A low-carbon landscape building material carbon footprint tracing and alarm system based on a knowledge graph

Abstract

This invention relates to the field of low-carbon landscape construction technology, and particularly to a knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials. The system includes a graph construction module that integrates carbon emission data and environmental correlation data throughout the entire lifecycle of landscape building materials to establish a carbon footprint knowledge graph; an identification and traceability module that obtains the identification information of incoming building materials and matches the building material nodes and associated carbon footprint data corresponding to the identification information from the carbon footprint knowledge graph; a carbon emission calculation module that calculates the carbon emissions based on the associated carbon footprint data and corresponding construction stage data; an early warning and interception module that compares the total carbon emissions with a preset carbon emission threshold; and an optimization and recommendation module that attributes and displays the total carbon emissions. In this invention, by parsing the identification information of incoming building materials and combining it with the knowledge graph node associations, the total carbon emissions throughout the entire process are calculated, fundamentally preventing non-compliant high-carbon materials from entering the construction stage.

Description

Technical Field

[0001] This invention relates to the field of low-carbon landscape construction technology, and in particular to a carbon footprint traceability and alarm system for low-carbon landscape building materials based on knowledge graphs. Background Technology

[0002] In the current field of landscape construction, the scientific management of carbon emissions has become a key aspect of achieving low-carbon construction goals. Currently, most carbon emission monitoring systems on the market focus on static accounting and centralized data display after project completion. These traditional solutions are typically limited in function and lack timeliness, making it difficult to achieve real-time data tracking and dynamic monitoring throughout the entire lifecycle of a landscape project, including early design, material selection, logistics, and on-site construction. This fails to meet the refined and comprehensive management needs of modern low-carbon landscape construction.

[0003] Existing technologies can only perform post-event accounting and lack the ability to trace carbon footprint topology and intelligent reasoning based on knowledge graphs. The system cannot identify the hidden carbon emissions and illegal replacement behaviors in the life cycle of building materials, which makes it easy for high-carbon materials to flow into the construction site. Carbon management is in a situation of lagging monitoring and lack of traceability, and it cannot empower positive design from the source, making it difficult to achieve the goal of low-carbon construction of garden landscape. Summary of the Invention

[0004] To address the above shortcomings, this invention provides a knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials. It aims to improve the problems of lagging carbon emission calculation, lack of full life cycle tracking capabilities, and inability to reason about and intercept hidden high-carbon risks and illegal material substitution at the source.

[0005] In a first aspect, the present invention provides the following technical solution: a low-carbon landscape building materials carbon footprint traceability and alarm system based on knowledge graphs, comprising, The graph construction module integrates carbon emission data and environmental correlation data throughout the entire life cycle of landscape building materials to establish a knowledge graph of building materials carbon footprint. The identification and traceability module obtains the identification information of the building materials entering the site, and matches the building material nodes corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph; The carbon emission calculation module calculates the total carbon emissions of the incoming building materials throughout the entire construction process based on the associated carbon footprint data and the corresponding construction stage data. The early warning and interception module compares the total carbon emissions with a preset carbon emission threshold, and triggers a high-carbon alarm signal and performs material interception actions when the comparison fails. The optimization recommendation module attributes and displays the comprehensive carbon emissions, and based on preset performance index constraints, retrieves and outputs the building material combination that meets the performance index constraints and has the lowest comprehensive carbon emissions from the building material carbon footprint knowledge graph.

[0006] Preferably, in the graph construction module, the step of establishing a knowledge graph of building material carbon footprint includes: Obtain basic carbon emission data for the mining, production, processing, logistics, and on-site construction of landscape building materials; Collect carbon emission factors from external power grid systems and real-time operational records of logistics networks; The basic carbon emission data, the carbon emission factor of the external power grid system, and the real-time operation record of the logistics network are input into the database as node attribute features to establish a knowledge graph of the building materials carbon footprint with interdependent network relationships between nodes.

[0007] Preferably, in the identification and traceability module, the step of matching the building material node corresponding to the identification information and the associated carbon footprint data includes: The unique identification code of the material is obtained by parsing the identification information; Retrieve a unique building material entity node in the building material carbon footprint knowledge graph that matches the unique identification code of the material. Along the dependency network of the unique building material entity node, extract all historical carbon footprint data associated with each link in the path from the raw material mining node to the current on-site construction node.

[0008] Preferably, in the carbon emission calculation module, the step of calculating the comprehensive carbon emissions of the incoming building materials throughout the entire construction process includes: Extract the carbon emission values ​​of basic materials from the associated carbon footprint data; In the building material carbon footprint knowledge graph, traverse the path outward along the building material node to find the associated construction process node and associated mechanical equipment node that are bound to the building material node; Obtain the additional consumption parameters and basic carbon emission factors corresponding to the associated construction process nodes and the associated mechanical equipment nodes, and calculate the implicit additional carbon emission values. The total carbon emissions are obtained by adding the carbon emission values ​​of the basic materials to the implicit additional carbon emission values.

[0009] Preferably, in the carbon emission calculation module, the step of calculating the comprehensive carbon emissions of the incoming building materials throughout the entire construction process includes: When it is detected that the initial historical carbon emission data of the incoming building materials is missing, the material density attribute, origin attribute, and processing method attribute of the incoming building materials are extracted. The extracted attributes are compared with the building material nodes with known carbon emission values ​​in the building material carbon footprint knowledge graph to calculate the similarity of attribute features. The carbon emission value corresponding to the known building material node with the highest feature similarity is selected as the benchmark for predicting carbon emissions, and it is included in the comprehensive carbon emissions.

