital twin model representing the unique geometric and textural features of the physical sample. These three elements—the activated smart lock containing the physical sample, the digital twin model corresponding to the sample within the lock, and the established pairing relationship between them—together constitute a complete physical-digital anchoring certificate. This certificate will circulate throughout the entire batch of waste, serving as the core basis for verifying authenticity in subsequent stages.

[0034] A physical sample refers to a representative and uniquely shaped physical waste sample randomly selected from a batch of waste materials, serving as a physical identification for the entire batch. 3D scanning is a technology that uses optical or laser technology to non-contactly acquire the three-dimensional geometry and appearance information of an object. A digital twin model is a digital 3D model file that is consistent with the physical sample in terms of geometry and surface texture. Its data structure is typically in OBJ or FBX format, containing vertex coordinate data and texture mapping files. Shape contour and surface texture feature data are the core information constituting the digital twin model; the former describes the object's three-dimensional spatial form, while the latter records its surface color and detailed patterns. A sealed compartment is a specially designed tamper-proof container used to protect the physical sample from being tampered with during transportation. A one-time smart lock is a special lock installed on the sealed compartment; once locked, it cannot be opened without damage, and the locking action triggers internal electronic components. A radio frequency identification (RFID) chip is a miniature passive chip embedded in the smart lock; its function is to transmit a pre-written, globally unique ID number via near-field radio waves when activated. Pairing information refers to data records that establish a one-to-one correspondence between the ID of a radio frequency identification (RFID) chip and the unique identifier of a source traceability data packet. Its data structure can be key-value pairs, such as "NFC_ID_A1B2C3" associated with "Trace20231026143055-Dev007-Rand5892". Physical-digital anchored credentials are composite credentials. Physically, they are represented by a sealed lock containing the sample; digitally, they are represented by a data set containing a digital twin model and pairing relationships. Their function is to achieve a strong binding between the physical waste and digital information.

[0035] For example, when performing this step, the operator can randomly select at least one material sample from the 10 tons of discarded concrete blocks in the example above, such as a fist-sized physical sample with sharp protrusions. The operator scans this sample using a handheld scanner, generating a digital twin model file, such as a digital twin model file named "DT_A1B2C3.obj". This file includes feature data on the shape outline and surface texture of the sample, such as gray-white mottled texture at a certain location on the surface. Subsequently, the operator places this physical sample into a sealed chamber and locks it with a one-time smart lock. The locking operation activates the RFID chip inside the lock. The operator brings the terminal device close to the smart lock and reads the chip's identity information, assuming it is "NFC_ID_A1B2C3". The system automatically pairs this chip identity information with the unique identifier "Trace20231026143055-Dev007-Rand5892" of the previously generated source tracing data packet. Thus, the physical sample locked in the sealed chamber, its corresponding digital twin model "DT_A1B2C3.obj", the activated smart lock, and the established pairing information together constitute a complete physical-digital anchoring credential.

[0036] In one embodiment of the present invention, step S3 includes the following steps: Extract the pairing information and digital twin model feature data from the physical-digital anchoring certificate; associate the pairing information and digital twin model feature data with the source traceability data package and write them together into the data ledger entry; construct the waste traceability data object based on the data ledger entry.

[0037] Specifically, firstly, the system program will actively extract the core data contained in the generated physical-digital anchoring certificate, read and parse the pairing information that binds the chip identity with the waste batch identity, as well as the digital twin model feature data that represents the unique form of the physical sample.

[0038] Next, the system performs crucial association and consolidation operations, using the unique identifier from the pairing information to retrieve the corresponding complete source tracing data package generated in S1 from the database. At this point, the system has gathered all the information about this batch of waste, including the spatiotemporal and environmental characteristics of the source, the estimated type and quantity, the unique physical sample model, and the anchoring relationship linking the physical and digital worlds. The system packages all this data—pairing information, digital twin model feature data, and the complete source tracing data package—into a single dataset. Then, this dataset is treated as an independent transaction and submitted to a shared, tamper-proof data ledger system. This data ledger system uses a distributed database technology; once information is recorded, it is cryptographically linked to the previous record, forming a chain that cannot be deleted or modified by a single participant, ensuring the permanence and authenticity of the data. This write operation creates a completely new data ledger entry.

