A tunnel section carbon emission calculation system and method based on an AI intelligent safety helmet

Abstract

The application discloses a tunnel section carbon emission calculation system and method based on an AI intelligent safety helmet, relates to the technical field of shield tunnel section engineering carbon emission calculation, and constructs a construction period carbon emission benchmark database; the intelligent safety helmet collects construction mechanical equipment information through a built-in machine vision module, an equipment identification sensor group and a positioning module, identifies shield segment types and quantities through an AI algorithm model, determines safety helmet travel mileage data in a tunnel, synchronously identifies mechanical equipment running in the tunnel, combines multi-source sensor data to calculate carbon emission in real time, and calculates total carbon emission; the carbon emission of the tunnel section is displayed in real time; and the carbon emission is compared with that of a historical period and a similar tunnel.

Description

Technical Field

[0001] This invention relates to the field of carbon emission calculation technology for shield tunnel section engineering, and more specifically to a carbon emission calculation system and method for tunnel sections based on an AI smart safety helmet. Background Technology

[0002] Currently, the requirements for green and low-carbon infrastructure construction are becoming increasingly stringent, making the accurate measurement and dynamic management of carbon emissions during tunnel construction a pressing issue. Existing carbon emission calculation methods largely rely on post-construction statistics and static factors, resulting in poor timeliness, low accuracy, and difficulty in covering dynamic changes throughout the entire construction process. Particularly in the complex tunnel construction environment, real-time calculation of the amount of shield tunnel segments used, the operating status and location of construction machinery and equipment is extremely difficult, hindering the achievement of refined carbon emission accounting and real-time optimization guidance.

[0003] Therefore, given the shortcomings of existing technologies, how to provide a carbon emission calculation system and method for tunnel sections based on AI-powered smart safety helmets, which are essential for construction sites, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a carbon emission calculation system and method for tunnel sections based on AI smart safety helmets. It innovatively integrates wearable smart devices with real-time carbon emission calculation technology to achieve accurate and dynamic measurement of carbon emissions throughout the entire tunnel construction cycle. It also guides process optimization through visual feedback and builds a closed-loop management system of "calculation-early warning-control" to provide a dynamic carbon footprint management solution for green tunnel construction.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a tunnel section carbon emission calculation system based on an AI smart safety helmet, comprising: a tunnel information system, a smart safety helmet, a carbon emission analysis platform, and a multi-dimensional statistical report module;

[0006] The tunnel information system is used to integrate tunnel engineering parameters, equipment information and pipeline material data to build a carbon emission benchmark database for the construction cycle;

[0007] The smart safety helmet has a built-in device that identifies a sensor group, a machine vision module, a positioning module, a power module, and an AI algorithm model.

[0008] The power module is used to provide electrical energy;

[0009] The device identification sensor group, machine vision module, and positioning module are used to collect information about construction machinery and equipment.

[0010] The AI ​​algorithm model is used to identify the type and quantity of shield tunnel segments based on the data information of the construction machinery and equipment, determine the travel distance of the safety helmet in the tunnel, and calculate the carbon emissions based on the carbon emission benchmark database of the construction cycle.

[0011] The carbon emission analysis platform is used to display the carbon emissions in the tunnel section in real time;

[0012] The multi-dimensional statistical reporting module uses historical carbon emission data to compare carbon emissions during the construction phases and benchmark carbon emissions of similar tunnels in the horizontal direction.

[0013] Preferably, the device identification sensor group includes:

[0014] Millimeter-wave radar is used to detect the three-dimensional profile of operating machinery;

[0015] Multispectral cameras are used to verify the type of machinery by analyzing the shape of the equipment, while simultaneously observing the shape and quantity of the tube segments.

[0016] RFID readers are used to identify the type of construction machinery and equipment.

[0017] Vibration sensors are used to identify the operating status of construction machinery.

[0018] Preferably, the AI ​​algorithm model includes: a segment type identification model and a mechanical carbon emission model;

[0019] The segment type identification model employs a multi-scale segment classification network to identify the size and thickness of the segments and determine their type.

[0020] The mechanical carbon emission model adopts a hybrid calculation model that integrates an equipment power database and real-time operating condition perception.

[0021] Preferably, the smart safety helmet further includes an encryption and evidence storage module, which is used to encrypt carbon emission data and then upload it to the carbon emission analysis platform.

[0022] Preferably, a method for calculating carbon emissions in tunnel sections based on AI-powered smart safety helmets includes:

[0023] Import carbon emission models of different tunnel segments and tunnel construction carbon emission models, analyze the construction stage division, material list and mechanical equipment configuration scheme, and generate carbon emission calculation benchmark parameters;

[0024] The machine vision module of the smart safety helmet is activated, and data of the construction scene is captured by the multispectral camera. The device identification sensor group is triggered simultaneously to determine the type of machinery and collect the operating data of the construction machinery. The position of the safety helmet is determined by the positioning module.

