Low-carbon building construction system

Abstract

The invention discloses a building low-carbon construction system which comprises the components of S1, a carbon emission data acquisition module which acquires and processes carbon emission data in a whole life cycle of a building; s2, a block chain data management module generates a non-tampering and traceable distributed carbon emission record; s3, based on a predefined carbon emission optimization strategy, an intelligent contract execution module automatically executes carbon emission monitoring and optimization operation of each stage of the whole life cycle of the building through the intelligent contract of the block chain; s4, a dynamic analysis module which dynamically identifies the carbon emission hot spot intensity in the whole life cycle of the building; s5, an optimization suggestion generation module which generates a targeted low-carbon optimization suggestion based on the identification result of the carbon emission hot spot intensity; and S6, a visual display module which displays the multi-dimensional carbon emission data, the carbon emission hot spot intensity and the low-carbon optimization suggestion in a multi-dimensional chart form. The method has the advantages of high data transparency, strong optimization response real-time performance and high carbon emission management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon construction of buildings, and particularly to a low-carbon construction system for buildings. Background Art

[0002] With the rapid development of the concepts of green buildings and low-carbon economy, carbon emission management throughout the building life cycle has gradually become the focus of global attention. Especially in each stage of building construction, operation, and demolition, a large amount of energy consumption and carbon emissions have had a significant impact on the environment. Carbon emission management throughout the building life cycle is not only a key link in achieving low-carbon construction but also an important measure for the world to respond to climate change. However, in the existing technology, there are still many deficiencies in the carbon emission management methods throughout the building life cycle at the technical level, making it difficult to meet the needs of modern low-carbon building construction.

[0003] The existing building carbon emission management methods usually rely on a single data collection method and a static management mechanism. These methods have the following significant defects when facing complex building life cycle management:

[0004] 1. The carbon emission data management is not transparent and is easily tampered with: The traditional carbon emission data recording methods mostly rely on a centralized storage structure, lacking data transparency and traceability, and are easily tampered with or lost due to human factors.

[0005] 2. Lack of dynamic optimization ability: The existing technology usually only monitors the carbon emissions in a certain stage and fails to perform real-time optimization by combining the dynamic data throughout the building life cycle, making it difficult to effectively identify and quickly respond to carbon emission hotspots.

[0006] 3. Low automation level: The existing methods rely on manual intervention in the execution of optimization strategies and lack an automated mechanism based on technologies such as smart contracts, resulting in low execution efficiency and prone to human errors.

[0007] 4. Insufficient real-time monitoring ability: The existing technology lags behind in the real-time collection and processing of carbon emission data, especially in the building operation and demolition stages, and it is impossible to obtain the data of key carbon emission nodes in a timely manner, making it difficult to achieve dynamic optimization management.

[0008] 5. Lack of visualization analysis ability: The traditional building carbon emission management methods lack an intuitive display of complex data, making it difficult to provide a comprehensive analysis perspective and effective decision-making support for building managers.

[0009] Therefore, how to provide a low-carbon construction system for buildings is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0010] An object of the present invention is to propose a building low-carbon construction system. The present invention fully combines Internet of Things, blockchain and smart contract technologies, and details methods for realizing dynamic monitoring, automated optimization and visual analysis of carbon emission data throughout the building life cycle, having the advantages of high data transparency, strong optimization response real-time performance and high carbon emission management efficiency.

[0011] A building low-carbon construction system according to an embodiment of the present invention includes:

[0012] S1. A carbon emission data collection module, configured to collect and process carbon emission data throughout the building life cycle through Internet of Things sensing devices, and generate multi-dimensional carbon emission data;

[0013] S2. A blockchain data management module, connected to the carbon emission data collection module, configured to encrypt the collected multi-dimensional carbon emission data and store it in the blockchain, generating an immutable and traceable distributed carbon emission record;

[0014] S3. A smart contract execution module, connected to the blockchain data management module, based on predefined carbon emission optimization strategies, automatically executes carbon emission monitoring and optimization operations at each stage of the building life cycle through the smart contract of the blockchain;

[0015] S4. A dynamic analysis module, connected to the blockchain data management module and the smart contract execution module, based on historical and real-time data in the distributed carbon emission record, dynamically identifies the carbon emission hot spot intensity throughout the building life cycle;

[0016] S5. An optimization suggestion generation module, generating targeted low-carbon optimization suggestions based on the identification result of the carbon emission hot spot intensity;

[0017] S6. A visual display module, connected to the optimization suggestion generation module, displaying the multi-dimensional carbon emission data, the carbon emission hot spot intensity and the low-carbon optimization suggestions in the form of multi-dimensional charts.

