Road engineering low-carbon data credible interaction method and system

Abstract

The invention belongs to the field of data processing, and provides a road engineering low-carbon data credible interaction method and system, and the method comprises the steps: constructing a full life cycle stage division model, generating corresponding low-carbon data according to the business activity of each stage, and adding a stage identifier, a timestamp and a spatial position label to the low-carbon data; based on a preset engineering logic, a data causal relationship and a carbon emission factor model, constructing a data context tree comprising a plurality of low-carbon data nodes, and organizing the low-carbon data nodes in different space-time ranges and life cycle stages into a life cycle data context tree with a staged conduction relationship; before data interaction is carried out, multi-dimensional verification operation is carried out on target data based on the data context tree; and calculating a credible score of the target data based on a structural path verification result, a space-time consistency verification result and a carbon factor mapping verification result, and controlling the interactive access permission of the low-carbon data according to a score result.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and in particular relates to a method and system for trusted interaction of low-carbon data in road engineering. Background Art

[0002] The concept of low-carbon development has gained widespread attention in the infrastructure construction sector. Road projects, as energy-intensive and resource-intensive industries, generate significant carbon emissions throughout their design, construction, operation, and demolition phases. To achieve comprehensive carbon emissions management, road project stakeholders must collect, transmit, and share low-carbon emission data covering the entire lifecycle for use in carbon footprint accounting, carbon emission assessment, and green performance assessment.

[0003] Existing technologies typically manage low-carbon data through a phased collection and entity-specific attribution model. This includes methods such as IoT sensors at construction sites, carbon emission estimation models, carbon verification platforms, and database-based data sharing interfaces. To verify data credibility, some platforms employ digital signatures, hash checks, or third-party audits to ensure data integrity and source authenticity.

[0004] However, existing technologies generally ignore the lifecycle dependency characteristics and phased logical transmission relationship of low-carbon data in road projects, and lack a systematic verification mechanism based on structural paths, spatiotemporal dynamics and causal semantics. As a result, in the process of multi-stage collaboration and interaction between heterogeneous systems, there are problems such as data silos, causal breaks, spatiotemporal conflicts and carbon emission deviations, which seriously affect the credibility and availability of low-carbon data in refined management and policy compliance. Summary of the Invention

[0005] In order to solve the problems in the prior art, the present invention provides a method for trusted interaction of low-carbon data in road engineering, comprising the following steps: S1: Build a full life cycle phase division model, divide the road project life cycle into the design phase, construction phase, operation phase, and demolition phase, generate corresponding low-carbon data based on the business activities in each phase, and attach a phase identifier, timestamp, and spatial location tag to the low-carbon data; S2: Based on the preset engineering logic, data causality, and carbon emission factor model, a data context tree containing multiple low-carbon data nodes is constructed. The low-carbon data nodes in different spatiotemporal ranges and life cycle stages are organized into a life cycle data context tree with a stage-by-stage transmission relationship. Each data node in the data context tree includes the source stage, spatial location, timestamp, responsible entity, semantic label, and its logical mapping relationship with the upstream node; S3, before data interaction, performing a multi-dimensional verification operation on the target data based on the data context tree, the multi-dimensional verification operation including: Structural path verification: judging whether the target data node has a complete upstream logical path based on the data context tree; if the path is missing, it is marked as structurally incomplete data; Spatiotemporal consistency verification: Based on the timestamp and spatial label of the target data, the semantic similarity of similar data in its spatial neighborhood and temporal neighborhood is compared. If there is inconsistency, an anomaly flag is triggered; Carbon factor mapping verification: Based on the carbon emission model, causal calculation verification is performed on the target data and the data content of its upstream nodes to determine whether it complies with the carbon emission conversion logic; S4, calculating the trustworthiness score of the target data based on the structural path verification results, the spatiotemporal consistency verification results, and the carbon factor mapping verification results, and controlling the interactive access rights of the low-carbon data according to the scoring results.

[0006] Furthermore, the steps of the data context tree include: Take the data nodes of all design stages as the root node set, and perform the recursive process for each root node: Traverse all construction phase nodes and find nodes that have a legal mapping relationship with the node; For each matching node, create a subnode reference under the design node; Repeat this process for each child node, and continue to search for data nodes in the operation and demolition phases; If a node has multiple upstream paths, the node is allowed to have multiple parent node references; The constructed path forms a lifecycle data context path, and multiple paths constitute a complete data context tree.

[0007] Furthermore, spatiotemporal consistency verification includes: Extracting the stage to which the target data belongs and setting an adaptive neighborhood, wherein the adaptive neighborhood is a time domain or a space domain; Retrieve similar datasets in the neighborhood; Construct a multimodal embedding representation, use a multimodal encoding model to map it into a unified dimensional vector, and calculate the similarity: sim(D,Di), where D is the target data and Di is the data point in the i-th domain; Calculate the neighborhood density-driven anomaly index DDI = m / n, where n is the total number of similar data points in the neighborhood; m is the number of data points satisfying sim(D, Di) < θ; θ is the similarity threshold; The anomaly index is used to determine whether the target data is spatiotemporally abnormal within its neighborhood.

[0008] Furthermore, structural path verification includes: Extract the upstream path of the target data node; Call the system's preset standard lifecycle path set; The node phase labels in the actual path are semantically compared with the phase labels in the standard path. If the semantic similarity is higher than the preset threshold, it is considered a successful match. The total number of stages that successfully match the actual path and the standard path is counted, and a stage weight vector is introduced to express the importance of different stages to the trusted path and calculate the structural path matching score; Whether the structural path is complete is determined according to the structural path matching score.

[0009] Furthermore, the carbon factor mapping verification includes: Based on the upstream logical path of the target node in the data context tree, carbon-related behavior nodes and their causal dependencies are extracted to construct a directed graph, where nodes represent construction behaviors and edges represent their direct or indirect impact on target carbon emissions. According to the equipment model, operating load, construction method, and ambient temperature parameters, consult the corresponding working condition database and correct the carbon factor value; The theoretical carbon emissions of the target node are calculated using the material quantities and correction factors on all paths in the causal graph structure; For all upstream nodes that have a causal transmission path with the target node, the differences between their recorded values ​​and the model estimated values ​​are compared one by one. If the deviations on all chains are within the tolerable error range, the collaborative consistency score is calculated; If the collaborative consistency score is less than the preset score or the error is greater than the preset error, the system marks the node as a carbon causal anomaly.

[0010] Another aspect of the present invention provides a road engineering low-carbon data trusted interaction system, characterized in that the system includes the following modules: The life cycle division module is used to build a full life cycle phase division model, dividing the road project life cycle into the design phase, construction phase, operation phase and demolition phase, generating corresponding low-carbon data based on the business activities in each phase, and adding phase identification, timestamp and spatial location tags to the low-carbon data; A data context tree construction module is used to construct a data context tree containing multiple low-carbon data nodes based on preset engineering logic, data causality, and carbon emission factor models. The module organizes low-carbon data nodes in different spatiotemporal ranges and life cycle stages into a life cycle data context tree with a stage-by-stage transmission relationship. Each data node in the data context tree includes a source stage, spatial location, timestamp, responsible entity, semantic label, and its logical mapping relationship with the upstream node; A multi-dimensional verification module, used for performing structural path verification, spatiotemporal consistency verification and carbon factor mapping verification on the target data based on the data context tree before data interaction; The multi-dimensional verification operation includes: Structural path verification: judging whether the target data node has a complete upstream logical path based on the data context tree; if the path is missing, it is marked as structurally incomplete data; Spatiotemporal consistency verification: Based on the timestamp and spatial label of the target data, the semantic similarity of similar data in its spatial neighborhood and temporal neighborhood is compared. If there is inconsistency, an anomaly flag is triggered; Carbon factor mapping verification: Based on the carbon emission model, causal calculation verification is performed on the target data and the data content of its upstream nodes to determine whether it complies with the carbon emission conversion logic; The trust score and permission control module is used to calculate the trust score of the target data based on the above three verification results, and control the interactive access rights of the low-carbon data according to the score result.