[0025] The AI ​​algorithm model fuses the perceived multimodal data, matches the segment carbon emission factor library according to the corresponding segment type and quantity, matches the preset emission factor library according to the construction excavation machinery type, and dynamically calculates the instantaneous carbon emissions of the current construction face by combining the real-time power of the equipment and the fuel consumption rate.

[0026] The calculation results are encrypted in two channels and transmitted to the time-series database and blockchain node of the carbon emission analysis platform, respectively.

[0027] The carbon emission analysis platform receives data streams in real time and generates a three-dimensional carbon emission distribution model through spatial interpolation algorithms, while also distinguishing different types of carbon emissions.

[0028] The multi-dimensional statistical reporting module regularly generates carbon emission audit reports during the construction phase, compares the deviation rate between design values ​​and measured values, and outputs process optimization suggestions to the project management terminal based on the differences.

[0029] Preferably, the different types of carbon emissions include construction carbon emissions, segment carbon emissions, and mechanical equipment operation carbon emissions.

[0030] Preferably, the shape and number of tunnel segments within the interval are captured by a multispectral camera, and the number and corresponding type of tunnel segments in the current interval are counted using a tunnel segment type recognition model;

[0031] The emission factor for a single tunnel segment is determined based on the number and type of the identified segments, using the following formula:

[0032] Carbon emissions per segment = Number of segments × Carbon emission equivalent per prefabricated segment;

[0033] The carbon emission equivalent of a single precast segment is calculated from the list of materials, labor and equipment provided by the precast yard. Different types of segments have different carbon emission amounts.

[0034] Preferably, based on the identified travel mileage data and tunnel width, a construction carbon emission factor library is matched to determine the construction carbon emission factor per unit volume, as shown in the following formula:

[0035] Carbon emissions from construction machinery = tunnel cross-sectional area × location length × carbon emission factor per unit volume.

[0036] Preferably, when mechanical equipment and its corresponding type are identified, the carbon emission factor library for the mechanical type is matched, and the carbon emissions from the operation of the mechanical equipment are calculated as follows:

[0037] Mechanical carbon emissions = Σ (equipment power × operating time × fuel carbon emission coefficient).

[0038] As can be seen from the above technical solution, compared with the prior art, this invention discloses a carbon emission calculation system and method for tunnel sections based on AI smart safety helmets, constructing a carbon emission benchmark database for the construction cycle. The smart safety helmet collects information on construction machinery and equipment through its built-in machine vision module, equipment identification sensor group, and positioning module. By calling AI algorithm models to identify the type and quantity of shield tunnel segments and determine the safety helmet's mileage data in the tunnel, it simultaneously identifies the operating machinery and equipment in the tunnel and calculates the carbon emissions in real time by combining multi-source sensor data, calculating the total carbon emissions; it displays the carbon emissions of the tunnel section in real time; and compares them with the carbon emissions of similar tunnels in historical periods. This invention innovatively integrates wearable devices and carbon emission calculation technology to achieve accurate measurement of carbon emissions throughout the entire tunnel construction cycle. Through real-time visual feedback, it guides process optimization and constructs a closed-loop management system of "real-time calculation - instant analysis - effective control," providing a dynamic carbon footprint management solution for green tunnel construction. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 A schematic diagram of a tunnel section carbon emission calculation system based on an AI smart safety helmet is provided as an embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of the structure of the smart safety helmet of the present invention;

[0042] Figure 3 This is a schematic diagram of the "end-edge-chain" collaborative architecture used in the smart safety helmet of this invention;

[0043] Figure 4 This is a functional schematic diagram of the carbon emission analysis platform of the present invention;

[0044] Figure 5 This is a flowchart illustrating a specific implementation method of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] This invention discloses a carbon emission calculation system for tunnel sections based on an AI-powered smart safety helmet, such as... Figure 1 As shown, it includes: a tunnel information system, a smart safety helmet, a carbon emission analysis platform, and a multi-dimensional statistical report module;

[0047] The tunnel information system is used to integrate tunnel engineering parameters (such as design drawings, construction plans, and phase divisions), equipment information (such as machinery models and power), and pipeline material data (such as segment types and specifications), as well as a material and machinery carbon emission factor database (segment carbon emission factors and energy carbon emissions), and to build a carbon emission benchmark database for the construction cycle.

[0048] The segment information captured by the safety helmet spectral camera will have corresponding carbon emission factors, forming a known database. The construction cycle carbon emission benchmark database includes various carbon emission factor libraries for subsequent carbon emission calculations.

[0049] The smart safety helmet has a built-in device that identifies a sensor group, a machine vision module, a positioning module, a power module, and an AI algorithm model.

[0050] The power module is used to provide electrical energy;

[0051] The device identification sensor group, machine vision module, and positioning module are used to collect information about construction machinery and equipment.

[0052] The AI ​​algorithm model is used to identify the type and quantity of shield tunnel segments based on the data information of the construction machinery and equipment, determine the travel distance of the safety helmet in the tunnel, and calculate the carbon emissions based on the carbon emission benchmark database of the construction cycle.