[0010] Preferably, in the early warning and interception module, the steps of triggering the high-carbon alarm signal and executing the material interception action include: Obtain information from on-site construction personnel regarding proposed replacement building materials and original design materials. In the building material carbon footprint knowledge graph, the parent classification nodes of the proposed replacement building material and the original design building material are compared to determine the equivalence of material functions. When the material functional equivalence determination result is consistent and the comprehensive carbon emission of the building material to be replaced is greater than the comprehensive carbon emission of the original design building material, it is determined that an illegal high-carbon material replacement has occurred and a high-carbon alarm signal is triggered.

[0011] Preferably, in the early warning and interception module, the steps of triggering the high-carbon alarm signal and executing the material interception action include: When the threshold comparison fails and the high carbon alarm signal is triggered, a digital interception command carrying the reason for exceeding the standard is generated. The digital interception command is sent to the construction site access control system and the on-site audible and visual alarm equipment is activated to provide a physical prompt. Block the entry approval process for the building materials and lock the backend data permissions.

[0012] Preferably, in the optimization recommendation module, the step of attributing and displaying the comprehensive carbon emissions includes: The portion of the total carbon emissions exceeding the preset carbon emission threshold is decomposed into carbon emissions from the material itself, carbon emissions from logistics and transportation, and carbon emissions from on-site construction. Each of the decomposed abnormal carbon emission values ​​is numerically bound to the corresponding responsibility node in the building materials carbon footprint knowledge graph; According to the topological network hierarchy, the responsible nodes that are bound to abnormal carbon emission values ​​are highlighted on the system visualization interface, and the carbon footprint source attribution results are output.

[0013] Preferably, in the optimization recommendation module, the step of retrieving and outputting the building material combination that satisfies the performance index constraints and has the lowest overall carbon emissions from the building material carbon footprint knowledge graph includes: Extract the load-bearing requirement parameters, durability requirement parameters, and corrosion resistance requirement parameters from the performance index constraints. Retrieve and filter all candidate building material nodes that meet the three required parameters in the building material carbon footprint knowledge graph; All candidate building material nodes are automatically sorted according to the comprehensive carbon emission from low to high. The candidate building material node combination with the highest ranking is extracted to form the lowest carbon building material combination and output to the front end for display.

[0014] Secondly, this invention provides the following technical solution: a method for tracing and alerting the carbon footprint of low-carbon landscape building materials based on knowledge graphs, comprising: Step S1: Integrate carbon emission data and environmental correlation data throughout the entire life cycle of landscape building materials to establish a knowledge graph of building material carbon footprint; Step S2: Obtain the identification information of the incoming building materials, and match the building material node corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph; Step S3: Calculate the total carbon emissions of the incoming building materials throughout the entire construction process based on the associated carbon footprint data and the corresponding construction stage data. Step S4: Compare the total carbon emissions with the preset carbon emission threshold, and trigger a high carbon alarm signal and perform a material interception action when the comparison fails. Step S5: Attribution and display of the comprehensive carbon emissions, and based on preset performance index constraints, retrieve and output the building material combination that meets the performance index constraints and has the lowest comprehensive carbon emissions from the building material carbon footprint knowledge graph.

[0015] The present invention has the following beneficial effects: 1. In this invention, by parsing the identification information of the building materials entering the site and combining it with the knowledge graph node association, the comprehensive carbon emissions of the whole process are calculated. At the same time, by comparing the parent nodes of the building materials to make a functional equivalence judgment, once it is found that the comprehensive carbon emissions exceed the standard or that there is illegal material replacement, the system immediately triggers an alarm command and links the on-site access control equipment to perform interception, fundamentally preventing non-compliant high-carbon materials from entering the construction process.

[0016] 2. In this invention, the system can extract data from the entire chain of raw material mining, logistics and construction along the dependency relationship of building material nodes, and break down the carbon emissions that exceed the threshold into three specific dimensions: material body, transportation distance and on-site construction. The decomposed values ​​are bound to the specific responsible nodes in the map and displayed, so that managers can intuitively identify the exact link that leads to high carbon emissions and make targeted adjustments.

[0017] 3. In this invention, the system can receive engineering performance constraints such as load-bearing capacity, durability, and corrosion resistance. It automatically retrieves and filters candidate building materials that meet the objective physical requirements in the building material carbon footprint knowledge graph. These candidate materials that meet the conditions are sorted from low to high in terms of comprehensive carbon emissions, and the combination of building materials with the lowest carbon footprint is output. Thus, the optimal material selection scheme is given under the premise of ensuring the quality of landscape construction. Attached Figure Description

[0018] Figure 1 This is a system module diagram of a knowledge graph-based low-carbon landscape building material carbon footprint tracing and alarm system proposed in this invention. Figure 2 This is a flowchart of a method for tracing and alarming the carbon footprint of low-carbon landscape building materials based on knowledge graphs, as proposed in this invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0020] In a first embodiment of the present invention, the present invention provides a knowledge graph-based carbon footprint tracing and alarm system for low-carbon landscape building materials, such as... Figure 1 As shown, it includes the following steps: The graph construction module integrates carbon emission data and environmental correlation data throughout the entire life cycle of landscape building materials to establish a knowledge graph of building materials carbon footprint. In the graph construction module, the steps for building a knowledge graph of building materials carbon footprint include: Obtain basic carbon emission data for the mining, production, processing, logistics, and on-site construction of landscape building materials; Collect carbon emission factors from external power grid systems and real-time operational records of logistics networks; Basic carbon emission data, carbon emission factors of external power grid systems, and real-time operation records of logistics networks are input into the database as node attribute features to establish a knowledge graph of building material carbon footprint with interdependent network relationships among nodes. Specifically, the map construction module acquires basic carbon emission data from the mining, processing, logistics, and on-site construction of landscape building materials. The system is equipped with a basic carbon accounting engine, which reads energy consumption, material loss rates, and equipment operating parameters for the corresponding stages from a pre-set life cycle assessment database. Based on the read parameters, the system establishes a static stage carbon emission calculation model, using the following formula: ; in, This represents basic carbon emission data for a specific process. This indicates the total number of different types of raw materials consumed. Indicates the first The physical consumption of various raw materials. Indicates the first The carbon emission factor per unit of raw materials; This indicates the total number of different types of energy consumed. Indicates the first The consumption of this type of energy, Indicates the first The carbon emission factor per unit of energy.