[0018] Optionally, the S2 specifically includes:

[0019] S21. Internet of Things sensing devices, collecting carbon emission data throughout the building life cycle, including energy consumption and carbon emission data D m in the building material production stage, fuel consumption and carbon emission data D t in the transportation process, energy consumption and equipment carbon emission data D c in the construction process, energy consumption and carbon emission data D r in the building operation stage, and waste treatment carbon emission data D d in the demolition stage;

[0020] A data processing unit, connected to the Internet of Things sensing devices, preprocesses the collected carbon emission data, including denoising, format conversion, and normalization processing;

[0021] A timestamp generation unit, connected to the data processing unit, generates a unique timestamp T for each collected carbon emission data record k and generates the processed multi-dimensional carbon emission data D multi :

[0022] D multi ={(D m ,T m ),(D t ,T t ),(D c ,T c ),(D o ,T o ),(D d ,T d )}

[0023] where D multi represents the processed multi-dimensional carbon emission data, D x is the carbon emission data at each stage, and T x is the timestamp of the carbon emission data corresponding to the stage of D x ;

[0024] S22. A data encryption unit, connected to the carbon emission data collection module, encrypts the processed multi-dimensional carbon emission data D multi using a composite asymmetric encryption method:

[0025]

[0026] where C multi represents the encrypted multi-dimensional carbon emission data, E pub1 (·) and E pub2 (·) respectively represent encryption functions using different public keys, H(D multi ) represents the hash value generated after hashing the multi-dimensional carbon emission data D multi , K session is the symmetric encryption session key, encrypted by E pub2 , represents the bitwise exclusive OR operation, and || represents the concatenation operation of the encryption results;

[0027] S23. A data storage unit, connected to the data encryption unit, is used to store the encrypted multi-dimensional carbon emission data C multi in the blockchain in the form of blocks, generating distributed carbon emission records. Each block contains the following set of data fields:

[0028] Block = {C multi , H prev , T block}

[0029] Among them, Block represents the set of data fields of a block. C multi represents the encrypted multi - dimensional carbon emission data, H prev represents the hash value of the previous block, and T block represents the timestamp generated by the current block;

[0030] S24. The data verification unit, connected to the data storage unit, is used to verify the integrity of the block before adding a new block to the blockchain:

[0031]

[0032] Among them, Hash block is the hash value of the current block, H(·) represents the calculation of the hash function, and H prev represents the hash value of the previous block, is the encrypted multi - dimensional carbon emission data and its encryption session key, and T block represents the generation timestamp of the current block;

[0033] S25. The data access control unit, connected to the data storage unit, provides authorized users with access rights to the multi - dimensional carbon emission data stored in the blockchain according to the permission control policy.

[0034] Optionally, the S3 specifically includes:

[0035] S31. The carbon emission optimization strategy definition unit, which pre - defines the carbon emission optimization strategies {P1, P2,..., P n} according to the carbon emission monitoring requirements during the whole life cycle of the building. Each strategy includes a target stage, an optimization goal, and an execution condition. Among them, the optimization goal includes the carbon emission threshold T r and the energy utilization rate improvement parameter E r ;

[0036] S32. The smart contract generation unit, connected to the carbon emission optimization strategy definition unit, converts each carbon emission optimization strategy P i into a smart contract SC i on the blockchain, and attaches a unique identifier ID SC and a trigger condition C trigger to each smart contract:

[0037]

[0038] Among them, C triggerRepresents the calculation result of the trigger condition, D x Represents the carbon emission data of the x-th stage, w x Represents the weight parameter of the x-th stage, which is used to reflect the relative importance of this stage to the total carbon emissions. It is a positive real number. m represents the total number of stages in the whole life cycle of the building. Tr represents the predefined carbon emission threshold in the carbon emission optimization strategy. If C trigger ≤0, then trigger the execution of the smart contract;

[0039] S33. The smart contract deployment unit is connected to the smart contract generation unit, and deploys the generated smart contract SC i to the blockchain and associates the smart contract with the blockchain data management module;

[0040] S34. The smart contract execution unit is connected to the blockchain data management module and the smart contract deployment unit. It performs real-time detection on the carbon emission data of the current stage according to the trigger condition C trigger When the trigger condition is met, it calls the corresponding smart contract SC i to execute:

[0041]

[0042] Among them, D opt represents the optimized carbon emission data, α x represents the optimization adjustment coefficient of the x-th stage, which is used to reflect the adjustment weight of the carbon emissions in this stage. D x represents the carbon emission data of the x-th stage. m represents the total number of stages in the whole life cycle of the building. E r represents the energy utilization rate improvement parameter, which is a non-negative real number;

[0043] S35. The execution result on-chain unit is connected to the smart contract execution unit, and records the hash value H(R SC ) of the execution result corresponding to the smart contract to the blockchain, and updates the carbon emission record block of the current stage. The hash value of the new block is expressed as: SC ) to the blockchain and updates the carbon emission record block of the current stage. The hash value of the new block is expressed as:

[0044] H(Block′) = H prev + H(R SC ) + T block ′

[0045] Among them, H(Block′) represents the hash value of the new block, H prev represents the hash value of the previous block, H(R SC ) represents the hash value of the execution result corresponding to the smart contract, T block ′ represents the timestamp when the current new block is generated.