[0053] The carbon emission analysis platform is used to display the carbon emissions in the tunnel section in real time;

[0054] The multi-dimensional statistical reporting module uses historical carbon emission data to compare carbon emissions during the construction phases and benchmark carbon emissions of similar tunnels in the horizontal direction.

[0055] Specifically, the smart safety helmet uses an embedded AI algorithm model to identify the type and quantity of tunnel segments, determines the helmet's position in the tunnel in real time (travel mileage data) through a positioning module, identifies the operating machinery in the tunnel, and calculates carbon emissions from tunnel segments, construction, and machinery operation in real time by combining multi-source sensor data. The raw environmental data and AI analysis results are then transmitted to the cloud platform through an encryption and evidence storage module.

[0056] The carbon emission analysis platform receives and processes encrypted data from the smart safety helmet, providing real-time visualization of dynamic carbon emissions within the tunnel section and generating carbon emission intensity heatmaps and trend analysis maps. It generates a three-dimensional carbon emission distribution model (bound to the spatial coordinates of the BIM model) using spatial interpolation algorithms, distinguishing between different types of carbon emissions (construction carbon emissions, segment carbon emissions, and mechanical equipment operation carbon emissions). It supports a sliding time window analysis mode, allowing for the retrospective analysis of the spatial distribution characteristics of carbon emissions during any construction period.

[0057] The multi-dimensional statistical reporting module compiles historical carbon emission data, enabling vertical comparisons of construction phases and horizontal benchmarking against similar tunnels. It regularly generates carbon emission audit reports for each construction phase, comparing the deviation rate between design and measured values. It also outputs process optimization suggestions to the project management terminal.

[0058] Specifically, the smart safety helmet includes: a helmet shell, which constitutes the main protective structure of the helmet and has an inner cavity;

[0059] The liner is fixedly installed on the top of the inner cavity of the helmet shell and is used to contact the head when worn;

[0060] A buffer layer is disposed between the helmet shell and the helmet liner;

[0061] The chin straps are detachably connected at both ends to the lower sides of the helmet shell;

[0062] The AI ​​algorithm model is installed in software form within a chip and is fixedly mounted on the rear outer surface or the rear cavity of the helmet shell.

[0063] A multispectral camera is embedded or detachably mounted on the front outer surface of the helmet shell (such as the center above the brim) via a bracket, and is electrically connected to the AI ​​algorithm model via a data cable / internal wiring / wireless communication module.

[0064] The IoT sensor group includes: an RFID reader embedded in the side or rear outer surface of the helmet shell, or fixedly mounted on a specific bracket outside the shell; and a vibration sensor fixedly mounted in the inner cavity (such as the top or side) or outer surface of the helmet shell.

[0065] The RFID reader and vibration sensor in the IoT sensor group are electrically connected to the AI ​​algorithm model via data cable / internal wiring / wireless communication module;

[0066] The power module is fixedly installed in the rear of the inner cavity of the helmet shell (such as below the liner) or the rear of the outer surface (such as next to the AI ​​algorithm model), and supplies power to the AI ​​algorithm model, multispectral camera and IoT sensor group through a power cable;

[0067] A data interface is located on the outer surface of the helmet shell (lower edge side), and is connected to the AI ​​algorithm model via an internal data cable for data export or charging (can be integrated with a power module).

[0068] The AI ​​algorithm model integrates machine vision recognition results, equipment operating parameters, and environmental data, and uses the dynamic emission factor method to calculate the carbon emissions of construction machinery in real time.

[0069] Specifically, the device identification sensor group includes:

[0070] Millimeter-wave radar is used to detect the three-dimensional outline of construction machinery in low-visibility environments.

[0071] Multispectral cameras are used to verify the type of machinery by analyzing the shape of the equipment, while simultaneously observing the shape and quantity of the tube segments.

[0072] The IoT sensor array, comprising RFID readers and vibration sensors, is used to identify the model and operating status (e.g., no-load / loaded / abnormal) of construction machinery. The RFID reader determines the type of machinery by reading the equipment tag.

[0073] Specifically, the AI ​​algorithm model includes: a segment type identification model and a mechanical carbon emission model;

[0074] The segment type identification model employs a multi-scale segment classification network to identify the size and thickness of the segments and determine their type.

[0075] The mechanical carbon emission model adopts a hybrid calculation model that integrates an equipment power database and real-time operating condition perception.

[0076] The segment type identification model is based on a multi-scale segment classification network to identify segment types; it identifies the size and thickness of the segments to determine the segment type, thereby distinguishing the type and quantity of shield tunnel segments;

[0077] A mechanical carbon emission model is used to integrate equipment power data with real-time operating condition perception for hybrid calculations.

[0078] Specifically, the smart safety helmet also includes an encryption and evidence storage module, which is used to encrypt carbon emission data and then upload it to the carbon emission analysis platform.