[0021] The graph construction module collects real-time operational records of carbon emission factors from the external power grid system and the logistics network. The system is equipped with an application programming interface (API) that establishes a communication connection with the external regional power grid data center server. The system receives energy structure proportion data from the power grid data center and establishes a dynamic update model for the power grid carbon factor. The calculation formula is as follows: ; in, express The carbon emission factor of the external power grid system at any given time. This indicates the total number of power generation types connected to the power grid. express Time of the first The power share of each type of power generation in the total power grid. Indicates the first The basic carbon emission coefficient for each type of power generation.

[0022] Simultaneously, the system establishes a communication connection with the logistics business scheduling system to obtain the actual load capacity and driving trajectory parameters of the transport vehicles, and establishes a dynamic logistics carbon emission model. The calculation formula is as follows: ; in, This represents the dynamic logistics carbon emission value recorded in the real-time operation log of the logistics network. Indicates the total number of transport segments. Indicates the first The actual driving distance of each road segment Indicates the first The actual load capacity of each road section Indicates the first The fuel consumption coefficient per unit mass per 100 kilometers under the corresponding road conditions for each road segment. This indicates the carbon emission factor of fuel.

[0023] The graph construction module inputs basic carbon emission data, carbon emission factors from the external power grid system, and real-time operation records of the logistics network as node attribute features into the database to establish a knowledge graph of building materials carbon footprint with interdependent network relationships between nodes. The system defines the knowledge graph as a directed attribute graph. .in, This represents a set of nodes, including entities of landscape building materials, production equipment, and transport vehicles. Represents a set of directed edges; A set of attributes representing nodes and edges.

[0024] The system will obtain , as well as Transformed into the feature vector of the corresponding entity node After establishing directed edges between nodes, the system constructs a graph carbon footprint accumulation and propagation model. (Directed edges) Indicates from node To the node The system assigns a weight matrix to each directed edge to determine the flow relationship. .node The formula for calculating the cumulative carbon footprint is as follows: ; in, Represents a node The total carbon footprint characteristic value after aggregating upstream transfer paths, Represents a node Its own direct carbon emission characteristics, Represents all pointer nodes The set of preceding adjacent nodes, Indicates the preceding node To the node Carbon allocation weighting coefficients during the circulation process. System traversal set. All nodes in the graph output a knowledge graph structure data of building material carbon footprint with quantified carbon transmission characteristics.

[0025] The identification and traceability module obtains the identification information of the building materials entering the site, and matches the building material nodes corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph; In the identification and traceability module, the steps of matching the building material nodes corresponding to the identification information and the associated carbon footprint data include: The unique identification code of the material is obtained by parsing the identification information; Search the building material carbon footprint knowledge graph for a unique building material entity node that matches the material's unique identification code; Along the dependency network of the unique building material entity node, extract all historical carbon footprint data associated with each link in the path from the raw material mining node to the current on-site construction node; Specifically, the identification and traceability module obtains the identification information of the incoming building materials and matches the building material nodes corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph.

[0026] In the identification and traceability module, the identification information is parsed to obtain a unique material identification code. The system is configured with an identification parsing algorithm model and receives the physical identification signal matrix from the front-end scanning device. After denoising and binarizing the signal matrix, the system inputs it into a decoding mapping function to calculate the feature vector of the material's unique identification code. The calculation formula is as follows: ; in, This represents the preset decoding weight matrix. This represents the bias vector. This represents a non-linear activation function. The system outputs a feature vector. It is converted into a standardized string format and used as a unique material identification code for the incoming building material.

[0027] The system retrieves unique building material entity nodes that match the material's unique identification code within the building materials carbon footprint knowledge graph. The system is configured with an entity node retrieval and matching model within the established building materials carbon footprint knowledge graph. The process initiates a traversal query. For a set of nodes, This is a node attribute matrix. The system extracts each node from the node set. Encoding attribute feature field And establish a distance calculation formula with respect to the feature vector of the incoming building materials: ; The system calculates the target distance values ​​for all nodes. The system sets the objective function as follows: ; When the minimum distance value When the value is zero or less than the system's set tolerance threshold, the system determines that the search match is successful and will... A unique building material entity node is established that matches the unique identification code of the material.

[0028] Following the dependency network of unique building material entity nodes, extract all historical carbon footprint data associated with each step along the path from the raw material mining node to the current on-site construction node. The system configures a graph reverse traversal algorithm model to match the unique building material entity nodes. For terminal nodes, a set of directed edges based on the graph. Perform reverse tracing. The system defines the rules for reverse adjacency lookup: for any node... Its upstream node set .

[0029] The system uses an iterative search method to construct a set of tracing path nodes containing all ancestor nodes. And determine the complete path by connectivity determination. The system extracts historical carbon footprint feature values ​​corresponding to all nodes along the path, and constructs a historical link-related carbon footprint dataset. The extraction formula is as follows: ; in, This represents the maximum level depth of the reverse traversal, corresponding to the longest topological distance from the on-site construction node to the raw material mining node; express The The set of upstream nodes of the layer; Represents a node The cumulative carbon footprint values ​​generated during the map construction phase. The system will extract the dataset. Stored in the cache as input data for subsequent carbon emission calculations.