[0046] Optionally, S31 specifically includes:

[0047] S311. A target stage definition unit, which divides the building life cycle into multiple target stages according to the carbon emission data during the whole building life cycle, including the material production stage, transportation stage, construction stage, operation stage, and demolition stage, and generates a carbon emission stage matrix for each stage:

[0048] M x =[E x ,P x ,R x

[0049] where M x represents the carbon emission stage matrix of the x-th stage, E x represents the total energy consumption within the stage, P x represents the proportion vector of energy types, and R x represents the stage resource recovery rate;

[0050] S312. An optimization goal setting unit, which is connected to the target stage definition unit and sets optimization goals for each target stage. The optimization goals include a carbon emission threshold Tr:

[0051]

[0052] where and respectively represent the baseline energy consumption and target energy consumption of the x-th stage, and represent the baseline proportion and target proportion of energy types, represents the target resource recovery rate of the x-th stage, and m is the total number of stages within the whole building life cycle;

[0053] S313. An energy utilization rate improvement parameter calculation unit, which is connected to the optimization goal setting unit and calculates an energy utilization rate improvement parameter E r :

[0054]

[0055] where η x represents the energy utilization efficiency improvement factor of the x-th stage, which is a non-negative real number greater than 0;

[0056] S314. An execution condition definition unit, which is connected to the optimization goal setting unit and the energy utilization rate improvement parameter calculation unit, and defines execution conditions according to the optimization goals and the energy utilization rate improvement parameters:

[0057] C exec =(T r ≤T threshold )∧(E​r ≥E min )

[0058] Among them, C exec represents the execution condition, T threshold is the predefined carbon emission threshold limit value, and E min is the minimum energy utilization rate improvement parameter;

[0059] S315, the strategy combination unit, is connected to the target stage definition unit, the optimization target setting unit, and the execution condition definition unit, and is used to generate the carbon emission optimization strategy {P1, P2,..., P n}}, and each strategy is:

[0060] P i =[M x , T r , E r , C exec

[0061] Among them, Pi represents the i-th carbon emission optimization strategy, M x is the carbon emission stage matrix, Tr is the carbon emission threshold of the carbon emission reduction target, E r is the energy utilization rate improvement parameter, and C exec is the corresponding execution condition.

[0062] Optionally, the S4 specifically includes:

[0063] S41, the carbon emission data preprocessing unit, is connected to the blockchain data management module, extracts the historical carbon emission data D history and the real-time carbon emission data D real-time , and normalizes the extracted historical carbon emission data and real-time carbon emission data to generate the normalized carbon emission data D x ' of the x-th stage;

[0064] S42, the carbon emission time series analysis unit, is connected to the carbon emission data preprocessing unit, constructs a time series dynamic model based on the historical carbon emission data D history and the real-time carbon emission data D real-time , and the carbon emission trend model T x (t) is expressed as:

[0065]

[0066] Among them, T x (t) represents the carbon emission dynamic trend at time t in the x-th stage, D x '(t i ) represents the normalized carbon emission data of the x-th stage at time t i , and γ x ​is the disturbance coefficient for the x-th stage, ω x represents the frequency of periodic change in the x-th stage, φ x represents the initial phase of the x-th stage, β x represents the time change adjustment coefficient for the x-th stage, t j represents the time point of the past moment, ∈ is a small positive number introduced to avoid a zero denominator, k is the total number of data points within the time window, and N is the total number of sampling points;

[0067] S43. The carbon emission hot spot identification unit, connected to the carbon emission time series analysis unit, calculates the carbon emission hot spot intensity H based on the carbon emission dynamic trend Tx(t) x :

[0068]

[0069] where H x represents the carbon emission hot spot intensity for the x-th stage, w x represents the weight parameter for the x-th stage, α is a positive definite smoothing parameter, is the sum of the carbon emission trends of all stages at time t, [t1, t2] is the analysis time interval, and m represents the total number of stages within the building's whole life cycle;

[0070] S44. The hot spot ranking unit, connected to the carbon emission hot spot identification unit, ranks each stage according to the calculated carbon emission hot spot intensity H x to generate a carbon emission hot spot priority list {H1, H2,..., H m}, where m represents the total number of stages within the building's whole life cycle.