[0079] The encryption and evidence storage module is fixedly installed inside the helmet shell. Its core contains a dedicated encryption chip, which is electrically connected to the core processor of the AI ​​algorithm model via an internal data bus for data interaction. This encryption chip uses the national standard SM4 algorithm to uniformly encrypt the data to be transmitted from the AI ​​algorithm model, generating an encrypted data stream. The encrypted data stream is then packaged into data packets conforming to a specific transmission protocol (such as MQTT or HTTPS) by a data encapsulation function unit. The encapsulated encrypted data packets are then connected to the AI ​​algorithm model or encryption chip via an internal data cable through a communication interface module fixedly installed on the helmet shell, and finally transmitted to the cloud platform wirelessly. Simultaneously, the blockchain evidence storage function runs on the AI ​​algorithm model or cloud platform, responsible for receiving the data to be stored, combining it with a timestamp and device ID to form an evidence storage transaction, and sending it to a pre-defined blockchain network via the communication interface or cloud network. This blockchain network adopts a consortium blockchain architecture, with node deployment including cloud server nodes and at least one local regulatory agency or enterprise server node, thereby achieving traceability and tamper-proof evidence storage of carbon emission data.

[0080] In this embodiment, the smart safety helmet adopts an "edge-chain" collaborative architecture:

[0081] Multimodal perception layer: Multispectral cameras support the fusion of visible and near-infrared bands to enhance the ability to extract surface texture features of pipe segments; millimeter-wave radar constructs a three-dimensional spatial scanning system to identify the outline and movement trajectory of construction machinery through dynamic point cloud matching technology; vibration sensors analyze and invert the operating status of machinery through spectral features and dynamically correlate it with the equipment energy consumption model.

[0082] Edge intelligent computing layer: Embedded computing units realize real-time alignment and feature fusion of multi-source data, and support rapid carbon emission inference calculation; integrate dynamic emission factor library (basic layer: carbon emission factors of prefabricated components throughout their entire life cycle; operating condition layer: dynamic correlation parameters of equipment operating status and energy consumption).

[0083] In one specific embodiment of the present invention, a method for calculating carbon emissions in tunnel sections based on an AI-powered smart safety helmet includes:

[0084] S100. Import carbon emission model factors for different tunnel segments and carbon emission model factors for tunnel construction into the tunnel information system, analyze the construction stage division, material list and mechanical equipment configuration scheme, and generate carbon emission calculation benchmark parameters (segment emission factor library, construction emission factor library, mechanical emission factor library, etc.).

[0085] S200, Real-time perception and data collection of construction scene: The smart safety helmet is activated to perceive multimodal data of the construction scene, and the multimodal data is fused to dynamically calculate the instantaneous carbon emission intensity of the current construction surface;

[0086] Specifically, the machine vision module of the smart safety helmet is activated, and data of the construction scene is captured by a multispectral camera. Simultaneously, the equipment identification sensor group is triggered to determine the type of machinery and collect the operating data of the construction machinery. The safety helmet position is determined by the positioning module. The number of pipe segments is calculated, the workload of the equipment is calculated, and then the carbon emissions at the corresponding position are calculated.

[0087] The AI ​​algorithm model fuses the perceived multimodal data, matches the segment carbon emission factor library according to the corresponding segment type and quantity, matches the preset emission factor library according to the type of construction excavation machinery, and dynamically calculates the instantaneous carbon emissions of the current construction face by combining the real-time power of the equipment and the fuel consumption rate.

[0088] Specifically, workers wear smart safety helmets, activate them, and use multispectral cameras to capture the shape and quantity of tunnel segments within the tunnel section. A segment type identification model is then used to statistically determine the type and quantity of segments in the current section. Simultaneously, an IoT sensor array (RFID, millimeter-wave radar) is triggered to determine the type of machinery and collect its operating data (power, status). A positioning module determines the location (mileage) of the safety helmet, thereby identifying the corresponding carbon emission flow.

[0089] S300: The multimodal data and calculation results obtained from sensing are encrypted in two channels and transmitted to the time-series database and blockchain node of the carbon emission analysis platform, respectively.

[0090] Specifically, the AI ​​algorithm model performs multimodal data fusion, matches the segment carbon emission factor library and construction excavation carbon emission quota according to the corresponding segment type and quantity, determines the segment and excavation carbon emission amount, and dynamically calculates the instantaneous carbon emission amount of the current construction surface by matching the machinery type with the preset emission factor library and combining the equipment's real-time power and fuel consumption rate.

[0091] Specifically, the encryption module performs dual-channel encryption on the sensed data and the calculation results, and transmits the encrypted data to the time-series database and blockchain node of the cloud platform for storage.

[0092] The S400 carbon emission analysis platform receives data streams in real time and generates a three-dimensional carbon emission distribution model (bound to the spatial coordinates of the BIM model) through spatial interpolation algorithms, while distinguishing different types of carbon emissions.