[0011] Furthermore, the data context tree construction module includes: The node initialization submodule is used to take the data nodes of all design stages as the root node set; The node recursive matching submodule is used to recursively perform phase node matching operations on each root node, traverse all construction phase, operation phase, and demolition phase nodes, find nodes that have a legal mapping relationship with the current node, and establish child node references under them; The path construction submodule is used to allow nodes with multiple upstream paths to have multiple parent node references, and to combine the multiple constructed paths into a complete lifecycle data context tree structure.

[0012] Furthermore, the spatiotemporal consistency verification unit in the multi-dimensional verification module includes: A neighborhood setting unit is used to extract the stage to which the target data belongs and set an adaptive neighborhood, including time and space domains; A neighborhood data extraction unit, used to retrieve similar data sets within the neighborhood range; The semantic similarity scoring unit is used to construct a multimodal embedding representation and use the multimodal encoding model to map it into a unified dimensional vector to calculate the similarity sim(D,Di), where D is the target data and Di is the i-th data point in the neighborhood; The anomaly index calculation unit is used to calculate the neighborhood density-driven anomaly index DDI = m / n, where n is the total number of neighborhood data points and m is the number of data points with a similarity lower than a preset threshold θ. Based on this index, it is determined whether the target data has spatiotemporal anomalies.

[0013] Furthermore, the structural path verification unit in the multi-dimensional verification module includes: A path extraction unit, used to extract the upstream path of the target data node; The standard path comparison unit is used to call the system's preset standard lifecycle path set and compare the phase labels of the nodes in the actual path with the standard path for semantic similarity; Matching score calculation unit, used to count the number of successfully matched stages and calculate the structural path matching score based on the stage weight vector; The structure integrity determination unit is used to determine whether the structure path of the target data is complete according to the matching score result.

[0014] Furthermore, the carbon factor mapping verification unit in the multi-dimensional verification module includes: A carbon causal graph construction unit is used to extract carbon-related behavior nodes and their causal dependencies based on the upstream logical path of the target node in the data context tree, and construct a carbon causal graph structure; The carbon factor correction unit is used to query the working condition database based on parameters such as equipment model, operating load, construction method and ambient temperature, and correct the original carbon emission factor value; Theoretical carbon emission calculation unit, used to calculate the theoretical carbon emissions of the target node using the material usage and the modified carbon factor on all causal paths; The collaborative consistency analysis unit is used to compare the actual recorded values ​​of the target node and the upstream node with the theoretical estimated values. If the deviations are within the tolerable error range, the collaborative consistency score is calculated. If the score is lower than the threshold or the error is too large, it is marked as carbon causal abnormal data.

[0015] By constructing a lifecycle data context tree, the present invention realizes the full lifecycle organization and logical mapping of low-carbon data. It can systematically identify the dependency paths, upstream sources and causal structures between data nodes, thereby effectively solving the problems of loose structure and broken paths of traditional low-carbon data, and improving the data traceability and semantic consistency.

[0016] The present invention designs three complementary multi-dimensional verification mechanisms: structural path verification, spatiotemporal consistency verification, and carbon factor mapping verification. These mechanisms can comprehensively judge the credibility of target data in terms of structural rationality, spatiotemporal logic, and carbon emission causal relationships, significantly improving the accuracy of data quality control and anomaly identification, and providing guarantees for trusted data interaction between multiple subjects.

[0017] By introducing a trusted scoring model and access permission control mechanism, the present invention implements a dynamic authorization strategy based on verification results, which can ensure stable operation of the system without leaking low-trust data. At the same time, it supports flexible applications in various scenarios such as government supervision, carbon verification and smart decision-making, and enhances the security and scalability of the low-carbon data management system for road projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is an overall flow chart of the road engineering low-carbon data trusted interaction method of the present invention; Figure 2 It is a schematic diagram of the data context tree of the present invention. DETAILED DESCRIPTION

[0020] The invention is preferably described below in conjunction with the accompanying drawings and specific embodiments.

[0021] In one embodiment, the present invention proposes a trusted interaction method for low-carbon data in road projects, which is suitable for realizing spatiotemporal labeling, semantic modeling, logical mapping and multi-dimensional verification of low-carbon emission-related data in various stages of road projects, thereby constructing a trusted interaction mechanism with structural traceability, semantic consistency and causal rationality to support data compliance management and accountability traceability of road projects in carbon accounting, carbon supervision and carbon trading scenarios.

[0022] For the purposes of this document, road engineering refers to a comprehensive engineering construction activity encompassing the planning, design, construction, operation, maintenance, and renovation and expansion of various types of road infrastructure, including highways, urban roads, and rural roads. This activity encompasses route design, roadbed and pavement structure construction, supporting bridge and tunnel facilities, drainage and lighting system integration, traffic safety engineering deployment, and post-maintenance management, forming a crucial component of the transportation infrastructure system. Road engineering, characterized by large investments, long construction cycles, numerous participating entities, and extensive operation and maintenance cycles, is a crucial pillar of national economic development and regional transportation operations.

[0023] Road project low-carbon data refers to a collection of data directly or indirectly related to low-carbon factors such as carbon emissions, energy consumption, carbon sequestration, energy-saving measures, and the use of green building materials at all stages of a road project. This data ranges from structural plans and material lists in the early design phase, to equipment operating energy consumption and material transportation carbon emissions during the construction phase, to lighting and maintenance energy consumption during the operation and maintenance phase, and to carbon emission reduction records from final demolition and recycling. All of this data, linked to carbon emission factor models, forms the basis for quantifying and monitoring the project's carbon footprint. Road project low-carbon data not only reflects the efficiency of resource utilization during project construction but also carries key indicators of the project's green transformation.

[0024] Road engineering low-carbon data has the following notable characteristics: First, it exhibits strong lifecycle dependencies, meaning that data generated at different times has a logical and semantically interdependent, stage-by-stage relationship. For example, design parameters determine construction intensity, and construction behavior influences operation and maintenance energy consumption. Second, low-carbon data exhibits dynamic evolution and spatiotemporal sensitivity, changing dynamically over time and geographic location, exhibiting non-stationary characteristics. Third, its data is strongly tied to the responsible parties, possessing high legal and compliance audit value. Therefore, while complex in structure and sensitive in content, road engineering low-carbon data also requires a high degree of authenticity, integrity, and traceability.

[0025] In the context of the dual carbon strategy, the carbon emission performance of road projects has become an assessment basis for policy regulation, carbon trading, and green financing. The authenticity and verifiability of low-carbon data are crucial for project management throughout its lifecycle. However, low-carbon data for road projects is often dispersed among different stakeholders, subject to inconsistent data standards, inconsistent semantics, chaotic timing, and ambiguous responsibilities, severely restricting its trusted use and interactive sharing. Therefore, building a trusted interaction mechanism for low-carbon data for road projects can establish semantically unified structural associations between multi-source heterogeneous data, ensure data authenticity and integrity through triple verification of time, space, and phase, and enable secure data sharing and accountability traceability among government regulatory agencies, design and construction units, and carbon auditing agencies, thereby enhancing the scientific nature, transparency, and compliance of green road construction.

[0026] Based on the above background, reference Figure 1 As shown, the road engineering low-carbon data trusted interaction method of the present invention specifically includes the following steps: S1, build a full life cycle stage division model, divide the road project life cycle into the design stage, construction stage, operation stage and demolition stage, generate corresponding low-carbon data based on the business activities in each stage, and add stage identification, timestamp and spatial location label to the low-carbon data.

[0027] In the full life cycle management of road projects, business activities at different stages correspond to different types of low-carbon emission behaviors and their data recording methods. The design stage mainly determines the material usage and structural form, and is the link for setting the source of carbon emissions; the construction stage involves a large amount of energy consumption and material transportation, and is a high-density stage of carbon emissions; the operation stage focuses on facility maintenance and energy use, and is the main source of continuous carbon emissions; the demolition stage involves construction waste treatment and material reuse, which is related to carbon recovery and carbon offset. In order to effectively support the full process supervision of road project carbon emissions and the trusted interaction of data, it is necessary to clearly divide the project stages according to the life cycle logic, and add multi-dimensional labels such as stage, time and space during the data generation process to ensure that the data source is clear, the context is consistent, and it is easy to trace and compare.