[0071] Optionally, the S5 specifically includes:

[0072] S51. The carbon emission reduction target definition unit, connected to the hot spot ranking unit, sets the carbon emission reduction target T within the whole life cycle and allocates the carbon emission reduction targets {T m} for each stage according to the carbon emission hot spot priority list {H1, H2,..., H total} and the initial total carbon emission T of the building's whole life cycle goal : r1 T r2 ,..., T rm}:

[0073]

[0074] T goal = T total ·R target

[0075] where Represents the carbon emission reduction target for the x-th stage, H x Is the carbon emission hot spot intensity for the x-th stage, T goal Is the total carbon emission reduction target over the entire life cycle, T total Is the total initial carbon emissions over the building's entire life cycle, R target Is the carbon emission reduction ratio, with a value range of 0 to 1, and m is the total number of stages in the building's entire life cycle;

[0076] S52, Time allocation unit, connected to the carbon emission reduction target definition unit, and sets the time allocation matrix T for optimized execution according to the carbon emission reduction target {T r1 , T r2 ,..., T rm} and the construction and operation plans for each stage: matrix :

[0077]

[0078] Among them, T matrix Represents the time allocation matrix for optimized execution, and t ij Represents the optimized time allocated to the j-th stage in the i-th stage;

[0079] S53, Adjustment parameter generation unit, connected to the time allocation unit, and calculates the set of dynamic adjustment parameters {α1, α2,..., α matrix} for each stage according to the time allocation matrix T r1 and the carbon emission reduction target {T r2 , T rm ,..., T m}:

[0080]

[0081] Among them, α x Represents the dynamic adjustment parameter for the x-th stage, Is the carbon emission reduction target for the x-th stage, t x Is the actual optimized time for the x-th stage, and η x Is the energy utilization efficiency for the x-th stage;

[0082] S54, Recommendation generation unit, connected to the adjustment parameter generation unit, and generates low-carbon optimization recommendations {G1, G2,..., G r1 , T r2 ,..., T rm}, the time allocation matrix T matrix and the set of dynamic adjustment parameters {α1, α2,..., α m}: m}, where each suggestion includes a target stage, an optimization time, and adjustment parameters.

[0083] The beneficial effects of the present invention are as follows:

[0084] (1) By combining the Internet of Things technology and the blockchain technology, the present invention realizes the transparent and tamper-proof management of carbon emission data throughout the building life cycle. The integrity and traceability of the data are ensured by using the blockchain distributed storage technology, thus effectively solving the problems of data loss and tampering in traditional carbon emission management.

[0085] (2) Through the smart contract technology, the present invention realizes the automatic execution of the carbon emission optimization strategies at each stage of the building life cycle, greatly improving the efficiency and accuracy of the execution of the optimization strategies and reducing the delays and errors caused by human intervention.

[0086] (3) Through the dynamic analysis module, by combining historical and real-time data, the present invention can identify the carbon emission hotspots in real time throughout the building life cycle, provide dynamic optimization support, quickly respond to and handle the high-emission stages, and ensure the realization of the building's low-carbon construction goal.

[0087] (4) Through the visualization display module, the present invention displays multi-dimensional carbon emission data, hotspot analysis results, and optimization suggestions in an intuitive chart form, providing a comprehensive carbon emission analysis perspective and decision-making support for building managers, and improving the management efficiency and scientificity.

[0088] (5) Through the optimization suggestion generation module, by combining the hotspot priority list, the time allocation matrix, and the adjustment parameter set, the present invention generates a targeted optimization plan, providing intelligent and dynamic support for the low-carbon construction of the building life cycle, and significantly improving the overall efficiency of carbon emission management and the low-carbon level. Description of the Drawings

[0089] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0090] Figure 1 is the overall framework diagram of a building low-carbon construction system proposed by the present invention. Detailed Embodiments

[0091] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic way, so they only show the components related to the present invention.

[0092] Refer to Figure 1 , a building low-carbon construction system, comprising:

[0093] S1. Carbon emission data collection module, which is used to collect and process carbon emission data during the whole life cycle of a building through Internet of Things sensing devices, and generate multi-dimensional carbon emission data;

[0094] In this embodiment, the carbon emission data during the whole life cycle of a building is collected through Internet of Things sensing devices, including multi-dimensional data in the stages of material production, transportation, construction, operation and demolition, realizing comprehensive monitoring of key carbon emission nodes. It not only covers the carbon emission characteristics in different stages, but also collects and integrates carbon emission data in real time, significantly improving the integrity and timeliness of the data, providing a reliable data basis for the optimization of carbon emissions in the whole life cycle of a building, solving the problems of incomplete and non-real-time collection of carbon emission data in traditional methods, and laying a solid technical foundation for achieving the goal of low-carbon construction.

[0095] S2. Blockchain data management module, which is connected to the carbon emission data collection module, and is used to encrypt the collected multi-dimensional carbon emission data and store it in the blockchain, generating an immutable and traceable distributed carbon emission record;

[0096] Optionally, the S2 specifically includes:

[0097] S21. Internet of Things sensing devices, which collect carbon emission data during the whole life cycle of a building, including energy consumption and carbon emission data D m during the building material production stage, fuel consumption and carbon emission data D t during the transportation process, energy consumption and equipment carbon emission data D c during the construction process, energy consumption and carbon emission data D o during the building operation stage, as well as waste treatment carbon emission data D d during the demolition stage;

[0098] Data processing unit, which is connected to the Internet of Things sensing devices and preprocesses the collected carbon emission data, including denoising, format conversion and normalization processing;