[0039] Finally, once the system confirms that this data ledger entry containing all the information has been successfully and permanently recorded, it will trigger the voucher generation process. This process creates a new digital data object, namely the waste traceability data object. The content of this data object mainly includes: a unique link to the newly created data ledger entry, ensuring its traceability; the theoretical carbon reduction potential re-read and clearly recorded from the source traceability data package; and a crucial status field, explicitly set to inactive. The generation of this waste traceability data object signifies that the potential carbon reduction value of this batch of construction waste has been formally registered and pre-locked.

[0040] The waste traceability data object is a digital data structure containing addresses pointing to data ledger entries, numerical fields for the records, and tags indicating their current status. Its function is to serve as pre-registration proof of the potential environmental impact of waste batches before they enter the actual processing stage. Theoretical carbon reduction potential, i.e., previously calculated theoretical carbon reduction values, is recorded here as a data record value. Shared, tamper-proof data ledger entries refer to permanent data records created on a distributed technology platform similar to blockchain. Their information is jointly maintained by multiple authorized parties and cannot be tampered with once written, providing a trusted data foundation for traceability throughout the entire lifecycle of waste.

[0041] For example, continuing from the previous example, the system backend automatically extracts the association of pairing information from the physical-digital anchored credentials, that is: The feature data of “NFC_ID_A1B2C3” and “Trace20231026143055-Dev007-Rand5892”, and the digital twin model “DT_A1B2C3.obj”; The system then invokes the identifier "Trace20231026143055-Dev007-Rand5892" to retrieve the previously generated source tracing data package containing GPS coordinates, environmental fingerprint short video, and an estimated 10 tons of concrete. All this information is packaged and written into a shared, immutable data ledger, forming a new data ledger entry. Based on this new entry, the system can randomly construct a waste tracing data object numbered "UCT-20231026-001". This data object records the theoretical carbon reduction potential, as previously assumed, as 1 ton of CO2 equivalent, and its status bar clearly indicates that it is inactive.

[0042] In one embodiment of the present invention, step S4 includes the following steps: The estimated weight information of waste recorded in the waste traceability data object is retrieved, and a multi-dimensional energy consumption prediction model is established by combining the vehicle model and the planned route, including the expected fuel consumption curve, load change curve and normal tire pressure range. During transportation, the actual energy consumption data of the vehicle's engine load, cargo box weight and tire pressure are collected in real time. The actual energy consumption data is continuously compared with the multi-dimensional energy consumption prediction model, and the comparison results, the geographical location information along the route and abnormal fluctuation events are recorded as a transportation process consistency log.

[0043] Specifically, after the waste traceability data object is generated, the waste batch, along with its physical-digital anchoring certificate, is loaded onto a transport vehicle, ready to be transferred from the source to the processing plant. At this point, the system initiates monitoring and recording of the transportation process. The goal of this stage is to generate a transportation process consistency log to ensure that the waste has not been tampered with or suffered abnormal losses during transportation. When the driver of the transport vehicle confirms the start of the transportation task on the onboard terminal, the system first retrieves the waste traceability data object associated with this task. From the ticket information, the system can read the estimated weight information of the waste recorded at the source. At the same time, the system background will query the standard fuel consumption, rated load capacity, and other parameters of the truck model bound to this transportation task from the vehicle database. Combined with the pre-planned transportation route data from the origin to the destination, the system will use an algorithm model to construct a multi-dimensional energy consumption prediction model. This model can predict the theoretical curves of various energy consumption indicators as a function of time and geographical location when the vehicle carrying this batch of waste travels on the planned route under ideal conditions. Specifically, this model will generate a expected fuel consumption curve, showing the theoretical fuel consumption of the vehicle on different road sections; a load change curve, which depicts the theoretical trend of the vehicle's total weight slowly decreasing due to fuel consumption; and a normal tire pressure range, which is the reasonable fluctuation range of tire pressure under normal driving conditions.

[0044] Once the transportation mission officially begins, multiple sensors installed on the vehicle will continuously collect a series of real-time energy consumption data while the vehicle is en route. These sensors include an interface connected to the engine control unit to read the engine's instantaneous load and fuel consumption rate; weight sensors installed under the cargo compartment to monitor the weight of the cargo in the compartment in real time; and pressure sensors installed on each tire to monitor the tire pressure in real time. This collected data on engine load, cargo weight, and tire pressure is uploaded to the back-end system in real time via the vehicle communication module.