[0093] S500: Regularly generate carbon emission audit reports during the construction phase, compare the deviation rate between design values ​​and measured values, and output process optimization suggestions to the project management terminal based on the benchmark parameters.

[0094] Specifically, the multi-dimensional statistical reporting module generates carbon emission audit reports for the construction phase on a regular basis (e.g., daily / weekly / construction phase), and outputs process optimization suggestions to the project management terminal based on historical data and comparative analysis.

[0095] Specifically, the smart safety helmet adopts an "end-edge-chain" collaborative architecture, and achieves accurate carbon emission sensing and calculation in construction scenarios through the following technologies:

[0096] Multimodal perception layer: Multispectral cameras support the fusion of visible and near-infrared bands, enhancing the ability to extract surface texture features of pipe segments, and combining with deep learning models to achieve high-precision pipe segment type identification; millimeter-wave radar constructs a three-dimensional spatial scanning system, and identifies the outline of operating machinery through dynamic point cloud matching technology; the vibration sensor signal processing module collects the raw vibration signal, and performs preprocessing (such as filtering and amplification) and fast Fourier transform (FFT) processing on the raw vibration signal to generate corresponding vibration spectrum data. Based on preset spectrum feature thresholds or pattern recognition rules, the received spectrum data is used for feature extraction and analysis, thereby determining whether the corresponding construction machinery is in an unloaded, loaded, or abnormal state; and it is dynamically associated with the equipment energy consumption model to obtain the energy consumption per unit time of the machinery through the mechanical energy consumption database.

[0097] Edge intelligent computing layer: Embedded computing units realize real-time alignment and feature fusion of multi-source data, and unify mechanical energy consumption data and segment carbon emission factors into carbon emission calculation, supporting millisecond-level carbon emission inference calculation;

[0098] The dynamic emission factor library integrates a multi-level parameter system: Basic layer: carbon emission factors throughout the entire life cycle of prefabricated components; Operating condition layer: dynamic correlation parameters between equipment operating status and energy consumption.

[0099] Trusted transmission layer: Dual-channel encryption mechanism implements differentiated security strategies for raw sensing data and calculation results; blockchain evidence storage module enables rapid on-chain data fingerprinting, ensuring the traceability and non-repudiation of carbon emission data;

[0100] The safety helmet shell has obtained industrial-grade protection certification, and its built-in emergency power supply can maintain core functions in case of emergencies. The system supports remote model updates and knowledge transfer, enabling intelligent adaptation across construction scenarios.

[0101] Specifically, the different types of carbon emissions include construction carbon emissions, segment carbon emissions, and mechanical equipment operation carbon emissions.

[0102] Specifically, edge intelligent computing (real-time carbon emission calculation)

[0103] AI algorithm models perform multimodal data fusion:

[0104] The shape and number of tunnel segments within the interval are captured by a multispectral camera. A segment type recognition model is used to statistically determine the current number and corresponding type of tunnel segments in the interval, and the emission coefficient for a single segment is calculated using the following formula:

[0105] Carbon emissions per segment = Number of segments × Carbon emission equivalent per prefabricated segment;

[0106] The carbon emission equivalent of a single precast segment is calculated from the list of materials, labor and equipment provided by the precast yard. Different types of segments have different carbon emission amounts.

[0107] Specifically, the calculation of carbon emissions during construction (excavation) involves matching the identified travel distance data (mileage) and tunnel cross-sectional information (width / area) with a construction carbon emission factor library to determine the carbon emission factor per unit volume.

[0108] Carbon emissions from construction machinery = tunnel cross-sectional area × location length × carbon emission factor per unit volume.

[0109] Specifically, when mechanical equipment and its corresponding type are identified, the carbon emission factor library for each mechanical type is matched, and the carbon emissions of the machinery are calculated as follows:

[0110] Mechanical carbon emissions = Σ (equipment power × operating time × fuel carbon emission coefficient).

[0111] Therefore, total carbon emissions = segment carbon emissions + construction carbon emissions + machinery carbon emissions;

[0112] The fuel carbon emission coefficient is dynamically adjusted based on real-time oil quality testing results.

[0113] Specifically, the three-dimensional carbon emission distribution model is bound to the spatial coordinates of the BIM model, supports a sliding time window analysis mode, and can trace back the spatial distribution characteristics of carbon emissions during any construction period.

[0114] For real-time visualization of dynamic carbon emissions in tunnel sections, the system constructs a carbon emission monitoring interface using 3D spatiotemporal mapping technology. This interface differentiates and visualizes different types of carbon emissions (construction, tunnel lining segments, machinery) in real time. The platform employs the following core technologies to achieve dynamic visualization:

[0115] A spatial thermal rendering engine is used to generate a 3D carbon emission intensity heat map of the tunnel (dynamic chromatographic mapping: green-yellow-red), reflecting the carbon emission equivalent per meter and the differences in different types of carbon emissions in real time. It supports layered display by carbon emission type and allows users to click on segments to view implicit carbon data from the prefabrication stage.