[0030] The carbon emission calculation module calculates the total carbon emissions of incoming building materials throughout the entire construction process based on the associated carbon footprint data and the corresponding construction stage data. In the carbon emission calculation module, the steps for calculating the overall carbon emissions of incoming building materials throughout the entire construction process include: Extract carbon emission values ​​of basic materials from associated carbon footprint data; In the knowledge graph of building material carbon footprint, traverse the path outward along the building material node to find the associated construction process node and associated mechanical equipment node that are bound to the building material node; Obtain the additional consumption parameters and basic carbon emission factors corresponding to the associated construction process nodes and associated mechanical equipment nodes, and calculate the implicit additional carbon emission values. The total carbon emissions are obtained by adding the carbon emissions from the basic materials to the implicit additional carbon emissions. In the carbon emission calculation module, the steps for calculating the overall carbon emissions of incoming building materials throughout the entire construction process include: When it is detected that the initial historical carbon emission data of the incoming building materials is missing, the material density attribute, origin attribute, and processing method attribute of the incoming building materials are extracted. The extracted attributes are compared with the building material nodes with known carbon emission values ​​in the building material carbon footprint knowledge graph to calculate the similarity of attribute features. The carbon emission values ​​corresponding to the known building material nodes with the highest feature similarity are selected as the benchmark for predicting carbon emissions and included in the comprehensive carbon emissions. Specifically, the system extracts the carbon emission values ​​of basic materials from the associated carbon footprint data. It parses the historical associated carbon footprint data set in the cache and extracts the accumulated carbon footprint values ​​of the corresponding building material nodes up to the on-site construction nodes as the carbon emission values ​​of the basic materials. The system employs a forward expansion algorithm model for the building materials carbon footprint knowledge graph. It traverses outwards from building material nodes to find associated construction process nodes and associated machinery nodes bound to the building material nodes. Starting with the unique building material entity node corresponding to the currently arriving building material, the system performs a breadth-first traversal along the set of outgoing dependency edges. By verifying the classification attribute labels of adjacent nodes, the system filters and extracts nodes belonging to the construction process category, forming a set of associated construction process nodes. Extract nodes belonging to the mechanical equipment category to form a set of associated mechanical equipment nodes. .

[0031] The system obtains the additional consumption parameters and basic carbon emission factors corresponding to associated construction process nodes and associated mechanical equipment nodes, and calculates the implicit additional carbon emission values. The system establishes an implicit carbon emission extrapolation model for the set... and For each associated node, extract the corresponding additional consumption parameters and basic carbon emission factor from the node attribute fields. Implicit additional carbon emission values. The calculation formula is as follows: ; in, Indicates associated construction process nodes Corresponding process material consumption This indicates the basic carbon emission factor of the corresponding process materials; Indicates associated mechanical equipment nodes runtime parameters, This indicates the rated power parameter of the equipment. This indicates the corresponding equipment energy base carbon emission factor.

[0032] The total carbon emissions are calculated by adding the carbon emissions from the basic materials to the implicit additional carbon emissions. The system is equipped with a linear summation unit to execute the following calculation formula: ; in, This represents the total carbon emissions of the building materials used in the construction process. The system will calculate the emissions. It is stored in a specific address register in memory.

[0033] In the carbon emission calculation module, when missing initial historical carbon emission data is detected for incoming building materials, the material density, origin, and processing method attributes of the materials are extracted. The system is configured with a data missing determination logic unit; when the parsed basic material carbon emission value is marked as null or does not reach a preset data confidence threshold, the system determines that initial historical carbon emission data is missing. The system calls the feature extraction interface to read the material density attribute value of the incoming building materials. Origin attribute values ​​represented by latitude and longitude coordinates and the processing method attribute value represented by digital encoding They are then spliced ​​and assembled into a feature vector of the building materials to be predicted. .

[0034] The extracted attributes are compared with the known carbon emission values ​​of building material nodes in the building material carbon footprint knowledge graph to calculate attribute feature similarity. The system is configured with a node similarity calculation model based on metric learning to extract a set of known building material nodes with complete carbon emission values ​​from the graph. The properties of each known building material node are transformed into a reference feature vector of the same dimension as the building material to be predicted. The system calculates the multidimensional weighted distance between the feature vector of the building material attribute to be predicted and each reference feature vector. The calculation formula is as follows: ; in, , , These represent the system's preset feature weight coefficients for density, origin, and processing method, respectively. A binary decision function representing the difference in processing method categories, taking a value of 0 when the categories are the same and a value of 1 when they are different.

[0035] The carbon emission value corresponding to the known building material node with the highest feature similarity is selected as the benchmark for predicted carbon emissions, and this value is included in the overall carbon emission estimate. The system sets the target node with the highest similarity. The search function is as follows: ; System extracts target nodes The carbon emission values ​​bound in the attribute field are used as a baseline for predicting carbon emissions. The system replaces the missing basic material carbon emission values ​​with baseline predicted carbon emissions, inputs them into the linear summation unit, and updates the formula for calculating the overall carbon emissions as follows: ; The system outputs an updated comprehensive carbon emission value, which serves as the data basis for subsequent threshold comparisons.

[0036] The early warning and interception module compares the total carbon emissions with the preset carbon emission threshold, and triggers a high carbon alarm signal and performs material interception actions when the comparison fails. In the early warning and interception module, the steps for triggering the high-carbon alarm signal and executing the material interception action include: Obtain information from on-site construction personnel regarding proposed replacement building materials and original design materials. In the knowledge graph of building material carbon footprint, the parent classification nodes of the building materials to be replaced and the original design building materials are compared to determine the equivalence of material functions. When the material functional equivalence determination result is consistent and the comprehensive carbon emission of the building material to be replaced is greater than the comprehensive carbon emission of the original design building material, it is determined that an illegal high-carbon material replacement has occurred and a high-carbon alarm signal is triggered. In the early warning and interception module, the steps for triggering the high-carbon alarm signal and executing the material interception action include: When the threshold comparison fails and a high carbon alarm signal is triggered, a digital interception command carrying information indicating the reason for exceeding the standard is generated. The digital interception command is sent to the construction site access control system and the on-site audible and visual alarm equipment is activated to provide a physical prompt. Block the entry approval process for building materials and lock backend data access; Specifically, the system retrieves the proposed replacement building materials and the original design building materials reported by on-site construction personnel. The system is equipped with a data receiving interface, through which it receives data packets sent by front-end terminal devices containing the identification codes of the proposed replacement building materials and the original design building materials. The system parses the data packets and extracts the entity vectors of the proposed replacement building materials, which are mapped to the system database. Physical vector of building materials in the original design .