[0028] In this step, the phase identifier refers to structured information used to indicate which stage of the project lifecycle the data belongs to, such as the design phase, construction phase, operation phase, or demolition phase. The timestamp refers to the specific time information recorded when the data was generated or recorded, usually in a standard time format, used to ensure the temporal consistency and sorting logic of the data. The spatial location tag refers to the identification method that represents the physical spatial location involved in the data. Common forms include pile number codes, K-segment numbers, GPS coordinates, geographic partition numbers, etc., which are used to implement spatial attribution and spatial aggregation analysis.

[0029] Establish a life cycle phase division model for road projects. The life cycle phase model consists of multiple engineering phases arranged in chronological order, including at least the design phase, construction phase, operation phase, and demolition phase. Each phase corresponds to one or more data types and business activity modules. The design phase includes structure selection, material list compilation, and carbon emission estimation; the construction phase includes construction organization planning, equipment scheduling, material transportation, and construction energy consumption collection; the operation phase includes road maintenance, lighting electricity consumption, greening maintenance, etc.; the demolition phase includes structure demolition, construction waste classification, and recycling statistics. Optional implementation solutions include: establishing a standardized phase structure based on national or industry road engineering specifications, or mapping and establishing it in combination with the predefined phase objects of the BIM (Building Information Modeling) system.

[0030] Build a phase-data type mapping table. Based on the lifecycle model, establish a one-to-one or many-to-many mapping relationship between phases and data types, allowing for rapid classification during data collection. For example, the design phase is associated with structural scheme data and material quota data; the construction phase is associated with equipment fuel consumption records, construction logs, and transportation records; the operation phase is associated with lighting energy consumption data and landscaping energy consumption data; and the demolition phase is associated with disassembly plans and construction waste classification statistics. This mapping table is used for subsequent automatic data classification.

[0031] Define and implement a phase identification field in the data collection system. Each piece of low-carbon data should have a phase identification field automatically or manually attached when it is generated or entered. Optional implementations include: embedding a phase tag field in the upload protocol of the IoT collection device, with the device's phase predefined in the system; or providing a phase selector in the manual entry process, allowing the entry staff to select a phase based on the current work task. For example, add a phasetag field to the data packet structure reported by the sensor, with the field value taken from a preset dictionary set {design phase, construction phase, operation phase, demolition phase}.

[0032] Add a standard timestamp field to record the time when data was generated or uploaded. The timestamp field records time information accurate to the second in standard UTC format or the local standard time format. Optional implementations include automatically calling the acquisition device's system clock to automatically generate a timestamp when data is reported, or having the data acquisition platform stamp each data entry with the receipt time upon receipt. If data is manually entered, the entry interface allows manual selection of time values. The system must verify the validity of the time range to prevent violations or conflicts.

[0033] Build a spatial location identification system and assign a spatial location tag to each piece of data. Spatial location tags are used to identify the physical area, construction unit, or measurement point to which the data corresponds. Implementation methods include: defining spatial coding rules based on the road stake number system, such as K10+500, which indicates a location 10 kilometers and 500 meters forward from the starting point; or using GPS latitude and longitude coordinates to record spatial locations, suitable for mobile data collection equipment; or defining the correspondence between structure numbers and spatial locations in the BIM model, using structure IDs as spatial tags. For fixed monitoring point data, the device ID and spatial location can be bound one-to-one during device initialization to achieve automatic injection of data spatial tags.

[0034] Embed the stage identifier, timestamp, and spatial location tag as structured fields within the data model structure of each low-carbon data item and store it in a database or real-time transport protocol. The data model structure should include the original business fields, three types of contextual tag fields (stage, time, and space), and optional semantic extension fields for subsequent data context modeling, interactive verification, and credibility assessment. Data format organization can be implemented using JSON, XML, or relational database table structures to ensure a consistent structure and semantic context across data.

[0035] After the above steps are completed, each piece of low-carbon data not only has content ontology information (such as usage, type, and source), but also has contextual attributes that are highly coupled with engineering logic, time flow, and geographical segments, thereby supporting subsequent data positioning, attribution, and comparison operations in structural path verification, spatiotemporal consistency verification, and carbon factor causal verification, effectively improving the manageability, comparability, and traceability of the data.

[0036] In a specific example: A provincial highway project, numbered G2020, has a starting stake at K0+000 and an ending stake at K20+000, with a total length of 20 kilometers. During the project design phase, it was determined that the section from kilometers 10 to 12 would use a cement-stabilized gravel base with a thickness of 18 cm. The design materials used were gravel, cement, and water. The system generated a material estimate based on the construction drawings and output the following design data record: Material name: cement stabilized gravel Design volume: 10,800 cubic meters Estimated cement consumption: 1,620 tons Design stage identification: Design stage Timestamp: 2020-04-12 14:00:00 Spatial location label: K10+000 to K12+000 The data is exported by the design unit in the design BIM system. The platform system automatically parses the phase attributes and adds the phase identifier as the design phase. The system obtains the export time as the timestamp, and automatically extracts the route range from the structural component mapping, marking the spatial position label as K10+000 to K12+000.

[0037] During the construction phase, from August 10, 2020, to August 17, 2020, the construction company laid the base layer from K10+800 to K11+200. The equipment monitoring system deployed on site automatically collected operating data, fuel consumption data, and material transportation records of the paving equipment, generating the following three low-carbon data records: Equipment operation record: Equipment No.: PL-012 Working time: 6.0 hours Fuel consumption: 18 liters diesel Phase identification: Construction phase Timestamp: 2020-08-12 09:30:00 Spatial location label: K10+900 Material arrival record: Material name: cement Actual delivery volume: 120 tons Batch number: SJK-20200812 Phase identification: Construction phase Timestamp: 2020-08-12 07:45:00 Spatial location label: K10+800 Paving carbon emission estimates (based on the previous two calculations): Operation section: K10+800 to K11+200 Estimated carbon emissions: 24.3 tons

[0038] Based on the factor: the fuel consumption carbon factor is 2.68kg / L, material factor is 0.89kg / kg Phase identification: Construction phase Timestamp: 2020-08-12 17:30:00 Spatial location label: K10+800 to K11+200 The above three data are reported in real time by the automated collection system. After unified structural processing by the platform system, the system adds a stage mark as the construction stage, uses the time recorded by the collection device as the timestamp, and uses device binding or construction section coding to automatically parse the spatial location label.

[0039] The data structure is as follows (expressed in JSON): { dataid:G2020-PL012-20200812, stagelabel: construction stage, timestamp:2020-08-12 09:30:00, location:K10+900, datatype: equipment operation record, content:{ deviceid:PL-012, runhours:6.0, diesellitre:18.0 } } When subsequently performing carbon emission accounting, the system automatically matches the data with the material list data from the early design phase based on the above-mentioned timestamps, spatial location tags, and stage identifiers. The system then uses the carbon factor mapping logic to convert the fuel consumption data into carbon emissions. The carbon factor refers to the relationship between various carbon emission-related behaviors in road projects (such as material use, equipment operation, and energy consumption) and their corresponding carbon emission equivalents, that is, the carbon dioxide equivalent corresponding to a preset amount of carbon emission-related behaviors. The carbon factor can be determined based on the following sources: national or local carbon accounting standards, such as the "Guidelines on Voluntary Greenhouse Gas Emission Reduction Project Methodology"; IPCC guidelines, such as the "IPCC Guidelines for National Greenhouse Gas Inventories"; engineering industry databases or carbon emission platforms, such as the "China Carbon Measurement Network" and the "Carbon Emission Factor Database"; operating condition correction factor reports or industry yearbooks published by third-party carbon verification agencies, etc. Carbon factor mapping refers to the calculation of specific carbon quantities based on the amount of carbon emission-related behaviors and carbon factors (for example, the fuel consumption data is 18 liters, and the fuel consumption carbon factor is 2.68 kg). / L, the carbon emission equivalent corresponding to the fuel consumption is 18 liters × 2.68 kg / L=48.24kg ) and verify spatiotemporal consistency with the total carbon emission records of the construction section. If spatial label offsets, timestamp overlap conflicts, or a break in the design-construction phase chain occurs, the system will issue an exception flag and block subsequent interactions.