[0045] After receiving this continuous actual energy consumption data, the system backend performs ongoing comparison operations. This involves comparing the real-time cargo weight with the load change curve in the multi-dimensional energy consumption prediction model to check for sudden, significant reductions, which could indicate that waste was discarded midway. Real-time fuel consumption data is compared with the expected fuel consumption curve to check for abnormal fuel consumption, which could be associated with abnormal driving behavior or route deviation. Simultaneously, real-time tire pressure is monitored to ensure it remains within the normal range. Each comparison result, whether matching or deviating, is recorded by the system. Furthermore, the system links these records to the vehicle's real-time geographical location information. If the system detects any actual data deviating from the normal range of the prediction model, it defines this as an abnormal fluctuation event and marks it specially. All these comparison results, the geographical location information along the route, and any specially marked abnormal fluctuation events are continuously and chronologically recorded, ultimately compiled into an electronic document—the transportation process consistency log associated with the waste traceability data object.

[0046] The transportation consistency log is a dynamically generated electronic record file. Its data structure is a time-sorted list of events, with each event containing a timestamp, geographical location, predicted and actual energy consumption values, and anomaly flags. Its purpose is to provide a detailed, auditable chain of evidence regarding the compliance of the waste transportation process. The estimated waste weight information is an estimate of the waste weight inherited from the source traceability data package. The multidimensional energy consumption prediction model is an algorithmic model built based on vehicle parameters, cargo weight, and route information. Its function is to output the theoretical expected values ​​of various key energy consumption indicators during vehicle operation.

[0047] The expected fuel consumption curve, part of the predictive model output, graphically or as a data sequence, shows the theoretical fuel consumption rate of the vehicle at each point along the planned route. The load variation curve, also part of the model output, depicts the theoretical trajectory of the vehicle's total weight as it gradually decreases due to fuel consumption over time. The normal tire pressure range is the safe pressure interval calculated by the model based on the vehicle model and load, for example, 2.2 to 2.5 atmospheres.

[0048] Actual energy consumption data consists of real-time operating parameters directly measured by onboard sensors, including specific values ​​for engine load, vehicle weight, and tire pressure. Geographic location information is the vehicle's real-time latitude and longitude coordinates obtained through the onboard positioning system module.

[0049] Abnormal fluctuation events refer to situations where the actual energy consumption data deviates from the theoretical value of the prediction model. For example, if the weight of the cargo compartment suddenly decreases by more than 5% in the non-unloading area, the system will record the abnormal weight event.

[0050] For example, suppose a truck is carrying the 10 tons of waste concrete blocks and the sealed compartment containing physical samples, as described in the example above. After the driver clicks "Start Transport," the system retrieves the waste traceability data object numbered "UCT-20231026-001" and obtains the estimated weight of the waste as 10 tons. The system, combining the truck model and the preset route, establishes a multi-dimensional energy consumption prediction model, assuming the total fuel consumption for this transport should be around 50 liters, the load will gradually decrease starting from the vehicle's own weight plus 10 tons, and the tire pressure should be maintained around 2.4 atmospheres. If, during the journey, the system collects real-time data showing a stable engine load and tire pressure consistently at 2.45 atmospheres, but when passing an unplanned stop, the actual energy consumption data from the cargo compartment weight sensor suddenly shows a drop in weight from 10 tons to 8 tons, the system will record this deviation as an abnormal fluctuation event and mark the geographical location where this event occurred. These normal comparison results and this abnormal event, along with all the geographical location information along the route, were recorded in real time in the transportation process consistency log, which was associated with the data object "UCT-20231026-001".

[0051] In one embodiment of the present invention, step S5 includes the following steps: Verify the smart lock status of the physical-digital anchoring certificate and read its radio frequency identification chip information; scan the physical samples in the compartment and compare their shape with the digital twin model recorded in the waste traceability data object; combine the data recorded in the transportation process consistency log to comprehensively evaluate the integrity and authenticity of the waste batch flow process and form a terminal cross-verification report.

[0052] Specifically, once the transport vehicle safely arrives at the designated waste processing plant, the process enters the final verification stage of the entire transportation process. The purpose is to generate a terminal cross-verification report to confirm the authenticity and integrity of the waste batch during its transit. The receiving personnel at the processing plant first perform physical credential verification. They locate the sealed compartment that accompanied the vehicle, which is the physical part of the physical-digital anchored credential. The first step is to check the condition of the one-time smart lock, visually inspecting the lock body structure for damage and confirming there are no signs of forced entry or prying. Next, the receiving personnel use a dedicated handheld terminal device to approach the smart lock. The terminal device's RFID reading function is activated to read the information from the chip inside the lock. The system then compares the read RFID chip information with the matching information recorded in the database associated with this waste traceability data object. Only when the lock body is intact and the chip information matches is the first layer of verification considered successful.