[0037] In the building materials carbon footprint knowledge graph, the parent category nodes of the proposed replacement building materials and the original design building materials are compared to determine the equivalence of their functions. The system is configured with a graph hierarchy retrieval algorithm model within the knowledge graph topology. In, respectively and Starting from the corresponding graph node, perform a depth-first traversal along the directed edges representing material category affiliations to extract the corresponding set of parent category nodes. and The system establishes a functional equivalence determination logic function. The calculation formula is as follows: ; The system performs set intersection operations; if two sets have an intersection, the result is determined by the logic function. When the output value is 1, the system determines that the material function is equivalent and the result is consistent.

[0038] When the material functional equivalence assessment result is consistent and the comprehensive carbon emissions of the proposed replacement building materials exceed the comprehensive carbon emissions of the original design building materials, a violation of high-carbon material replacement is determined, and a high-carbon alarm signal is triggered. The system is configured with a numerical assessment model for replacement compliance and reads the comprehensive carbon emissions of the proposed replacement building materials output by the carbon emission calculation module. Combined carbon emissions of building materials in the original design The system sets violation trigger parameters. The calculation formula is as follows: ; in, For a unit step function, when the input variable Output 1 when The system outputs 0 when the violation trigger parameter is calculated using the formula above. When the value is 1, the system determines that an illegal high-carbon material replacement action has occurred and writes a high-carbon alarm signal to the system's internal event bus.

[0039] In the early warning and interception module, when a threshold comparison fails and a high-carbon alarm signal is triggered, a digital interception command carrying information indicating the reason for exceeding the standard is generated. The system is configured with a comparison calculation unit and a command encoding unit. The comparison calculation unit receives the total carbon emissions of the currently arriving building materials. Compared with the preset carbon emission threshold And calculate the difference between the two. When the difference When the system detects a high-carbon alarm signal on the event bus, the instruction encoding unit performs a data encapsulation operation. The instruction encoding unit then encapsulates the difference data... The system concatenates the current building material identification code with the abnormal status feature code translated by the system's judgment logic, threshold overflow, or illegal replacement, to generate a digital interception instruction with a fixed message header.

[0040] The system sends digital interception commands to the construction site access control system and triggers on-site audible and visual alarm devices for physical alerts. The system is equipped with a hardware communication interface. Following a preset control area network bus protocol or transmission control protocol, the system sends the encapsulated digital interception commands to the microcontroller of the construction site access control system. The microcontroller parses the digital interception command message and outputs a low-level interlocking control level through its input / output pins to cut off the power supply circuit for the access control gate. Simultaneously, the microcontroller outputs a high-level drive signal through its relay module to close the power supply circuit for the on-site audible and visual alarm devices, triggering a physical alert.

[0041] The system blocks the entry approval process for incoming building materials and locks backend data permissions. The system is equipped with a database transaction processing unit. Simultaneously with issuing the digital interception command, the system executes a status update transaction in the system's business database. The system locates the primary key record in the data table corresponding to the incoming building material and changes its entry approval process status field from "pending" to "blocked and terminated." The system then calls the access control list to locate the supply chain operator account and equipment identifier associated with this batch of abnormal building materials, sets the corresponding backend business data permission flag to 0, downgrades its permissions to read-only, and completes the backend data permission locking operation.

[0042] The recommendation module is optimized to attribute and display the overall carbon emissions. Based on the preset performance index constraints, the combination of building materials that meets the performance index constraints and has the lowest overall carbon emissions is retrieved from the building materials carbon footprint knowledge graph and output. In the optimized recommendation module, the steps for attributing and displaying comprehensive carbon emissions include: The portion of total carbon emissions exceeding the preset carbon emission threshold is broken down into carbon emissions from the material itself, carbon emissions from logistics and transportation, and carbon emissions from on-site construction. Each abnormal carbon emission value extracted is numerically bound to the corresponding responsible node in the building materials carbon footprint knowledge graph. According to the topological network hierarchy, the responsible nodes that are bound to abnormal carbon emission values ​​are highlighted on the system visualization interface, and the carbon footprint source attribution results are output. In the optimization recommendation module, the steps of retrieving and outputting the building material combination that meets the performance constraints and has the lowest overall carbon emissions from the building material carbon footprint knowledge graph include: Extract the load-bearing requirement parameters, durability requirement parameters, and corrosion resistance requirement parameters from the performance index constraints; Search and filter all candidate building material nodes that meet the three required parameters in the building material carbon footprint knowledge graph; All candidate building material nodes are automatically sorted in order of comprehensive carbon emissions from low to high. The candidate building material node combination with the highest ranking is extracted to form the lowest carbon building material combination and output to the front end for display. Specifically, the portion of the total carbon emissions exceeding the preset carbon emission threshold is decomposed into carbon emissions from the material itself, carbon emissions from logistics and transportation, and carbon emissions from on-site construction. The system is configured with a data decomposition algorithm unit, which reads the exceedance difference parameter output by the comparison and calculation unit. ,in The system extracts the carbon emission values ​​of basic materials recorded in the carbon emission calculation module. Carbon emission values ​​in the logistics and transportation process and implied additional carbon emission figures The system establishes a numerical decomposition matrix based on the objective proportion of each carbon emission source in the total carbon emissions. The calculation formula is as follows: ; in, This indicates that the carbon emissions of the material itself exceed the standard. This indicates the amount of carbon emissions exceeding the standard in logistics and transportation. This indicates that the carbon emissions during on-site construction exceeded the standard.