[0040] In step S1, by assigning a clear life cycle stage identifier, standardized timestamp and spatial location label to each piece of low-carbon data, it is possible to accurately determine its upstream and downstream dependencies in the project life cycle, its evolutionary position on the timeline and its scope of ownership in the physical space, providing structured support for subsequent data interaction, carbon emission mapping, path verification and responsibility attribution, and realizing the verifiability, traceability and trusted sharing capabilities of road engineering low-carbon data throughout the entire life cycle.

[0041] S2, based on the preset engineering logic, data causality and carbon emission factor model, constructs a data context tree containing multiple low-carbon data nodes, and organizes the low-carbon data nodes in different time and space ranges and life cycle stages into a life cycle data context tree with a stage-by-stage transmission relationship. Any data node in the data context tree includes the source stage, spatial location, timestamp, responsible entity, semantic label and its logical mapping relationship with the upstream node.

[0042] In the low-carbon management process of the entire life cycle of road projects, there are obvious stage-by-stage transmission relationships and logical dependencies between the low-carbon data generated at different stages. The design stage determines the type and amount of materials, the construction stage records material consumption and equipment operation, the operation stage reflects energy consumption and maintenance behavior, and the demolition stage involves carbon emission recovery and reuse. In order to effectively realize the logical link and semantic mapping of these data in the life cycle dimension, and support subsequent data verification, responsibility tracking and carbon accounting, it is necessary to structure and organize various data nodes according to time sequence, spatial location and logical causal relationship, so as to construct a data context structure with stage evolution attributes. By constructing a life cycle data context tree, low-carbon data from different sources, different formats and different time points can be collected and associated in a unified logical structure to ensure its integrity, consistency and traceability in subsequent data interaction, verification and analysis.

[0043] In this step, the data context tree refers to a data structure organized by lifecycle stage, with engineering low-carbon data as nodes, and the logical relationship, spatiotemporal sequence, and semantic mapping between its upstream and downstream as the connection method, forming a chain evolution structure from the design stage to the demolition stage. A data node refers to an instance of low-carbon data representing a certain point in time, spatial location, and responsible entity, usually containing numerical values, sources, and label information related to carbon emissions. Logical mapping relationships refer to causal deductions or functional correspondences between nodes, such as the mapping between material design usage and actual construction consumption, or the transformation relationship between equipment fuel consumption data and carbon emissions, that is, the carbon factor.

[0044] In an optional implementation, the implementation process of step S2 is as follows: Step S21: Collect and structure low-carbon data at each life cycle stage Data collection sources include material lists and structural plans from the design phase; construction logs, material delivery records, and equipment fuel consumption data from the construction phase; road maintenance records, lighting and landscaping energy consumption from the operational phase; and waste disposal data and material recovery rate data from the demolition phase. All raw data undergoes structured processing and is converted into a unified data node object.

[0045] Each data node has the following basic fields: Source stage (such as design stage) Type of data (e.g. structural design, fuel consumption records, lighting energy consumption) Timestamp (accurate to the second, standard format such as 2020-08-12 09:30:00) Spatial location tag (such as K10+800, or GPS coordinate pair) Responsible entity identification (such as SG-Construction Unit 01) Semantic tags (e.g., carbon source: mechanical energy consumption or carbon benchmark: design plan) This process can be automatically completed by the data access module or the structured conversion module, outputting a set of standard data nodes.

[0046] Step S22: Establishing the engineering logic mapping relationship between nodes After obtaining standardized nodes, it is necessary to establish a conductive mapping path between data nodes based on engineering logic, time sequence, spatial attribution and semantic consistency. The system presets a set of mapping rule templates, including but not limited to: Time order mapping rule: If the timestamp of data node A is earlier than that of node B, and its life cycle stage is earlier than that of B, then the time order is satisfied; Spatial inclusion mapping rule: If the spatial label of node B belongs to the spatial range of node A, a spatial subordination relationship is established; Logical mapping rules for data types: For example, a design materials list can be mapped to material incoming records, and equipment fuel consumption records can be mapped to carbon emission estimates; Semantic label matching rule: If the semantic labels of upstream and downstream nodes have a functional mapping (such as the carbon factor calculation model), a causal path is established.

[0047] The system compares the above rules for each pair of nodes. If all matches are found, a directed edge is generated between the nodes, and the source of the mapping rule is recorded. The direction of the edge always points from the earlier stage to the later stage, for example, the design node points to the construction node, and the construction node points to the operation node.

[0048] Step S23: Organize and form a lifecycle data context tree structure The system uses the data nodes of all design stages as the root node set. For each root node, perform the recursive process: Traverse all construction phase nodes and find nodes that have a legal mapping relationship with the design node; For each matching node, create a subnode reference under the design node; Repeat this process for each child node, and continue to search for data nodes in the operation and demolition phases; If a node has multiple upstream paths, the node is allowed to have multiple parent node references; The constructed path forms a lifecycle data context path, and multiple paths constitute a complete data context tree.

[0049] The tree structure can be stored in one of three ways: Graph database representation, such as Neo4j, where each node is a graph vertex and the edge is a mapping relationship, supports graph queries; Tree-like nested structures, such as nested JSON objects to express parent-child reference relationships; In table storage format, the main table records node IDs, and the association table records mapping edge relationships (starting point ID, ending point ID, mapping type).

[0050] Step S24: Verify the integrity of the data path and mark the node status After the data tree is built, the system performs integrity checks on each path, including: Is there a complete path covering the design-construction-operation phases? Whether time reversal or spatial out-of-bounds mapping occurs; Whether there are isolated nodes (no upstream or downstream); Whether there are any unmapped logical nodes (e.g., there is a carbon emission estimate but no fuel consumption data); Whether there are duplicate mapping paths (e.g., the same construction behavior is pointed to by two design nodes); The system assigns a status label to each node based on the verification results, including status such as structural integrity, structural disconnection, time conflict, spatial dislocation, semantic loss, etc., and outputs the path coverage index. , which is calculated as: = Number of effective mapping paths / Number of theoretical stage chains The number of valid mapping paths represents the number of existing design-construction-operation paths, and the number of theoretical chains represents the number of life cycle stages defined by the project. If it is less than the set threshold (such as 0.7), the system marks the data as incomplete in structure.

[0051] The lifecycle data context tree constructed through the above process can clearly describe the evolution path and causal logic of low-carbon data in different life cycles based on the engineering stage, making the upstream and downstream structures, spatial ownership and time evolution relationships between each data node clearly visible, supporting subsequent data credibility verification, semantic consistency verification and responsible entity attribution analysis, solving the problems of traditional carbon data isolation, mismatch and chain breakage, and improving the structural organization, traceability and trusted interaction capabilities of engineering carbon data.

[0052] like Figure 2 As shown in the following simplified example, in a G2020 road project, the base structure and surface layer renovation work was carried out on the section from K10+500 to K12+000. The low-carbon data of the project's full life cycle includes key node information for each stage of design, construction, operation, and demolition, as shown below: During the design phase, Design Institute-A completed the structural design documents on April 1, 2020, specifying a cement-stabilized gravel base with a thickness of 18 cm and pile numbers ranging from K10+500 to K12+000. Subsequently, on April 2, 2020, the design firm released a material bill, planning to use 1,600 tons of cement and 8,600 tons of gravel.

[0053] Entering the construction phase, at 8:00 am on August 10, 2020, the construction unit SG01 recorded the entry of 210 tons of gravel at the K10+800 position, and then recorded the entry of 75 tons of cement at 8:15. At 14:00 on the same day, the compaction equipment started to operate in the range of K10+800 to K10+900, with a recorded diesel consumption of 20 liters and a construction time of 6 hours. At 10:30 on August 15, 2020, the surface asphalt concrete paving was completed, with a calibrated thickness of 5 cm, covering the pile number section K10+900 to K11+100. On August 16, 2020, the third-party carbon accounting unit HX01 estimated carbon emissions based on the above equipment operation and material usage data, and the result was 42.6 tons of carbon dioxide equivalent ( ).

[0054] Energy consumption data collection began during the operation phase on August 1, 2021. The operation and maintenance unit MAINT01 recorded that the energy consumption for greening irrigation in this section was 9.8 kWh and the lighting consumption was 41.2 kWh.