[0116] The spatiotemporal cube analysis module (which constructs a four-dimensional data cube of X / Y / Z + time using a sliding time window) supports cross-sectional comparison mode and time spectrum analysis mode.

[0117] Specifically, the spatial thermal rendering engine generates a 3D carbon emission intensity heat map of the tunnel by binding blockchain-certified environmental data with BIM model coordinates. The thermal gradient uses dynamic chromatographic mapping (green-yellow-red) to reflect the difference in carbon emission equivalent per meter in real time. The system supports layered display of carbon emission contribution by carbon emission type (segment / construction / machinery), and clicking on any segment allows users to view the implicit carbon emission data of its prefabrication stage.

[0118] Specifically, the spatiotemporal cube analysis module uses sliding time window technology to construct a four-dimensional carbon emission data cube (X / Y / Z axis spatial coordinates + time axis), supporting: cross-section comparison mode: synchronously displaying the carbon emission intensity differences of different tunnel cross-section groups at the same mileage; time spectrum analysis mode: presenting the carbon emission cycle characteristics of the tunnel through 3D graphs.

[0119] In one specific embodiment of the present invention, such as Figure 1 As shown, a tunnel section carbon emission calculation system based on an AI-powered smart helmet includes a tunnel information system, a smart helmet, a carbon emission analysis platform, and a multi-dimensional statistical reporting module.

[0120] The tunnel information system integrates tunnel engineering parameters (design drawings / construction stage division), equipment information (machinery model / power) and pipeline material data (segment type / specification) to build a carbon emission benchmark database;

[0121] The smart safety helmet incorporates a multispectral camera (front-mounted), an IoT sensor array (RFID reader / vibration sensor), a positioning module, an AI algorithm model, and an encryption and evidence storage module. Please refer to [link / reference]. Figure 2 ;

[0122] The carbon emission analysis platform receives data and generates a three-dimensional carbon emission distribution model and heat map;

[0123] The multi-dimensional statistical reporting module compiles historical data and outputs audit reports.

[0124] Specifically, such as Figure 3 As shown, the smart safety helmet adopts an "end-edge-chain" collaborative architecture:

[0125] 1. Multimodal sensing layer:

[0126] Multispectral cameras fuse visible light and near-infrared bands to enhance the ability to extract surface texture features of pipe segments;

[0127] Millimeter-wave radar constructs a three-dimensional spatial scanning system to identify the outline of construction machinery through dynamic point cloud matching;

[0128] Vibration sensors can invert the mechanical operating status (no load / load / abnormal) through spectrum analysis.

[0129] 2. Edge Intelligent Computing Layer:

[0130] The AI ​​algorithm model is equipped with a segment type recognition model (multi-scale classification network) and a mechanical carbon emission model;

[0131] Integrated dynamic emission factor library: basic layer (prefabricated component life cycle factors), operating condition layer (equipment status-energy consumption correlation parameters);

[0132] The carbon emission analysis platform generates a three-dimensional carbon emission distribution model that is bound to the coordinates of the BIM model using a spatial interpolation algorithm, such as... Figure 4 As shown, it supports:

[0133] (1) Three-dimensional model construction: Dynamically bind discrete carbon emission data points to the coordinate system of tunnel BIM design drawings to ensure that the data is accurately mapped to the corresponding tunnel segment location. Establish the real-time correlation between the spatial location of the tunnel segment and the carbon emission through the mileage coordinates (X / Y / Z) of the positioning module.

[0134] (2) Spatial thermal rendering engine: The carbon emission intensity per linear meter is displayed using dynamic color spectrum (green-yellow-red); it is classified into construction carbon emission layer (earthwork excavation), segment carbon emission layer (prefabricated implicit carbon), and mechanical equipment layer (tunnel boring machine operation carbon emission).

[0135] (3) Spatiotemporal cube analysis module: Construct a four-dimensional data cube (X / Y / Z + time axis) to realize cross-sectional comparison and time spectrum analysis.

[0136] Based on the above system, this invention also proposes a method for calculating carbon emissions in tunnel sections based on an AI-powered smart safety helmet, such as... Figure 5 As shown, it includes the following steps:

[0137] Step 1: Baseline parameter initialization

[0138] By importing the segment carbon emission factor library, construction emission factor library, and machinery emission factor library into the tunnel information system, the calculation benchmark parameters are generated by analyzing the construction stage division scheme.

[0139] Step 2: Real-time perception of the construction scene

[0140] Workers wearing smart safety helmets enter the tunnel section; multispectral cameras capture the shape and quantity of tunnel segments, and a segment type identification model counts the current segment type and quantity; millimeter-wave radar scans the three-dimensional outline of construction machinery, and RFID readers verify the machinery model; a positioning module determines the mileage position of the safety helmet; vibration sensors collect equipment operating status data (power / load rate).