[0043] Each decomposed abnormal carbon emission value is numerically bound to the corresponding responsible node in the building materials carbon footprint knowledge graph. The system configures the node mapping update logic and reads the set of source path nodes generated by the graph reverse traversal algorithm model. The system performs mapping binding by recognizing the node's category attribute labels: ... The numerical values ​​are written into the abnormal state data segment of the entity node with the classification attribute of raw material mining and processing; The numerical values ​​are written into the abnormal state data segment with the classification attribute of transport vehicles and logistics network nodes; Numerical values ​​are written into the abnormal status data segment of the on-site construction equipment and construction process nodes, generating structured map data with abnormal numerical labels.

[0044] The system displays the responsible nodes with abnormal carbon emission values ​​in a highlighted manner on the system's visualization interface according to the graph topology network hierarchy, outputting the carbon footprint source attribution results. The system is configured with a graphics rendering processor to read the updated graph topology data and generate node position parameters in a two-dimensional screen coordinate system based on the graph hierarchy depth parameters. For responsible nodes with non-zero abnormal state data segments, the system performs color channel reallocation, calculating the red, green, and blue pixel channel values ​​based on the abnormal value magnitude. The calculation formula is as follows: ; The system sets the output values ​​of the green and blue channels to zero, and uses this formula to map the node with the largest outlier value to a pure red pixel output. The system then sends the rendered image display signal, containing the node topological coordinates and red, green, and blue pixel values, to the visualization display terminal screen matrix via a multimedia interface.

[0045] In the optimization and recommendation module, load-bearing requirement parameters, durability requirement parameters, and corrosion resistance requirement parameters are extracted from the performance index constraints. The system is configured with a data receiving port to receive digital format configuration messages sent by the pre-engineering design terminal. The system extracts structured physical requirement feature vectors through message parsing logic. .in, This represents the minimum bearing stress parameter. This indicates the environmental test degradation period parameter. This indicates the corrosion resistance rating parameter.

[0046] The system retrieves and filters all candidate building material nodes that meet the three required parameters from the building material carbon footprint knowledge graph. A multi-dimensional conditional Boolean filtering model is established to traverse all building material nodes in the graph. The system reads the corresponding objective physical test data from the node attribute feature matrix, including the node bearing stress. Anti-fading cycle and corrosion resistance level The system executes the filtering logic judgment function. The calculation formula is as follows: ; The system stores all nodes with a function output value of 1 in the physical address space to establish a candidate building material node set. .

[0047] The system automatically sorts all candidate building material nodes according to their overall carbon emissions from lowest to highest, extracts the top-ranked candidate building material node combination to form the lowest-carbon building material combination, and displays it on the front end. The system is configured with a fast sorting algorithm model based on the set of candidate building material nodes. aggregated carbon footprint feature value of each node Based on the sorting criteria, comparison and position swap instructions are executed. The system generates a monotonically increasing sequence of nodes. The sequence satisfies The system extracts the sequence. Header first and second node objects The system generates a result data frame containing the identifier of the lowest carbon building material combination and its corresponding carbon emission value, along with the data of its bound dependent nodes. This result data frame is then pushed to the response buffer of the front-end display application via a Hypertext Transfer Protocol interface. Example 2

[0048] In the actual construction scenario of a large-scale urban ecological landscape project, the original design plan specified the use of low-carbon permeable concrete and prefabricated recycled structural components of a particular specification. During the material arrival phase, due to supply chain shortages or cost-cutting, the construction party illegally replaced these with traditional silicate concrete and cast-in-place components, which have similar physical load-bearing performance but extremely high carbon emission factors. In this scenario, there are defects such as delayed data collection and a static, one-way accounting mechanism. The management system relies solely on manual filling of construction ledgers later, lacking real-time cross-validation capabilities based on underlying material identification and full lifecycle data. This results in the system's inability to identify the hidden high-carbon substitution behavior at the moment of material arrival, failing to trigger alarms and link access control for physical interception. This allows high-carbon materials to directly enter the construction flow and solidify in the building structure, ultimately causing irreversible exceedances of the overall project's carbon emissions. Furthermore, during the post-completion environmental audit, it is impossible to trace the exact carbon spill points and responsible parties. To solve these problems, this invention provides a knowledge graph-based method for tracing and alarming the carbon footprint of low-carbon landscape building materials, such as... Figure 2 As shown. The specific implementation process of this method is as follows: Step S1: Integrate carbon emission data and environmental correlation data throughout the entire life cycle of landscape building materials to establish a knowledge graph of building material carbon footprint; Step S2: Obtain the identification information of the incoming building materials, and match the building material nodes corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph; Step S3: Calculate the total carbon emissions of the building materials entering the site during the entire construction process based on the associated carbon footprint data and the corresponding construction stage data. Step S4: Compare the total carbon emissions with the preset carbon emission threshold, and trigger a high carbon alarm signal and perform material interception actions when the comparison fails. Step S5: Attribution and display of comprehensive carbon emissions, and based on preset performance index constraints, retrieve and output the building material combination that meets the performance index constraints and has the lowest comprehensive carbon emissions from the building material carbon footprint knowledge graph.