[0053] After successful verification, the receiving personnel will use specialized tools to break and open the one-time smart lock, open the sealed compartment, and remove the physical sample inside. Immediately, the receiving personnel will use a 3D scanning device with the same precision as that used during source collection to scan the physical sample removed from the compartment. After scanning, a new 3D model will be generated. The system will automatically and precisely compare the morphological features of this newly generated 3D model with the original digital twin model stored in the corresponding data ledger entry of the waste traceability data object. The comparison algorithm will analyze the key geometric feature points, curvature, and surface texture distribution of the two models to calculate the similarity. If the similarity is higher than a preset threshold, such as 99.5%, it proves that the physical sample has not been tampered with during transportation, and the second verification is successful.

[0054] Finally, the system performs a comprehensive data assessment. It automatically retrieves the complete transportation consistency log generated in step S4 and associated with this waste traceability data object. The system analyzes all data recorded in the log, including whether the vehicle's trajectory matches the planned route, whether various energy consumption data generally conform to the multidimensional energy consumption prediction model, and whether there are any unexplained abnormal fluctuations. For example, if the log records an abnormal weight loss, but the driver cannot provide a reasonable weighbridge slip or explanation at handover, this assessment will be marked as negative. The system integrates the results of these three verifications—smart lock status verification results, physical sample and digital twin model comparison results, and the assessment conclusion of the transportation consistency log—into a structured electronic report. This report is the final terminal cross-validation report, comprehensively evaluating the integrity and authenticity of the entire flow process from source to end.

[0055] The terminal cross-validation report is a comprehensive evaluation document. Its data structure contains multiple fields that record the results and conclusions of smart lock verification, sample comparison, and transportation log analysis. Its function is to provide a final and reliable audit conclusion for the entire process of waste batch monitoring from source to end. Smart lock status refers to the physical integrity of the one-time smart lock and the normal functionality of its internal chip.

[0056] For example, when the transport vehicle arrives at the processing plant, the receiving personnel can verify the lock, checking its integrity and using a terminal device to read the RFID chip information within the lock to see if it matches the pairing information recorded by the system. After opening the sealed compartment, the physical sample is scanned, generating a new 3D model. The system compares this new model with the original digital twin model. Assuming a similarity of 99.8% is obtained, the comparison is considered successful. Finally, the system retrieves the transport process consistency log and, after analysis, finds that, apart from a recorded abnormal fluctuation event where the weight suddenly dropped from 10 tons to 8 tons, all other data is normal. Combining these three pieces of information, the system generates a terminal cross-validation report, concluding that: physical document verification and sample consistency verification are successful, but there is a 2-ton abnormal weight reduction event during transport that requires further explanation.

[0057] In one embodiment of the present invention, step S6 includes the following steps: After the terminal cross-validation report confirms that there are no errors, the waste batch is weighed, sorted, and recycled to obtain the actual type and quantity of recycled materials produced. The preset carbon emission reduction factor database is queried, and the verified actual carbon emission reduction is calculated based on the actual type and quantity of recycled materials produced. The emission reduction value field of the waste traceability data object is updated with the verified actual carbon emission reduction and its status is changed to activated to generate a verified emission reduction verification data package.

[0058] Specifically, the processing plant staff will decide whether to accept the batch of waste based on the conclusions given in the terminal cross-validation report. If the report is correct, or as in the example above, if the anomaly is reasonably explained, such as the driver providing proof that some waste was rejected in advance due to excessive moisture content, then the batch of waste will be officially accepted and enter the processing flow. The first step in processing is to accurately weigh the batch of waste to obtain its actual weight. Subsequently, the waste will be screened, such as by sending it on a conveyor belt and passing through a series of mechanical or manual sorting lines to completely separate impurities, such as plastics and wood chips, leaving only the core materials that can be recycled. This process will ultimately yield the exact type and quantity of one or more recycled materials; for example, 7.8 tons of pure concrete aggregate and 0.1 tons of scrap steel bars.

[0059] Next, the system will perform carbon emission reduction calculations by querying a built-in carbon emission reduction factor database. This database details the carbon dioxide emission reduction that can be achieved per unit mass of different types of materials through specific resource recycling technologies. The system will use the type and quantity of recycled materials actually produced in the previous step, such as 7.8 tons of concrete aggregate and 0.1 tons of scrap steel bars, as input for the query. The database will return the corresponding carbon emission reduction factors, for example, recycling 1 ton of concrete aggregate can reduce carbon dioxide emissions by 0.08 tons, and recycling 1 ton of steel bars can reduce carbon dioxide emissions by 1.6 tons. The system will then perform calculations, multiplying the quantity of each type of recycled material by its corresponding carbon emission reduction factor, and then summing all the results to obtain the verified actual carbon emission reduction that represents the real environmental contribution of this batch of waste.