[0099] Timestamp generation unit, which is connected to the data processing unit, generates a unique timestamp T k for each collected carbon emission data record, and generates the processed multi-dimensional carbon emission data D multi :

[0100] D multi ={(D m , T m ), (D t , T t ), (D c , T c ), (D o , T o ), (Dd , T d )}

[0101] Among them, D multi represents the processed multi-dimensional carbon emission data, and D x is the carbon emission data for each stage, and T x is the timestamp of the carbon emission data corresponding to the stage of D x ;

[0102] S22. The data encryption unit is connected to the carbon emission data collection module and encrypts the processed multi-dimensional carbon emission data D multi using a composite asymmetric encryption method:

[0103]

[0104] Among them, C multi represents the encrypted multi-dimensional carbon emission data, E pub1 (·) and E pub2 (·) respectively represent encryption functions using different public keys, and H(D multi ) represents the hash value generated after performing a hash process on the multi-dimensional carbon emission data D multi , K session is the symmetric encryption session key, which is encrypted by E pub2 , represents the bitwise exclusive OR operation, and || represents the concatenation operation of the encryption result;

[0105] S23. The data storage unit is connected to the data encryption unit and is used to store the encrypted multi-dimensional carbon emission data C multi in the blockchain in the form of blocks, generating a distributed carbon emission record. Each block contains the following data field set:

[0106] Block = {C multi , H prev , T block}

[0107] Among them, Block represents the data field set of the block, C multi represents the encrypted multi-dimensional carbon emission data, H prev represents the hash value of the previous block, and T block represents the timestamp when the current block is generated;

[0108] S24. The data verification unit is connected to the data storage unit and is used to verify the integrity of the block before adding a new block to the blockchain:

[0109]

[0110] Among them, Hashblock is the hash value of the current block, where H(·) represents the calculation of the hash function, and H prev represents the hash value of the previous block, is the encrypted multi-dimensional carbon emission data and its encrypted session key, T block represents the generation timestamp of the current block;

[0111] S25, a data access control unit, is connected to the data storage unit and provides authorized users with access rights to the multi-dimensional carbon emission data stored in the blockchain according to the permission control policy.

[0112] This embodiment provides comprehensive management of carbon emission data throughout the building life cycle by combining blockchain technology and carbon emission data management, not only realizing data transparency and immutability, but also ensuring data integrity and security through distributed storage.

[0113] S3, a smart contract execution module, is connected to the blockchain data management module and automatically executes carbon emission monitoring and optimization operations for each stage of the building life cycle through the smart contract of the blockchain based on the predefined carbon emission optimization strategy;

[0114] Optionally, the S3 specifically includes:

[0115] S3 1, a carbon emission optimization strategy definition unit, predefines carbon emission optimization strategies {P1, P2,..., P n} according to the carbon emission monitoring requirements during the building life cycle. Each strategy includes a target stage, an optimization target, and execution conditions, where the optimization target includes a carbon emission threshold T r and an energy utilization rate improvement parameter E r ;

[0116] S32, a smart contract generation unit, is connected to the carbon emission optimization strategy definition unit, converts each carbon emission optimization strategy Pi into a smart contract SC i on the blockchain, and attaches a unique identifier ID SC and a trigger condition C trigger to each smart contract:

[0117]

[0118] Among them, C trigger represents the calculation result of the trigger condition, D x represents the carbon emission data of the x-th stage, w x represents the weight parameter of the x-th stage, which is used to reflect the relative importance of this stage to the total carbon emission, is a positive real number, m represents the total number of stages in the building life cycle, Tr represents the predefined carbon emission threshold in the carbon emission optimization strategy, if C trigger≤0, then trigger the execution of the smart contract;

[0119] S33. A smart contract deployment unit, connected to the smart contract generation unit, deploys the generated smart contract SC i to the blockchain and associates the smart contract with the blockchain data management module;

[0120] S34. A smart contract execution unit, connected to the blockchain data management module and the smart contract deployment unit, performs real-time detection on the carbon emission data of the current stage. When the trigger condition is met, it calls the corresponding smart contract SC trigger to execute: i Execute:

[0121]

[0122] where D opt represents the optimized carbon emission data, α x represents the optimization adjustment coefficient of the x-th stage, used to reflect the adjustment weight of the carbon emissions in this stage, D x represents the carbon emission data of the x-th stage, m represents the total number of stages in the building's full life cycle, and E r represents the energy utilization rate improvement parameter, which is a non-negative real number;

[0123] S35. An execution result on-chain unit, connected to the smart contract execution unit, records the hash value H(R SC ) of the execution result R corresponding to the smart contract on the blockchain and updates the carbon emission record block of the current stage. The hash value of the new block is expressed as: SC ) to the blockchain and updates the carbon emission record block of the current stage. The hash value of the new block is expressed as:

[0124] H(Block′) = H prev + H(R SC ) + T block ′

[0125] where H(Block′) represents the hash value of the new block, H prev represents the hash value of the previous block, H(R SC ) represents the hash value of the execution result corresponding to the smart contract, and T block ′ represents the timestamp when the current new block is generated.