[0049] Specifically, in step S1, the system configures a basic carbon accounting engine to read the physical consumption of raw materials, energy consumption, and corresponding basic carbon emission factors from a preset life cycle assessment database. The system multiplies the physical consumption by the corresponding basic carbon emission factor, multiplies the energy consumption by the corresponding basic carbon emission factor, and sums all the products to obtain the basic carbon emission data for the static stage. The system connects to the external regional power grid data center server through an application programming interface to receive the current time period's power generation ratio data and basic carbon emission coefficients, and obtains the external power grid system's dynamic carbon emission factor through weighted summation. The system connects to the logistics business scheduling system to extract actual driving distance, actual load capacity, and road condition fuel consumption coefficients, and obtains the dynamic logistics carbon emission value through multiplication. The system converts the calculated basic carbon emission data, dynamic carbon emission factor, and dynamic logistics carbon emission value into feature vectors of entity nodes and stores them in a graph database. The system establishes directional connection edges according to the material flow process sequence and assigns carbon transfer weights based on the energy efficiency transfer ratio, completing the construction of a knowledge graph topology structure with quantified carbon transmission characteristics.

[0050] In step S2, the system receives the physical identifier signal matrix from the front-end scanning device and performs noise reduction and binarization processing. The system inputs the processed signal matrix into the decoding mapping function and outputs a standardized string representing the unique material identification code. The system extracts the coded attribute feature fields of all nodes in the node set of the building material carbon footprint knowledge graph and calculates the multidimensional spatial distance between the feature fields of each node and the standardized string. The system retrieves nodes with a spatial distance value of zero and establishes them as unique building material entity nodes. Using this unique building material entity node as the terminal node, the system performs a reverse tracing along the directional connection edges of the graph. The system extracts all ancestor nodes up to the raw material mining node layer by layer, summarizes the historical cumulative carbon footprint values ​​of all nodes on the tracing path, packages them to generate an associated carbon footprint dataset, and merges and stores it in the system cache.

[0051] Step S3: The system parses the associated carbon footprint data set in the cache and extracts the accumulated values ​​before reaching the on-site construction node as the basic material carbon emission values. Starting with the unique matched building material entity node, the system performs a breadth-first forward traversal, filtering for associated nodes categorized by construction technology and machinery. The system extracts the process material consumption, equipment runtime, equipment rated power, and corresponding carbon emission factors of the associated nodes, and obtains the implicit additional carbon emission values ​​through multiplication and summation. The system performs a linear summation of the basic material carbon emission values ​​and the implicit additional carbon emission values ​​to obtain the comprehensive carbon emission amount. When a null value is detected for the basic material carbon emission value, the system reads the material density attribute, origin coordinate attribute, and processing method attribute of the incoming building material, and performs a multi-dimensional weighted distance calculation with the building material nodes with known carbon emission values ​​in the knowledge graph. The system extracts the carbon emission value bound to the known node with the smallest distance value as the baseline prediction, replaces the null value, and includes it in the comprehensive carbon emission amount.

[0052] In step S4, the system receives the entity vectors of the proposed replacement building materials and the original design building materials sent by the terminal, and extracts the parent classification node sets of both from the knowledge graph by traversing upwards. When the intersection of the two parent classification node sets is not empty, the system confirms that the materials are functionally equivalent. When the materials are functionally equivalent and the comprehensive carbon emissions of the proposed replacement building materials are significantly greater than the comprehensive carbon emissions of the original design building materials, the system writes a high-carbon alarm signal to the internal event bus. The system calculates the difference between the comprehensive carbon emissions and the preset carbon emission threshold. For cases where the difference is positive and a high-carbon alarm signal exists on the event bus, the system uniformly executes the data encapsulation operation of the instruction encoding unit. The system concatenates the difference data, building material identification code, and abnormal status feature code into a digital interception instruction message. The system sends the message to the on-site access control microcontroller through the transmission control protocol, driving the microcontroller to output a low level to cut off the power supply circuit of the barrier gate, and simultaneously output a high level to close the power supply circuit of the audible and visual alarm device. The system synchronously overwrites the entry approval status field of this batch of building materials in the business database to "blocked and terminated", and sets the bound operator account permission flag to zero, forcibly downgrading it to read-only status.

[0053] Step S5: The system extracts the out-of-standard difference data and establishes a numerical decomposition matrix based on the objective numerical proportions of basic materials, logistics and transportation, and on-site construction. The out-of-standard difference is then broken down into three abnormal carbon emission values. Based on the classification attribute tags, the system writes these three abnormal carbon emission values ​​into the abnormal status data segment of the corresponding responsible entity. The system's graphics rendering processor reads the updated graph data, reallocates the red, green, and blue pixel channel values ​​based on the magnitude of the abnormal values, sets the green and blue channel outputs to zero, and renders the responsible node with the largest value as a pure red pixel and outputs it to the terminal screen. The system parses the preliminary engineering design report and extracts the minimum bearing stress parameter, anti-attenuation cycle parameter, and corrosion resistance level parameter. The system traverses the knowledge graph, extracts all building material nodes that simultaneously satisfy the three parameters from the physical test data, and stores them in a candidate set. The system calls a fast sorting algorithm, using the node aggregate carbon footprint feature value as the sorting criterion, to generate a monotonically increasing node sequence. The system extracts the first and second node objects at the beginning of the sequence and their dependent data, generates the lowest carbon building material combination result data frame, and pushes it to the front-end display interface.

[0054] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials, characterized in that, include: The graph construction module integrates carbon emission data and environmental correlation data throughout the entire life cycle of landscape building materials to establish a knowledge graph of building materials carbon footprint. The identification and traceability module obtains the identification information of the building materials entering the site, and matches the building material nodes corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph; The carbon emission calculation module calculates the total carbon emissions of the incoming building materials throughout the entire construction process based on the associated carbon footprint data and the corresponding construction stage data. The early warning and interception module compares the total carbon emissions with a preset carbon emission threshold, and triggers a high-carbon alarm signal and performs material interception actions when the comparison fails. The optimization recommendation module attributes and displays the comprehensive carbon emissions, and based on preset performance index constraints, retrieves and outputs the building material combination that meets the performance index constraints and has the lowest comprehensive carbon emissions from the building material carbon footprint knowledge graph.