[0060] Finally, the verified actual carbon emission reduction calculated earlier is used to update the waste traceability data object numbered "UCT-20231026-001". Specifically, the system locates the record for this data object in the shared data ledger and appends a new, immutable update, changing the numerical field from the original theoretical carbon emission reduction potential to this verified actual carbon emission reduction. Simultaneously, the system changes the status field of this document from inactive to activated. After this numerical update and status change operation, the original waste traceability data object is officially transformed into a verified emission reduction verification data package. This data package represents the actual and verified carbon emission reduction results.

[0061] The formula for calculating actual carbon emission reductions: In the formula, This represents the verified actual carbon emission reductions, expressed in tons of CO2 equivalent. n represents the number of types of recycled materials. The actual output quantity of the i-th type of recycled material is expressed in tons. This value is usually obtained through precise weighing and sorting at the processing plant. The carbon emission reduction factor represents the i-th type of recycled material, expressed in tons of CO2 equivalent per ton of recycled material. This factor indicates the reduction in CO2 emissions compared to using virgin materials for every ton of that material recycled. The values ​​are usually derived from a carbon emission reduction factor database, which is based on authoritative institutions or industry-recognized standard databases.

[0062] It should be noted that the verified emissions reduction data package is a digital data proof with technical verification attributes. Its data structure is based on waste traceability data objects, with updated numerical fields and changed status identifiers. Its function is to serve as the final data record of carbon emissions reduction achievements, which can be used for trading or write-off. The actual type and quantity of recycled materials produced are accurate data obtained through physical sorting and precise measurement of waste batches. The carbon emissions reduction factor database is a structured dataset storing emissions reduction coefficients corresponding to the recycling of various materials. Its settings are based on scientific research results such as life cycle assessments and officially published technical standards. The verified actual carbon emissions reduction is a value representing the true environmental contribution, obtained by multiplying the actual quantity of recycled materials produced by authoritative carbon emissions reduction factors and summing the results.

[0063] For example, suppose the processing plant receives the waste material from the example above and confirms that the 2-ton weight loss is due to moisture evaporation and the proper removal of minor impurities during processing. After precise weighing and sorting, suppose the final output of recycled materials is 7.5 tons of clean concrete aggregate and 0.1 tons of scrap steel bars. The system queries the carbon emission reduction factor database, assuming the carbon emission reduction factor of the concrete aggregate is... The carbon emission reduction factor for scrap steel bars is 1.6, with a value of 0.08. The system is based on the formula... Calculations show that the verified actual carbon emission reduction is 7.5 × 0.08 + 0.1 × 1.6 = 0.6 + 0.16 = 0.76 tons of CO2 equivalent. The system then uses this 0.76-ton value to update the numerical field of the traceability data object "UCT-20231026-001" and changes its status to "activated." At this point, a verified emission reduction data package containing the 0.76-ton actual carbon emission reduction and possessing data integrity is officially generated.

[0064] See appendix Figure 2 This invention also proposes a carbon-neutral construction waste classification and recycling tracking system, comprising the following modules: The source information collection module acquires source traceability data packages containing identity identifiers and theoretical carbon emission reduction values; The physical-digital anchoring module generates physical-digital anchoring credentials bound to the source traceability data packet. The data object construction module, based on physical-digital anchored credentials, generates waste traceability data objects that record theoretical carbon reduction potential and are inactive. The transportation monitoring module generates a consistent log of the transportation process based on waste traceability data objects. The terminal verification module generates a terminal cross-verification report based on the transportation process consistency log and waste traceability data object; The data packet verification activation module, based on the terminal cross-verification report, updates the waste traceability data object by calculating the verified actual carbon emission reduction and generates a verified emission reduction verification data packet.