[0126] The specific content of the said S31 includes:

[0127] S311. A target stage definition unit, based on the carbon emission data within the building's full life cycle, divides the building life cycle into multiple target stages, including the material production stage, transportation stage, construction stage, operation stage, and demolition stage, and generates a carbon emission stage matrix for each stage:

[0128] Mx = [E x , P x , R x

[0129] Among them, M x represents the carbon emission phase matrix in the x-th phase, E x represents the total energy consumption within the phase, P x represents the proportion vector of energy types, R x represents the phase resource recovery rate;

[0130] S312. The optimization target setting unit is connected to the target phase definition unit, and sets the optimization target for each target phase. The optimization target includes the carbon emission threshold T r :

[0131]

[0132] Among them, and respectively represent the baseline energy consumption and the target energy consumption in the x-th phase, and represent the baseline proportion and the target proportion of energy types, represents the target resource recovery rate in the x-th phase, and m is the total number of phases in the building's whole life cycle;

[0133] S313. The energy utilization rate improvement parameter calculation unit is connected to the optimization target setting unit and calculates the energy utilization rate improvement parameter E r :

[0134]

[0135] Among them, η x represents the energy utilization efficiency improvement factor in the x-th phase, which is a non-negative real number greater than 0;

[0136] S314. The execution condition definition unit is connected to the optimization target setting unit and the energy utilization rate improvement parameter calculation unit, and defines the execution condition according to the optimization target and the energy utilization rate improvement parameter:

[0137] C exec = (T r ≤ T threshold ) ∧ (E r ≥ E min )

[0138] Among them, C exec represents the execution condition, T threshold is the predefined carbon emission threshold limit, and E min is the minimum energy utilization rate improvement parameter;

[0139] ​S315. The strategy combination unit is connected to the target stage definition unit, the optimization goal setting unit, and the execution condition definition unit, and is used to generate carbon emission optimization strategies {P1, P2,..., P n} where each strategy is:

[0140] P i = [M x , T r , E r , C exec

[0141] where Pi represents the i-th carbon emission optimization strategy, M x is the carbon emission stage matrix, Tr is the carbon emission threshold for the carbon emission reduction target, E r is the energy utilization rate improvement parameter, and C exec is the corresponding execution condition.

[0142] In this embodiment, by introducing smart contract technology and combining the distributed storage and automated execution capabilities of the blockchain, the automated execution of carbon emission optimization strategies throughout the building life cycle is realized. The smart contract not only automatically monitors and optimizes the carbon emissions at each stage according to the preset carbon emission optimization strategies, but also can ensure the transparency and immutability of the strategy execution through the blockchain, thereby reducing the delays and deviations caused by manual intervention and improving the accuracy and execution efficiency of the optimization.

[0143] S4. The dynamic analysis module is connected to the blockchain data management module and the smart contract execution module, and dynamically identifies the carbon emission hot spot intensity during the building life cycle based on the historical and real-time data in the distributed carbon emission records;

[0144] Optionally, the S4 specifically includes:

[0145] S41. The carbon emission data preprocessing unit is connected to the blockchain data management module, extracts the historical carbon emission data D history and the real-time carbon emission data D real-time from the distributed carbon emission records, and normalizes the extracted historical and real-time carbon emission data to generate the normalized carbon emission data D x ' at the x-th stage;

[0146] S42. The carbon emission time series analysis unit is connected to the carbon emission data preprocessing unit, constructs a time series dynamic model based on the historical carbon emission data D history and the real-time carbon emission data D real-time , and the carbon emission trend model T x (t) is expressed as:

[0147] ​

[0148] Among them, T x (t) represents the dynamic trend of carbon emissions at time t in the x-th stage, D x ′(t i ) represents the normalized carbon emission data in the x-th stage at time t i , γ x is the disturbance coefficient in the x-th stage, ω x represents the frequency of periodic change in the x-th stage, φ x represents the initial phase in the x-th stage, β x represents the time change adjustment coefficient in the x-th stage, t j represents the time point of the past moment, ∈ is a small positive number introduced to avoid the denominator being zero, k is the total number of data points within the time window, and N is the total number of sampling points;

[0149] S43. The carbon emission hot spot identification unit is connected to the carbon emission time series analysis unit, and calculates the carbon emission hot spot intensity H based on the carbon emission dynamic trend Tx(t) x :

[0150]

[0151] Among them, H x represents the carbon emission hot spot intensity in the x-th stage, w x represents the weight parameter in the x-th stage, α is a positive definite smoothing parameter, is the sum of the carbon emission trends of all stages at time t, [t1, t2] is the analysis time interval, and m represents the total number of stages in the building's whole life cycle;

[0152] S44. The hot spot ranking unit is connected to the carbon emission hot spot identification unit, and ranks each stage according to the calculated carbon emission hot spot intensity H x to generate a carbon emission hot spot priority list {H1, H2,..., H m}, where m represents the total number of stages in the building's whole life cycle.