2. The knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the graph construction module, the step of establishing a knowledge graph of building material carbon footprint includes: Obtain basic carbon emission data for the mining, production, processing, logistics, and on-site construction of landscape building materials; Collect carbon emission factors from external power grid systems and real-time operational records of logistics networks; The basic carbon emission data, the carbon emission factor of the external power grid system, and the real-time operation record of the logistics network are input into the database as node attribute features to establish a knowledge graph of the building materials carbon footprint with interdependent network relationships between nodes.

3. The knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the identification and traceability module, the step of matching the building material node corresponding to the identification information and the associated carbon footprint data includes: The unique identification code of the material is obtained by parsing the identification information; Retrieve a unique building material entity node in the building material carbon footprint knowledge graph that matches the unique identification code of the material. Along the dependency network of the unique building material entity node, extract all historical carbon footprint data associated with each link in the path from the raw material mining node to the current on-site construction node.

4. The knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the carbon emission calculation module, the step of calculating the comprehensive carbon emissions of the incoming building materials throughout the entire construction process includes: Extract the carbon emission values ​​of basic materials from the associated carbon footprint data; In the building material carbon footprint knowledge graph, traverse the path outward along the building material node to find the associated construction process node and associated mechanical equipment node that are bound to the building material node; Obtain the additional consumption parameters and basic carbon emission factors corresponding to the associated construction process nodes and the associated mechanical equipment nodes, and calculate the implicit additional carbon emission values. The total carbon emissions are obtained by adding the carbon emission values ​​of the basic materials to the implicit additional carbon emission values.

5. A knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the carbon emission calculation module, the step of calculating the comprehensive carbon emissions of the incoming building materials throughout the entire construction process includes: When it is detected that the initial historical carbon emission data of the incoming building materials is missing, the material density attribute, origin attribute, and processing method attribute of the incoming building materials are extracted. The extracted attributes are compared with the building material nodes with known carbon emission values ​​in the building material carbon footprint knowledge graph to calculate the similarity of attribute features. The carbon emission value corresponding to the known building material node with the highest feature similarity is selected as the benchmark for predicting carbon emissions, and it is included in the comprehensive carbon emissions.

6. A knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the aforementioned early warning and interception module, the steps of triggering the high-carbon alarm signal and executing the material interception action include: Obtain information from on-site construction personnel regarding proposed replacement building materials and original design materials. In the building material carbon footprint knowledge graph, the parent classification nodes of the proposed replacement building material and the original design building material are compared to determine the equivalence of material functions. When the material functional equivalence determination result is consistent and the comprehensive carbon emission of the building material to be replaced is greater than the comprehensive carbon emission of the original design building material, it is determined that an illegal high-carbon material replacement has occurred and a high-carbon alarm signal is triggered.

7. A knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the aforementioned early warning and interception module, the steps of triggering the high-carbon alarm signal and executing the material interception action include: When the threshold comparison fails and the high carbon alarm signal is triggered, a digital interception command carrying the reason for exceeding the standard is generated. The digital interception command is sent to the construction site access control system and the on-site audible and visual alarm equipment is activated to provide a physical prompt. Block the entry approval process for the building materials and lock the backend data permissions.

8. A knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the optimization recommendation module, the step of attributing and displaying the comprehensive carbon emissions includes: The portion of the total carbon emissions exceeding the preset carbon emission threshold is decomposed into carbon emissions from the material itself, carbon emissions from logistics and transportation, and carbon emissions from on-site construction. Each of the decomposed abnormal carbon emission values ​​is numerically bound to the corresponding responsibility node in the building materials carbon footprint knowledge graph; According to the topological network hierarchy, the responsible nodes that are bound to abnormal carbon emission values ​​are highlighted on the system visualization interface, and the carbon footprint source attribution results are output.

9. A knowledge graph-based carbon footprint traceability and alarm system for low-carbon landscape building materials according to claim 1, characterized in that, In the optimization recommendation module, the step of retrieving and outputting the building material combination that satisfies the performance index constraints and has the lowest overall carbon emissions from the building material carbon footprint knowledge graph includes: Extract the load-bearing requirement parameters, durability requirement parameters, and corrosion resistance requirement parameters from the performance index constraints. Retrieve and filter all candidate building material nodes that meet the three required parameters in the building material carbon footprint knowledge graph; All candidate building material nodes are automatically sorted according to the comprehensive carbon emission from low to high. The candidate building material node combination with the highest ranking is extracted to form the lowest carbon building material combination and output to the front end for display.

10. A method for tracing and alerting the carbon footprint of low-carbon landscape building materials based on knowledge graphs, characterized in that, A knowledge graph-based low-carbon landscape building materials carbon footprint tracing and alarm system according to any one of claims 1-9 includes: Step S1: Integrate carbon emission data and environmental correlation data throughout the entire life cycle of landscape building materials to establish a knowledge graph of building material carbon footprint; Step S2: Obtain the identification information of the incoming building materials, and match the building material node corresponding to the identification information and the associated carbon footprint data from the building material carbon footprint knowledge graph; Step S3: Calculate the total carbon emissions of the incoming building materials throughout the entire construction process based on the associated carbon footprint data and the corresponding construction stage data. Step S4: Compare the total carbon emissions with the preset carbon emission threshold, and trigger a high carbon alarm signal and perform a material interception action when the comparison fails. Step S5: Attribution and display of the comprehensive carbon emissions, and based on preset performance index constraints, retrieve and output the building material combination that meets the performance index constraints and has the lowest comprehensive carbon emissions from the building material carbon footprint knowledge graph.

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