[0065] Each of the modules can be implemented in whole or in part through software, hardware, or a combination thereof. It supports hardware embedded in or independent of the processor in the computer device, and also supports software stored in the memory of the computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0066] It should be noted that the human information (including but not limited to human device information and personal information) and data (including but not limited to data used for analysis, data stored and data displayed) involved in this invention are all information and data authorized by the human body or fully authorized by all parties. The collection, use and processing of related data require relevant legal standards.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for classifying and recycling construction waste for carbon neutrality, characterized in that, Includes the following steps: S1. Obtain the source tracing data packet containing the identity identifier and theoretical carbon emission reduction value; S2. Obtain the digital twin model feature data of the physical sample of the waste, and activate the one-time smart lock and radio frequency identification chip on the sealed chamber of the physical sample, bind the chip identity information with the source traceability data packet, and generate a physical-digital anchoring certificate. S3. Based on physical-digital anchored credentials, generate waste traceability data objects that record theoretical carbon reduction potential and are inactive. S4. Generate a consistent log of the transportation process based on the waste traceability data object; S5. Generate terminal cross-validation reports based on transportation process consistency logs and waste traceability data objects; S6. Based on the terminal cross-validation report, update the waste traceability data object by calculating the verified actual carbon emission reduction, and generate a verified emission reduction verification data package.

2. The method for classifying and recycling construction waste for carbon neutrality according to claim 1, characterized in that, Generating a source tracing data packet includes the following steps: For pre-classified batches of construction waste to be processed, collect their associated spatiotemporal stamp-environmental fingerprint composite information; Based on the pre-selected waste type and estimated quantity, and combined with spatiotemporal stamp-environmental fingerprint composite information, a source traceability data package is generated.

3. The method for classifying and recycling construction waste for carbon neutrality according to claim 1, characterized in that, Generating waste traceability data objects includes the following steps: Extract pairing information and digital twin model feature data from physical-digital anchored credentials; The pairing information and digital twin model feature data are associated with the source tracing data package and written together into the data ledger entry; Based on the data ledger entries, construct waste traceability data objects.

4. The method for classifying and recycling construction waste for carbon neutrality according to claim 1, characterized in that, Generating a consistent log of the transportation process includes the following steps: Retrieve the estimated weight information of waste recorded in the waste traceability data object, and combine it with vehicle model and planned route to establish a multi-dimensional energy consumption prediction model that includes expected fuel consumption curve, load change curve and normal tire pressure range. During transportation, real-time data on the vehicle's engine load, cargo box weight, and tire pressure are collected. The actual energy consumption data is continuously compared with the multidimensional energy consumption prediction model, and the comparison results, geographical location information along the route, and abnormal fluctuation events are recorded as a transportation process consistency log.

5. The method for classifying and recycling construction waste for carbon neutrality according to claim 1, characterized in that, Generate a terminal cross-validation report, including the following steps: Verify the status of the smart lock with the physical-digital anchoring credential and read its radio frequency identification chip information; Physical samples inside the cabin were scanned, and their morphology was compared with the digital twin model recorded in the waste traceability data object; By combining the data recorded in the consistency log of the transportation process, the integrity and authenticity of the waste batch flow process are comprehensively evaluated, and a terminal cross-validation report is generated.

6. The method for classifying and recycling construction waste for carbon neutrality according to claim 1, characterized in that, Generating a verified emissions reduction validation data package includes the following steps: After the terminal cross-validation report confirms that there are no errors, the waste batch is weighed, sorted and recycled to obtain the actual type and quantity of recycled materials produced. The system queries a pre-defined carbon reduction factor database and calculates the verified actual carbon reduction based on the type and quantity of recycled materials actually produced. Update the emission reduction values ​​of the waste traceability data object with verified actual carbon emission reductions and change its status to activated to generate a verified emission reduction verification data package.

7. The method for classifying and recycling construction waste for carbon neutrality according to claim 2, characterized in that, The spatiotemporal stamp includes geographic coordinate data obtained through a satellite positioning system, as well as the current date and time data generated by the system; the environmental fingerprint includes the appearance of the batch of construction waste to be processed.

8. The method for classifying and recycling construction waste for carbon neutrality according to claim 1, characterized in that, The process of generating pairing information includes the following steps: Read the identity information of the activated radio frequency identification chip; After receiving the chip's identity information, it is bound to the unique identifier of the source tracing data packet, and the pairing relationship is recorded to generate pairing information.

9. The method for classifying and recycling construction waste for carbon neutrality according to claim 5, characterized in that, The physical samples inside the cabin were scanned and compared, including the following steps: Using 3D scanning equipment with the same precision as that used during source acquisition, the physical samples inside the cabin were scanned to generate a new 3D model; The morphological features of the new 3D model are compared with the original digital twin model corresponding to the waste traceability data object previously stored. Analyze the key geometric feature points, curvature, and surface texture distribution of the two models, and calculate their similarity.

Images