[0141] Step 3: Edge Intelligent Computing (Core Computing Logic)

[0142] After the data is collected, the carbon emissions of the tunnel segments, construction, and mechanical equipment operation can be calculated using the built-in AI chip.

[0143] The calculation of segment carbon emissions involves: based on the identified segment types and quantities, matching the segment carbon emission factor library to determine the emission coefficient for a single segment. The segment carbon emissions are as follows:

[0144] Carbon emissions per segment = Number of segments × Carbon emission equivalent per prefabricated segment;

[0145] Construction (excavation) carbon emission calculation: Based on the identified travel distance data (mileage) and tunnel cross-section information (width / area), the carbon emission factor per unit volume is determined by matching the construction carbon emission factor library.

[0146] Construction carbon emissions = tunnel cross-sectional area × location length × construction carbon emission factor per unit volume;

[0147] Calculation of carbon emissions from mechanical equipment operation: Based on the identified mechanical equipment and its type, match it to the mechanical type carbon emission factor library:

[0148] Mechanical carbon emissions = Σ (equipment power × operating time × fuel carbon emission coefficient).

[0149] Among them, the fuel carbon emission coefficient is dynamically adjusted based on the real-time energy carbon emission factor results or operating conditions.

[0150] Calculate the instantaneous total carbon emission intensity of the current construction site:

[0151] Total carbon emissions = segment carbon emissions + construction carbon emissions + machinery carbon emissions.

[0152] Step 4: Trusted Data Encryption and Transmission

[0153] The system integrates carbon emission data from different parts, as well as data on pipe segments, construction, and machinery. The data and calculation results are encrypted in two channels through an encryption and evidence storage module and then transmitted synchronously to a cloud-based time-series database and a blockchain node for evidence storage.

[0154] Step 5: 3D Visualization and Dynamic Analysis

[0155] After obtaining encrypted data from the carbon emission analysis platform, a 3D model is constructed using machine vision data, positioning module data, and data from the built-in tunnel information database, including segment carbon emission calculations, construction carbon emissions, and machinery carbon emission data. The main process involves dynamically binding discrete carbon emission data points to the tunnel BIM design coordinate system to ensure accurate mapping of data to the corresponding segment locations. A real-time correlation between segment spatial location and carbon emissions is established using the mileage coordinates (X / Y / Z) from the positioning module. Subsequently, based on the segment carbon emission calculations, construction carbon emissions, and machinery carbon emission data, spatial thermal rendering is implemented, using a dynamic color spectrum (green-yellow-red) to display the carbon emission intensity per linear meter, with different colors representing different types of carbon emissions. The data is categorized into construction carbon emission layers (earthwork excavation), segment carbon emission layers (prefabricated implicit carbon), and machinery equipment layers (shield machine operation carbon emissions). Finally, based on the tunnel construction database component spatiotemporal cube analysis module, including the construction of a four-dimensional data cube (X / Y / Z + time axis), cross-sectional comparison and time-spectrum analysis are performed.

[0156] Step 6: Optimize Feedback

[0157] Finally, the data analyzed by the carbon emission platform is uploaded to the multi-dimensional statistical reporting module to generate an audit report, the main contents of which include:

[0158] - Compare the deviation rate between design values ​​and measured values;

[0159] - Compare the measured data with previously measured tunnel data to optimize the benchmark value for tunnel carbon emissions;

[0160] - Output process optimization suggestions to the project management terminal (e.g., "The carbon emissions of machinery in section XX exceed the standard, and it is recommended to adjust the equipment load rate").

[0161] The embodiments of the present invention have the following beneficial effects:

[0162] Precise and Real-Time: By integrating multimodal sensors and edge AI computing into the smart safety helmet, the system can directly and accurately identify pipe segments and equipment and calculate carbon emissions on the construction site in real time, solving the problems of lagging and low accuracy of traditional methods.

[0163] Full lifecycle coverage: The system covers the carbon footprint of tunnel construction throughout the entire lifecycle, from the carbon hidden in the precast segments to the dynamic carbon emissions during the construction process.

[0164] Dynamic visualization: Real-time, intuitive, and multi-dimensional visualization of carbon emissions is achieved through 3D heat maps, spatiotemporal cubes, etc., making it easy for managers to quickly grasp the distribution and trends of carbon emissions.

[0165] Trustworthy and reliable: Employing dual-channel encryption and blockchain storage technology, it ensures that carbon emission data is tamper-proof and traceable, meeting carbon verification requirements.

[0166] Closed-loop optimization: It provides historical comparison, horizontal benchmarking, deviation analysis and optimization suggestions, and constructs a closed-loop carbon management mechanism of "calculation-early warning-control" to effectively guide the optimization of green construction technology.

[0167] Wearable and convenient: Using the safety helmet worn by workers as a carrier, no additional complicated deployment is required, and it is easy to promote and use on construction sites.