[0153] In this embodiment, through the dynamic analysis module, combining the historical data and real-time data in the distributed carbon emission records, the carbon emission hot spots in the whole life cycle of the building are dynamically identified, not only analyzing the carbon emission characteristics of the current stage, but also comprehensively analyzing the change law of carbon emissions in combination with the time series trend.

[0154] S5. The optimization suggestion generation module generates targeted low-carbon optimization suggestions based on the identification results of the carbon emission hot spot intensity;

[0155] Optionally, the S5 specifically includes:

[0156] S51. Carbon emission reduction target definition unit, connected to the hot spot sorting unit, according to the carbon emission hot spot priority list {H1, H2,..., H m}, and the initial total carbon emission T during the whole life cycle of the building total , set the carbon emission reduction target T during the whole life cycle goal , and allocate the carbon emission reduction targets for each stage {T r1 , T r2 ,..., T rm}:

[0157]

[0158] T goal = T total ·R target

[0159] Among them, represents the carbon emission reduction target in the x-th stage, H x is the carbon emission hot spot intensity in the x-th stage, T goal is the total carbon emission reduction target during the whole life cycle, T total is the initial total carbon emission during the whole life cycle of the building, R target is the carbon emission reduction ratio, with a value range of 0 to 1, and m is the total number of stages in the whole life cycle of the building;

[0160] S52. Time allocation unit, connected to the carbon emission reduction target definition unit, according to the carbon emission reduction targets {T r1 , T r2 ,..., T rm} and the construction and operation plans for each stage, set the time allocation matrix T matrix for optimized execution:

[0161]

[0162] Among them, T matrix represents the time allocation matrix for optimized execution, and t ij represents the optimized time allocated to the j-th stage in the i-th stage;

[0163] S53. Adjustment parameter generation unit, connected to the time allocation unit, according to the time allocation matrix T matrix and the carbon emission reduction targets {T r1 , T r2 ,..., T rm}, calculate the set of dynamic adjustment parameters {α1, α2,..., α m} for each stage:

[0164]

[0165] Among them, α x represents the dynamic adjustment parameter in the x-th stage, is the carbon emission reduction target in the x-th stage, t x is the actual optimization time in the x-th stage, η x is the energy utilization efficiency in the x-th stage;

[0166] S54. The recommendation generation unit is connected to the adjustment parameter generation unit, and combines the carbon emission reduction target {T r1 , T r2 ,..., T rm}, the time allocation matrix T matrix and the set of dynamic adjustment parameters {α1, α2,..., α m} to generate low-carbon optimization recommendations {G1, G2,..., G m}, where each recommendation includes the target stage, the optimization time, and the adjustment parameter.

[0167] In this embodiment, by combining the priority of carbon emission hotspots and the carbon emission data within the whole life cycle of the building, the carbon emission reduction target for the whole life cycle is set, and the corresponding carbon emission reduction targets are allocated according to the carbon emission hotspot intensity in each stage. Further, according to the construction and operation plans in each stage, through the combination of optimization objectives, time allocation, and dynamic adjustment parameters, precise carbon emission optimization management can be achieved.

[0168] S6. The visualization display module is connected to the optimization recommendation generation module, and displays the multi-dimensional carbon emission data, the carbon emission hotspot intensity, and the low-carbon optimization recommendations in the form of a multi-dimensional chart.

[0169] In this embodiment, the visualization display module intuitively shows the relevance and dynamic change trend of carbon emission hotspots and optimization strategies. This module can more precisely help building managers identify key issues from complex carbon emission data, quickly locate high-emission stages, improve the efficiency of building low-carbon management and the scientificity of decision-making, and provide a powerful analysis and support tool for achieving the low-carbon goal of the whole life cycle of the building.

[0170] Example:

[0171] To verify the feasibility of the present invention, the present invention is applied to a green building pilot project located in Beijing. This project is a large commercial complex, including management requirements for the entire life cycle such as building material production, construction, operation, and demolition. Due to its large scale and complex energy use and carbon emission management, traditional carbon emission monitoring and optimization methods can no longer meet the needs of refined management and low-carbon construction. Therefore, the building low-carbon construction system of the present invention is deployed in this project to dynamically monitor, optimize the management of carbon emissions throughout the building life cycle, and generate intuitive optimization suggestions.

[0172] In this scenario, first, carbon emission data of the building from the material production stage to the construction, operation, and demolition stages is collected through Internet of Things devices. These data include energy consumption data of building materials in the production stage, fuel consumption data during transportation, energy use data of construction equipment, building energy consumption data in the operation stage, and carbon emission data generated from waste treatment during demolition. After all the data is normalized, it is encrypted and stored through blockchain technology to generate an immutable distributed carbon emission record.