[0055] The demolition phase, planned by the government agency JTG on May 1, 2024, covers the road section from K10+500 to K10+800, with an estimated demolition area of ​​2,500 square meters. Asphalt recycling will also be carried out. On May 10, 2024, construction unit SG01 compiled the recycled data, which showed 15.3 tons of asphalt and 21.6 tons of cement-stabilized crushed stone base material.

[0056] In the above example, starting from D1, passing through multiple construction nodes, and finally connecting to operation and demolition data, a complete life cycle path is formed.

[0057] And the logical conduction is clear: The design structure and usage determine the materials brought in; Materials drive equipment operation, which in turn generates carbon emissions; Surface construction data not only affects carbon accounting but also determines future demolition behavior; Demolition plans are based on design and construction history, while also forming a closed loop of recycled data.

[0058] Each node is labeled with the engineering phase, time, space, and data type, which helps in structural path verification, causal analysis, and the establishment of a trustworthy scoring model.

[0059] S3, before data interaction, performing a multi-dimensional verification operation on the target data based on the data context tree, the multi-dimensional verification operation including: Structural path verification: Determine whether the target data node has a complete upstream logical path based on the data context tree. If the path is missing, mark it as structurally incomplete data.

[0060] Spatiotemporal consistency verification: Based on the timestamp and spatial label of the target data, the semantic similarity of similar data in its spatial neighborhood and temporal neighborhood is compared. If there is inconsistency, an anomaly mark is triggered.

[0061] Carbon factor mapping verification: Based on the carbon emission model, causal calculation verification is performed on the target data and the data content of its upstream nodes to determine whether it complies with the carbon emission conversion logic.

[0062] In low-carbon road projects, data exhibits strong causal chains, temporal order, and spatial attribution across different lifecycle stages. Because low-carbon data is used for supervision, verification, accountability, and subsequent optimization and design, its credibility directly determines the accuracy of management actions and the effectiveness of decision-making. Therefore, before data interaction (including data call, exchange, sharing, and integration), multi-dimensional verification operations must be performed to ensure that the target data nodes are structurally traceable, consistent in time and space, and meet the logical requirements of carbon emission models in terms of semantics and causality, thereby supporting a trusted and secure data circulation mechanism.

[0063] To achieve the above goals, this paper defines three core verification dimensions: structural path verification, spatiotemporal consistency verification, and carbon factor mapping verification. The concepts are defined as follows: Structural path verification refers to the use of the data transmission path established in the lifecycle data context tree to determine whether the target data node has an upstream data chain from design to the current stage, ensuring that its generation process is complete and traceable.

[0064] Spatiotemporal consistency verification refers to retrieving similar data in its adjacent time window and spatial neighborhood based on the timestamp and spatial location label of the target data, analyzing the semantic similarity and degree of deviation, and judging whether it conforms to the spatial attribution and timeliness logic.

[0065] Carbon factor mapping verification refers to the causal calculation and quantitative comparison of target data and its upstream data nodes based on a preset carbon emission causal model to determine whether the target data is consistent with the physical process and process model, and to eliminate the injection of false, discrete or logically erroneous data.

[0066] In an optional specific implementation, the multi-dimensional verification step specifically includes the following sub-steps: Step S31, perform structure path verification For example, a lifecycle path analysis is performed on the target data node. Based on the constructed data context tree structure, all its upstream node paths are extracted. If any node is missing at any lifecycle stage, or if the path is broken or mapped incorrectly, the target node is marked as having an incomplete structural path.

[0067] The verification method can call the structural path analysis method to return the integrity data of the path.

[0068] Step S32: Execute spatiotemporal consistency verification For example, for the timestamp T and spatial location S of the target data, the temporal neighborhood [ ] and spatial neighborhood[ ] range of similar data set Dneighbor. Calculate the semantic similarity value sim(D,Di) between the target data D and the neighborhood data. If most of the similarities are lower than the preset threshold , the target data is marked as spatiotemporal anomaly.

[0069] Where sim(D,Di) represents the semantic similarity scoring function, which can be calculated using the cosine similarity method based on the word embedding model. is the time window, is the spatial buffer distance, is the minimum semantic consistency threshold (e.g. 0.6).

[0070] Specifically, the semantic text fields in the data D and Di (such as data type descriptions, action labels, and responsible party descriptions) are input into a word embedding encoding model to generate the corresponding embedding vectors VD and VDi. The word embedding model can use a trained BERT, Word2Vec, RoBERTa, or a custom encoding network.

[0071] Then, the cosine similarity function is used to score the similarity between the two embedding vectors, and the calculation formula is as follows: ,in, represents the dot product, Represents the Euclidean norm of a vector.

[0072] Step S33: Execute carbon factor mapping verification For example, calling the carbon emission causal function , where Qi represents the material usage or energy consumption, and EFi is its corresponding carbon emission factor. Compare the carbon emission data Creal recorded in the target node with the theoretical carbon emission Ccalc calculated based on the upstream node. If (error tolerance), the data is judged to violate the causal consistency logic.

[0073] The formula variables are described as follows: Creal represents the carbon emission value actually recorded by the target data node; Ccalc represents the theoretical value of carbon emissions derived from the behavior of upstream materials and equipment; Qi represents category i consumption items, such as diesel consumption, cement usage, etc.; EFi represents the carbon factor of type i; Indicates the acceptable error limit, such as 3%; f(C) is the carbon emission causal mapping function.

[0074] Through the above three types of verification steps, a comprehensive credibility analysis of the target data can be conducted from three aspects: structural chain, time and space labels, and carbon emission causal logic. Low-quality data inputs such as unknown sources, logical conflicts, time and space dislocations, or data falsification can be effectively eliminated, ensuring that data interaction behaviors are based on reliable data, thereby supporting the accuracy and credibility of carbon supervision throughout the life cycle.

[0075] In a specific example, in the G2020 project, assuming that node C5 (carbon emission estimation node) is to be trusted for verification, the system first traces back its structural path: From C5, it is linked upward to C3 (equipment operation records) and C4 (paving records), and then traced back to C1 and C2 (material delivery), and finally connected to D1 and D2 (design stage nodes). The structural path is complete and S31 verification is passed.

[0076] Subsequently, the system extracted the time neighborhood of C5 as 2020-08-16±1 day, the spatial neighborhood as K10+500 to K11+100, and retrieved adjacent carbon emission nodes. The average semantic similarity was 0.89, which was higher than the threshold of 0.7, and S32 verification passed.

[0077] Finally, based on the diesel consumption of C3 in 20 liters (carbon factor 2.68kg / L) and the amount of C1 and C2 materials, the system calculated the theoretical carbon emissions as 20×2.68 + the sum of cement and gravel carbon emissions, totaling an estimated 41.9 tons, while the actual value of C5 was 42.6 tons, with an error of only 1.7%, which is less than the set value. , S33 verification passed.

[0078] In a preferred embodiment, to verify whether the road project low-carbon data nodes have a complete structural path, the system performs the following structural path verification steps. This verification method combines the project phase logic, semantic association relationships, and node path graph structure to confirm the structural legitimacy of multi-source low-carbon data.

[0079] The structural path verification step specifically includes the following sub-steps: Step S311: Extract the upstream path of the target data node Perform a reverse path analysis operation on the target data node N (for example, the carbon emission estimation node C5 of the G2020 project), trace back from node N in the constructed data context tree, and retrieve the direct upstream nodes with which it has a logical mapping relationship in sequence until it is traced back to the node at the beginning of the life cycle (such as the design stage D1).

[0080] In the example of C5, the system can obtain the following path chain in sequence: C5 (Carbon Emissions Estimate) ←C3 (Equipment Operation) ←C1 (crushed stone entering the site), C2 (cement entering the site) ←D2 (Material Consumption List) ←D1 (Structural Design) The obtained upstream path chain is denoted as Pactual = [D1, D2, C1, C2, C3, C5].

[0081] Step S312: Call the standard lifecycle path set The system presets a set of standard paths, denoted as Pstandard, representing the typical lifecycle structure path of low-carbon road engineering data. Each standard path represents a compliant data generation sequence.

[0082] In this example, the fully qualified path is set to: Pstd = [Design phase → Material delivery → Equipment operation → Carbon estimation] Used for subsequent comparison with the actual path.