[0168] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0169] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tunnel section carbon emission calculation system based on an AI intelligent safety helmet, characterized in that, The tunnel information system, the intelligent safety helmet, the carbon emission analysis platform, and the multi-dimensional statistical report module are included. The tunnel information system is used for integrating tunnel engineering parameters, equipment information, and pipeline material data, and constructing a construction period carbon emission benchmark database. The intelligent safety helmet is internally provided with an equipment identification sensor group, a machine vision module, a positioning module, a power module, and an AI algorithm model. The power module is used for providing electric energy. The equipment identification sensor group, the machine vision module, and the positioning module are used for collecting construction mechanical equipment information. The AI algorithm model is used for identifying shield segment types and quantities, determining safety helmet travel mileage data in a tunnel according to the construction mechanical equipment data information, and calculating carbon emissions based on the construction period carbon emission benchmark database. Wherein, a single segment emission coefficient is determined according to the identified segment quantity and corresponding type, and a segment carbon emission quantity formula is as follows: Segment carbon emission quantity = segment quantity × single segment prefabrication carbon emission equivalent; Wherein, the single segment prefabrication carbon emission equivalent is calculated by a carbon emission of a prefabrication yard given material machine list, and different segment types have different carbon emissions. According to the identified travel mileage data and the tunnel width, a construction carbon emission factor library is matched to determine a unit volume construction carbon emission factor, and a construction carbon emission quantity formula is as follows: Construction carbon emission quantity = tunnel cross-sectional area × positioning position length × unit volume construction carbon emission factor; When the mechanical equipment and its corresponding type are identified, a mechanical type carbon emission factor library is matched to calculate a mechanical equipment operation carbon emission quantity as follows: Mechanical equipment operation carbon emission quantity = Σ (device power × operation duration × fuel carbon emission coefficient); The total carbon emission quantity is calculated as follows: Total carbon emission quantity = segment carbon emission quantity + construction carbon emission quantity + mechanical equipment operation carbon emission quantity; The carbon emission analysis platform is used for displaying carbon emissions of a tunnel interval in real time. The multi-dimensional statistical report module realizes longitudinal construction stage comparison and horizontal similar tunnel carbon emission quantity benchmarking based on statistical historical period carbon emission data. The equipment identification sensor group includes:

2. The tunnel section carbon emission calculation system based on AI intelligent safety helmet according to claim 1, wherein A millimeter wave radar for detecting a three-dimensional contour of an operating mechanical equipment; A multi-spectrum camera for verifying a mechanical type by analyzing equipment shape while observing segment shape and quantity; An RFID reader for identifying a construction mechanical equipment type; A vibration sensor for identifying a running state of the construction mechanical equipment. The AI algorithm model includes a segment type identification model and a mechanical carbon emission model. 3.The tunnel section carbon emission calculation system based on AI intelligent safety helmet according to claim 1, wherein The segment type identification model adopts a multi-scale segment classification network. The mechanical carbon emission model adopts a hybrid calculation model integrating a device power database and real-time working condition perception. The intelligent safety helmet further includes an encryption and storage module, which is used for uploading the carbon emission quantity data to the carbon emission analysis platform after encryption.

4. The tunnel section carbon emission calculation system based on AI intelligent safety helmet according to claim 1, wherein The carbon emission model of different segments and the tunnel construction carbon emission model are imported, the construction stage division, the material list, and the mechanical equipment configuration scheme are analyzed, and carbon emission calculation benchmark parameters are generated.

5. A tunnel section carbon emission calculation method based on an AI intelligent safety helmet, applied to the tunnel section carbon emission calculation system based on the AI intelligent safety helmet according to any one of claims 1-4, characterized in that, ​ ​ Start the machine vision module of the intelligent safety helmet, capture the data of the construction scene through the multispectral camera, synchronously trigger the equipment identification sensor group to determine the type of machinery and collect the operation data of the construction machinery, and determine the position of the safety helmet through the positioning module; The AI algorithm model fuses the multi-modal data perceived, matches the pipe piece carbon emission factor library according to the corresponding pipe piece type and quantity, matches the preset emission factor library according to the type of construction excavation machinery, combines the real-time power and fuel consumption rate of the equipment, and dynamically calculates the instantaneous carbon emission of the current construction surface; The calculation results are double-channel encrypted and transmitted to the time sequence database and the blockchain node of the carbon emission analysis platform respectively; The carbon emission analysis platform receives the data stream in real time, generates a three-dimensional carbon emission distribution model through a spatial interpolation algorithm, and distinguishes different types of carbon emissions at the same time; The multi-dimensional statistical report module generates a carbon emission audit report for the construction phase at regular intervals, compares the deviation rate of the design value and the measured value, and outputs process optimization suggestions to the project management terminal based on the difference.

6. The tunnel section carbon emission calculation method based on the AI intelligent safety helmet according to claim 5, characterized in that, The multispectral camera captures the shape and quantity of the pipe pieces in the interval, and the pipe piece type identification model counts the number of pipe pieces in the current interval and the corresponding types.

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