[0083] Step S313: Execute semantic mapping of phase names The node phase labels in the actual path Pactual are semantically compared with the phase labels in the standard path Pstd. To accommodate label differences caused by differences in data record format, granularity, or system naming, the system introduces a semantic similarity analysis method based on word embedding models (such as Word2Vec or BERT).

[0084] Taking D2 as an example, its stage label is Material Usage List. After comparing it with the Material Delivery in Pstd, the system determines that they are synonymous expressions and matches them.

[0085] If the semantic similarity is higher than the preset threshold (such as 0.85), it is considered a successful match.

[0086] Step S314: Calculate the path phase coverage The total number of stages that successfully match Pactual and Pstd is counted, and a stage weight vector W is introduced to express the importance of different stages to the trusted path.

[0087] Assume the stage weights are as follows: W = {Design phase: 0.3, Material delivery: 0.2, Equipment operation: 0.3, Carbon estimation: 0.2} In this example, the path corresponding to C5 completely covers the four stages of Pstd, and each stage is matched successfully.

[0088] The system calculates the structure path matching score Score: Score=(0.3+0.2+0.3+0.2) / 1.0=1.0 Step S315: Determine whether the score exceeds the credibility threshold Set a trust threshold ,like , then the target data node structure path verification is determined to be successful, otherwise it is marked as incomplete.

[0089] In this example, Score = 1.0 > 0.75, the structural path is complete, and the verification passes.

[0090] This verification method, based on a semantically enhanced path matching mechanism, effectively overcomes misjudgments caused by differences in stage label naming, diverse path forms, or missing non-critical stages, significantly improving the robustness and accuracy of structural path verification. By introducing a stage weight model and a path coverage scoring mechanism, it rationally reflects the contribution of key stages to structural credibility, effectively supporting the path traceability and structural review needs of low-carbon data exchange in road projects.

[0091] When verifying spatiotemporal consistency, traditional methods mainly rely on fixed time windows. , spatial buffer distance Comparing adjacent data with the static semantic similarity function sim(D,Di) cannot effectively adapt to the following complex scenarios: At different stages of the life cycle, the evolution speed and spatial diffusion pattern of data are different, and a fixed neighborhood range is difficult to accurately capture reasonably similar data; Some nodes may contain multimodal information such as unstructured text, images, and sensor sequences, and a single text embedding model cannot be fully compared; In areas with sparse spatial distribution or sudden changes in information density, the average similarity index does not have effective discrimination capabilities and is prone to misjudgment or omission.

[0092] In order to solve the above problem, in a preferred embodiment, the spatiotemporal consistency verification method includes the following sub-steps: Step S321: Extract the target data stage and set the adaptive neighborhood Set the time window according to the stage label of the target data And the space buffer radius Δs.

[0093] Taking the G2020 project carbon emission node C5 as an example, C5 is in the construction phase. The system sets Δt=2 days and Δs=500 meters to form a spatiotemporal neighborhood: Time Neighborhood = [2020-08-14, 2020-08-18] Spatial neighborhood = [K10+500,K11+000] Step S322: Search for similar datasets in the neighborhood Within the above time and space range, a low-carbon data node set Dneighbor of the same carbon estimation type is extracted, which includes: C5a: K10+600, 2020-08-15, value = 41.8 tons C5b: K10+800, 2020-08-16, value = 42.2 tons C5c: K11+200, 2020-08-17, value = 38.9 tons Step S323: Construct multimodal embedding representation For the target data D and each neighborhood data Di, extract the following fields: Text description (e.g. carbon emissions calculation after compaction equipment operation) Numerical parameters (such as diesel consumption, cement usage) Node stage semantic labels (such as carbon accounting) Use a multimodal encoding model (such as MLP for structure fields, BERT for text, and normalization for values) to map to unified dimension vectors VD and VDi and calculate the composite similarity: sim(D,Di)=cosinesimilarity(VD,VDi) Step S324: Calculate the neighborhood density-driven anomaly index Define the density-driven anomaly index DDI = m / n, where: n is the total number of similar data points in the neighborhood; m is the number of data points satisfying sim(D,Di)<θ; θ is the similarity threshold, for example 0.65.

[0094] If DDI ≥ γ (e.g., 0.5), the target data is judged to be a spatiotemporal anomaly in its neighborhood.

[0095] Taking C5 as an example, if the similarity between two of the three neighboring points is lower than 0.65, then DDI = 2 / 3 ≈ 0.67, triggering an anomaly flag.

[0096] This method comprehensively considers phase differences, information modal complexity, and spatial density variations, enabling accurate assessment of spatiotemporal consistency within a multi-source, multi-type, low-carbon road engineering data environment. Compared to traditional methods, this method significantly improves the ability to identify semantic shift and density anomalies. It is applicable to multi-phase data scenarios involving high-frequency construction disturbances and long-term operational stability maintenance, enhancing the intelligence, accuracy, and fault tolerance of the spatiotemporal verification mechanism.

[0097] When validating carbon factor mapping, traditional methods rely solely on simplified linear product formulas to establish carbon emission estimation models. This method cannot accurately simulate carbon emission deviations in real projects due to factors such as material variety fluctuations, energy efficiency differences, construction intensity, and climatic conditions. Furthermore, this method typically only validates a single node, ignoring the upstream and downstream synergistic carbon transmission characteristics between nodes.

[0098] In order to improve the dynamics and accuracy of verification, in a preferred embodiment, the carbon factor mapping verification method specifically includes the following steps: Step S331: Construct the carbon causal graph of the target node According to the upstream logical path of the target node in the data context tree, carbon-related behavior nodes and the causal dependencies between them are extracted to construct a directed graph Gcausal. The nodes in the graph represent behaviors such as material delivery, equipment operation, and construction tasks, and the edges represent their direct or indirect impact on the target carbon emissions.

[0099] Take C5 (carbon emission estimation) of the G2020 project as an example: Gcausal contains: C1 (Crushed Stone Transport) → C3 (Compacting Equipment) → C5 C2 (cement mixing) → C3 → C5 Step S332: Correct the carbon factor under working conditions According to parameters such as equipment model, operating load, construction method, ambient temperature, etc., consult the corresponding working condition database, correct the carbon factor value EFi, and calculate the dynamic carbon factor EFi':

[0100] Where αi represents the operating condition adjustment factor (for example, diesel equipment has low combustion efficiency under plateau conditions, Step S333, calculate the theoretical carbon emission value Ccalc The theoretical carbon emissions of the target node are calculated using the material quantities Qi and correction factors EFi' on all paths in the causal graph structure:

[0101] Continuing with C5 as an example: Gravel transportation consumption Q1=5 tons, EF1=0.06t / ton, α1 = 1.00 Cement consumption Q2=3 tons, EF2=0.72t / ton, α2 = 1.00 Equipment diesel Q3=25 liters, EF3=2.68 kg / L, α3=1.10 Ccalc=5×0.06+3×0.72+(25×2.68×1.10) / 1000≈2.53 tons

[0102] Step S334: Execute upstream and downstream collaborative consistency scoring For all upstream nodes that have causal transmission paths with the target node C5, compare the differences between their recorded values ​​and the model estimated values ​​one by one. If the deviations on all chains are within the tolerable error If the value is within the range, the CCS score is calculated: ,…,errors of each path) If CCS≥ (e.g. 0.85), it is determined that the target data carbon factor verification has passed.

[0103] Step S335: Output verification results If CCS<γ or the error is greater than , the system marks the node as a carbon causal anomaly and refuses it to participate in trusted interaction.

[0104] This further refinement utilizes carbon causal graph modeling to achieve full-path traceability of carbon emissions and coordinated verification of upstream and downstream processes. The dynamic operating condition correction factor, αi, is introduced to enhance the model's adaptability to fluctuations in the actual project environment. Furthermore, a collaborative consistency scoring metric, CCS, is designed to verify not only the target node itself but also the consistency and coherence of the upstream chain. This approach significantly enhances anomaly detection capabilities and ensures logical consistency, providing high-precision support for the trusted interaction of low-carbon data in road projects.

[0105] S4, calculating the trustworthiness score of the target data based on the structural path verification results, the spatiotemporal consistency verification results, and the carbon factor mapping verification results, and controlling the interactive access rights of the low-carbon data according to the scoring results.

[0106] In this step, the trust score refers to the quantitative expression of the comprehensive trustworthiness of the target data node in the multi-dimensional verification, which is recorded as Scoretotal and the value range is usually 0 to 1; the permission level refers to the type of data interaction permission divided according to the trust score, which can include full access, restricted access, and denied access; the weight coefficient refers to the parameter used to express the importance of each verification result in the score, which is set as 、 and , corresponding to structural path verification, spatiotemporal consistency verification and carbon factor mapping verification.

[0107] In an optional specific implementation, the trust scoring and access control step includes the following sub-steps: Step S41: Summarize the three verification results The system receives and summarizes the score results of the target data node in the structural path verification, spatiotemporal consistency verification and carbon factor mapping verification, which are recorded as Scorestruct, Scoretemporal and Scorecarbon respectively.

[0108] Step S42: Apply the weighted scoring model to calculate the total score According to the preset weight coefficient, the total credibility score Scoretotal is calculated using the following weighted formula:

[0109] in Scorestruct represents the structural path verification score Scoretemporal represents the spatiotemporal consistency verification score Scorecarbon represents the carbon factor mapping verification score 、 、 are the weight coefficients of the three dimensions, satisfying .

[0110] Optional weight distribution schemes include: , , , or dynamically adjust according to the data source at different stages.

[0111] Step S43: assigning access rights based on the scoring results The system determines the access permission level for the data based on the value range of Scoretotal: like , then set to full access like , then set it to restricted access If Scoretotal < 0.60, access is denied With restricted access, the system can limit the frequency of data calls, mask sensitive fields, or limit calls to internal only.

[0112] Through the above steps, this method realizes the quantitative assessment and authority classification mechanism for the credibility of low-carbon data, effectively reducing the risks of misjudgment, mistransmission and trust attacks caused by low-credibility data.

[0113] This method offers the advantages of multi-dimensional integration, adaptive adjustment, and transparent results. It not only comprehensively considers the structural integrity, dynamic evolution, and causal consistency of data, but also flexibly adjusts weighting coefficients based on different scenarios, enabling refined trust management of data interaction processes. Compared to traditional judgment models based on single verification, this method significantly improves the scientific nature, robustness, and application adaptability of data access control.

[0114] In a specific example, continuing with the example of the G2020 project above, assume that the three verification scores of the target node C5 are as follows: (Structure path complete) (Good neighborhood consistency) (Carbon Causal Consistency) Using weight coefficient , , ,calculate:

[0115] Therefore, C5 is judged to be a full access level, which can open up full interactive permissions in the road engineering digital platform for various business links such as scheduling analysis, carbon assessment reporting and regulatory backtracking.

[0116] In another embodiment, the present invention further provides a road engineering low-carbon data trusted interaction system, comprising: The life cycle division module is used to build a full life cycle phase division model, dividing the road project life cycle into the design phase, construction phase, operation phase and demolition phase, generating corresponding low-carbon data based on the business activities in each phase, and adding phase identification, timestamp and spatial location tags to the low-carbon data; A data context tree construction module is used to construct a data context tree containing multiple low-carbon data nodes based on preset engineering logic, data causality, and carbon emission factor models. The module organizes low-carbon data nodes in different spatiotemporal ranges and life cycle stages into a life cycle data context tree with a stage-by-stage transmission relationship. Each data node in the data context tree includes a source stage, spatial location, timestamp, responsible entity, semantic label, and its logical mapping relationship with the upstream node; A multi-dimensional verification module, used for performing structural path verification, spatiotemporal consistency verification and carbon factor mapping verification on the target data based on the data context tree before data interaction; The multi-dimensional verification operation includes: Structural path verification: judging whether the target data node has a complete upstream logical path based on the data context tree; if the path is missing, it is marked as structurally incomplete data; Spatiotemporal consistency verification: Based on the timestamp and spatial label of the target data, the semantic similarity of similar data in its spatial neighborhood and temporal neighborhood is compared. If there is inconsistency, an anomaly flag is triggered; Carbon factor mapping verification: Based on the carbon emission model, causal calculation verification is performed on the target data and the data content of its upstream nodes to determine whether it complies with the carbon emission conversion logic; The trust score and permission control module is used to calculate the trust score of the target data based on the above three verification results, and control the interactive access rights of the low-carbon data according to the score result.

[0117] In a further improvement, the data context tree construction module includes: The node initialization submodule is used to take the data nodes of all design stages as the root node set; The node recursive matching submodule is used to recursively perform phase node matching operations on each root node, traverse all construction phase, operation phase, and demolition phase nodes, find nodes that have a legal mapping relationship with the current node, and establish child node references under them; The path construction submodule is used to allow nodes with multiple upstream paths to have multiple parent node references, and to combine the multiple constructed paths into a complete lifecycle data context tree structure.

[0118] In a further improvement, the spatiotemporal consistency verification unit in the multidimensional verification module includes: A neighborhood setting unit is used to extract the stage to which the target data belongs and set an adaptive neighborhood, including time and space domains; A neighborhood data extraction unit, used to retrieve similar data sets within the neighborhood range; The semantic similarity scoring unit is used to construct a multimodal embedding representation and use the multimodal encoding model to map it into a unified dimensional vector to calculate the similarity sim(D,Di), where D is the target data and Di is the i-th data point in the neighborhood; The anomaly index calculation unit is used to calculate the neighborhood density-driven anomaly index DDI = m / n, where n is the total number of neighborhood data points and m is the number of data points with a similarity lower than a preset threshold θ. Based on this index, it is determined whether the target data has spatiotemporal anomalies.

[0119] In a further improvement, the structural path verification unit in the multi-dimensional verification module includes: A path extraction unit, used to extract the upstream path of the target data node; The standard path comparison unit is used to call the system's preset standard lifecycle path set and compare the phase labels of the nodes in the actual path with the standard path for semantic similarity; Matching score calculation unit, used to count the number of successfully matched stages and calculate the structural path matching score based on the stage weight vector; The structure integrity determination unit is used to determine whether the structure path of the target data is complete according to the matching score result.

[0120] In a further improvement, the carbon factor mapping verification unit in the multi-dimensional verification module includes: A carbon causal graph construction unit is used to extract carbon-related behavior nodes and their causal dependencies based on the upstream logical path of the target node in the data context tree, and construct a carbon causal graph structure; The carbon factor correction unit is used to query the working condition database based on parameters such as equipment model, operating load, construction method and ambient temperature, and correct the original carbon emission factor value; Theoretical carbon emission calculation unit, used to calculate the theoretical carbon emissions of the target node using the material usage and the modified carbon factor on all causal paths; The collaborative consistency analysis unit is used to compare the actual recorded values ​​of the target node and the upstream node with the theoretical estimated values. If the deviations are within the tolerable error range, the collaborative consistency score is calculated. If the score is lower than the threshold or the error is too large, it is marked as carbon causal abnormal data. It should be noted that the explanation of the embodiment of the trusted interaction method for low-carbon data of road projects mentioned above is also applicable to the device of the embodiment of this application and will not be repeated here.

[0121] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk, and other media that can store program code.

[0124] The above is only a specific embodiment of the present application. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in this application, which should be included in the scope of protection of this application. For some module structures that are not particularly clear in the present invention, the content recorded in the prior art shall prevail. The prior art mentioned in the above background technology section and the specific embodiment section of the present invention can be regarded as part of the present invention and is used to understand the meaning of some technical features or parameters.

Claims

1. A road engineering low-carbon data trusted interaction method, characterized by: The method comprises the following steps: S1: Build a full life cycle phase division model, divide the road project life cycle into the design phase, construction phase, operation phase, and demolition phase, generate corresponding low-carbon data based on the business activities in each phase, and attach a phase identifier, timestamp, and spatial location tag to the low-carbon data; S2: Based on the preset engineering logic, data causality, and carbon emission factor model, a data context tree containing multiple low-carbon data nodes is constructed. The low-carbon data nodes in different spatiotemporal ranges and life cycle stages are organized into a life cycle data context tree with a stage-by-stage transmission relationship. Each data node in the data context tree includes the source stage, spatial location, timestamp, responsible entity, semantic label, and its logical mapping relationship with the upstream node; S3, before data interaction, performing a multi-dimensional verification operation on the target data based on the data context tree, the multi-dimensional verification operation including: Structural path verification: judging whether the target data node has a complete upstream logical path based on the data context tree; if the path is missing, it is marked as structurally incomplete data; Spatiotemporal consistency verification: Based on the timestamp and spatial label of the target data, the semantic similarity of similar data in its spatial neighborhood and temporal neighborhood is compared. If there is inconsistency, an anomaly flag is triggered; Carbon factor mapping verification: Based on the carbon emission model, causal calculation verification is performed on the target data and the data content of its upstream nodes to determine whether it complies with the carbon emission conversion logic; S4, calculating the trustworthiness score of the target data based on the structural path verification results, the spatiotemporal consistency verification results, and the carbon factor mapping verification results, and controlling the interactive access rights of the low-carbon data according to the scoring results.

2. The road engineering low-carbon data trusted interaction method according to claim 1 is characterized in that: The steps of constructing the data context tree include: Take the data nodes of all design stages as the root node set, and perform the recursive process for each root node: Traverse all construction phase nodes and find nodes that have a legal mapping relationship with the node; For each matching node, create a subnode reference under the design node; Repeat this process for each child node, and continue to search for data nodes in the operation and demolition phases; If a node has multiple upstream paths, the node is allowed to have multiple parent node references; The constructed path forms a lifecycle data context path, and multiple paths constitute a complete data context tree.

3. The road engineering low-carbon data trusted interaction method according to claim 1 is characterized in that: The spatiotemporal consistency verification includes: Extracting the stage to which the target data belongs and setting an adaptive neighborhood, wherein the adaptive neighborhood is a time domain or a space domain; Retrieve similar datasets in the neighborhood; Construct a multimodal embedding representation, use a multimodal encoding model to map it into a unified dimensional vector, and calculate the similarity: sim(D,Di), where D is the target data and Di is the data point in the i-th domain; Calculate the neighborhood density-driven anomaly index DDI = m / n, where n is the total number of similar data points in the neighborhood; m is the number of data points satisfying sim(D, Di) < θ; θ is the similarity threshold; The anomaly index is used to determine whether the target data is spatiotemporally abnormal within its neighborhood.

4. The road engineering low-carbon data trusted interaction method according to claim 1 is characterized in that: The structural path verification includes: Extract the upstream path of the target data node; Call the system's preset standard lifecycle path set; The node phase labels in the actual path are semantically compared with the phase labels in the standard path. If the semantic similarity is higher than the preset threshold, it is considered a successful match. The total number of stages that successfully match the actual path and the standard path is counted, and a stage weight vector is introduced to express the importance of different stages to the trusted path and calculate the structural path matching score; Whether the structural path is complete is determined according to the structural path matching score.

5. The road engineering low-carbon data trusted interaction method according to claim 1 is characterized in that: The carbon factor mapping verification includes: Based on the upstream logical path of the target node in the data context tree, carbon-related behavior nodes and their causal dependencies are extracted to construct a directed graph, where nodes represent construction behaviors and edges represent their direct or indirect impact on target carbon emissions. According to the equipment model, operating load, construction method, and ambient temperature parameters, consult the corresponding working condition database and correct the carbon factor value; The theoretical carbon emissions of the target node are calculated using the material quantities and correction factors on all paths in the causal graph structure; For all upstream nodes that have a causal transmission path with the target node, the differences between their recorded values ​​and the model estimated values ​​are compared one by one. If the deviations on all chains are within the tolerable error range, the collaborative consistency score is calculated; If the collaborative consistency score is less than the preset score or the error is greater than the preset error, the system marks the node as a carbon causal anomaly.

6. A road engineering low-carbon data trusted interactive system, characterized by: The system includes the following modules: The life cycle division module is used to build a full life cycle phase division model, dividing the road project life cycle into the design phase, construction phase, operation phase and demolition phase, generating corresponding low-carbon data based on the business activities in each phase, and adding phase identification, timestamp and spatial location tags to the low-carbon data; A data context tree construction module is used to construct a data context tree containing multiple low-carbon data nodes based on preset engineering logic, data causality, and carbon emission factor models. The module organizes low-carbon data nodes in different spatiotemporal ranges and life cycle stages into a life cycle data context tree with a stage-by-stage transmission relationship. Each data node in the data context tree includes a source stage, spatial location, timestamp, responsible entity, semantic label, and its logical mapping relationship with the upstream node; A multi-dimensional verification module, used for performing structural path verification, spatiotemporal consistency verification and carbon factor mapping verification on the target data based on the data context tree before data interaction; The multi-dimensional verification operation includes: Structural path verification: judging whether the target data node has a complete upstream logical path based on the data context tree; if the path is missing, it is marked as structurally incomplete data; Spatiotemporal consistency verification: Based on the timestamp and spatial label of the target data, the semantic similarity of similar data in its spatial neighborhood and temporal neighborhood is compared. If there is inconsistency, an anomaly flag is triggered; Carbon factor mapping verification: Based on the carbon emission model, causal calculation verification is performed on the target data and the data content of its upstream nodes to determine whether it complies with the carbon emission conversion logic; The trust score and permission control module is used to calculate the trust score of the target data based on the above three verification results, and control the interactive access rights of the low-carbon data according to the score result.

7. The road engineering low-carbon data trusted interactive system according to claim 6 is characterized in that: The data context tree construction module includes: The node initialization submodule is used to take the data nodes of all design stages as the root node set; The node recursive matching submodule is used to recursively perform phase node matching operations on each root node, traverse all construction phase, operation phase, and demolition phase nodes, find nodes that have a legal mapping relationship with the current node, and establish child node references under them; The path construction submodule is used to allow nodes with multiple upstream paths to have multiple parent node references, and to combine the multiple constructed paths into a complete lifecycle data context tree structure.

8. The road engineering low-carbon data trusted interactive system according to claim 6 is characterized in that: The spatiotemporal consistency verification unit in the multi-dimensional verification module includes: A neighborhood setting unit is used to extract the stage to which the target data belongs and set an adaptive neighborhood, including time and space domains; A neighborhood data extraction unit, used to retrieve similar data sets within the neighborhood range; The semantic similarity scoring unit is used to construct a multimodal embedding representation and use the multimodal encoding model to map it into a unified dimensional vector to calculate the similarity sim(D,Di), where D is the target data and Di is the i-th data point in the neighborhood; The anomaly index calculation unit is used to calculate the neighborhood density-driven anomaly index DDI = m / n, where n is the total number of neighborhood data points and m is the number of data points with a similarity lower than a preset threshold θ. Based on this index, it is determined whether the target data has spatiotemporal anomalies.

9. The road engineering low-carbon data trusted interactive system according to claim 6 is characterized in that: The structural path verification unit in the multi-dimensional verification module includes: A path extraction unit, used to extract the upstream path of the target data node; The standard path comparison unit is used to call the system's preset standard lifecycle path set and compare the phase labels of the nodes in the actual path with the standard path for semantic similarity; Matching score calculation unit, used to count the number of successfully matched stages and calculate the structural path matching score based on the stage weight vector; The structure integrity determination unit is used to determine whether the structure path of the target data is complete according to the matching score result.

10. The road engineering low-carbon data trusted interactive system according to claim 6, characterized in that: The carbon factor mapping verification unit in the multi-dimensional verification module includes: A carbon causal graph construction unit is used to extract carbon-related behavior nodes and their causal dependencies based on the upstream logical path of the target node in the data context tree, and construct a carbon causal graph structure; The carbon factor correction unit is used to query the working condition database based on parameters such as equipment model, operating load, construction method and ambient temperature, and correct the original carbon emission factor value; Theoretical carbon emission calculation unit, used to calculate the theoretical carbon emissions of the target node using the material usage and the modified carbon factor on all causal paths; The collaborative consistency analysis unit is used to compare the actual recorded values ​​of the target node and the upstream node with the theoretical estimated values. If the deviations are within the tolerable error range, the collaborative consistency score is calculated. If the score is lower than the threshold or the error is too large, it is marked as carbon causal abnormal data.

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