[0173] Subsequently, through the intelligent contract execution module, based on predefined optimization strategies, the system automatically monitors and optimizes the carbon emission data of the entire building life cycle. In the operation stage, the system dynamically analyzes historical carbon emission records and real-time data, combines with the dynamic analysis module of the present invention, identifies the cooling system as the main carbon emission hot spot, and proposes a series of optimization measures through the optimization suggestion generation module, including adjusting the running time of the cooling system and improving energy efficiency. The optimization suggestions are presented to the project manager in the form of charts through the visualization display module, providing intuitive decision-making support for him. Three months after the actual deployment, the carbon emission management efficiency of the project has been significantly improved. The specific data is shown in the following table:

[0174] Table 1 Implementation effect data of the building low-carbon construction system

[0175]

[0176] Through the deployment of the present invention, the project achieved a reduction of the total monthly carbon emissions from 400 tons to 280 tons within three months, with an overall reduction of 30%. The system identified the cooling system as the main carbon emission hot spot through the dynamic analysis module, and the analysis accuracy rate increased from the traditional 75% to 95%. In addition, the generation and execution efficiency of the optimization strategy have been greatly improved, shortened from 8 hours each time to 2 hours, and the understanding and decision-making time of the manager has also been shortened from 6 hours to 1 hour.

[0177] In a typical case, the system found that the cooling system was running with high energy consumption continuously during off-peak hours. After analysis by the optimization suggestion generation module, it was proposed to concentrate the running time during peak hours and adopt more efficient cooling equipment. After adjustment, the energy efficiency improvement ratio of this cooling system reached 20%, and the carbon emissions were significantly reduced.

[0178] The application of the present invention has greatly improved the efficiency and scientificity of carbon emission management throughout the building life cycle. By combining Internet of Things, blockchain, and smart contract technologies, the present invention realizes dynamic monitoring and optimization of carbon emission data and provides intuitive visual analysis support. In practical applications, the present invention has significantly reduced the total carbon emissions of the project and provided a reliable technical guarantee for achieving the goal of low-carbon buildings.

[0179] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. A building low-carbon construction system, characterized in that, Including: S1. A carbon emission data collection module, which is used to collect and process carbon emission data during the whole life cycle of a building through Internet of Things sensing devices, and generate multi-dimensional carbon emission data; S2. A blockchain data management module, connected to the carbon emission data collection module, which is used to encrypt the collected multi-dimensional carbon emission data and store it in the blockchain, generating an immutable and traceable distributed carbon emission record; S3. A smart contract execution module, connected to the blockchain data management module, which automatically executes carbon emission monitoring and optimization operations at each stage of the whole life cycle of the building through the smart contract of the blockchain based on predefined carbon emission optimization strategies; S4. A dynamic analysis module, connected to the blockchain data management module and the smart contract execution module, which dynamically identifies the carbon emission hot spot intensity during the whole life cycle of the building based on historical and real-time data in the distributed carbon emission record; S5. An optimization suggestion generation module, which generates targeted low-carbon optimization suggestions based on the identification results of carbon emission hot spot intensity; S6. A visualization display module, connected to the optimization suggestion generation module, which displays multi-dimensional carbon emission data, carbon emission hot spot intensity, and low-carbon optimization suggestions in the form of multi-dimensional charts.

2. The low-carbon construction system for buildings according to claim 1, wherein The specific content of S2 includes: S21. An Internet of Things sensing device collects carbon emission data throughout the life cycle of a building, including energy consumption and carbon emission data D during the building material production stage m , fuel consumption and carbon emission data D during the transportation process t , energy consumption and equipment carbon emission data D during the construction process c , energy consumption and carbon emission data D during the building operation stage o and waste treatment carbon emission data D during the demolition stage d ; A data processing unit, connected to the Internet of Things sensing devices, which preprocesses the collected carbon emission data, including denoising, format conversion, and normalization processing; A timestamp generation unit, connected to the data processing unit, generates a unique timestamp T for each collected carbon emission data record k and generates the processed multi-dimensional carbon emission data D multi : D multi ={(D m ,T m ),(D t ,T t ),(D c ,T c ),(D o ,T o ),(D d ,T d )} Among them, D multi represents the processed multi-dimensional carbon emission data, and D x is the carbon emission data for each stage, and T x is the time stamp of the carbon emission data corresponding to the stage of D x ; S22. The data encryption unit is connected to the carbon emission data collection module and encrypts the processed multi-dimensional carbon emission data D multi using the composite asymmetric encryption method for encryption processing: C multi = E pub1 (H(D multi ) ⊕ K session ) ∥ E pub2 (K session ) Among them, C multi represents the encrypted multi-dimensional carbon emission data, E pub1 (·) and E pub2 (·) respectively represent the encryption functions using different public keys. H(D multi ) represents the hash value generated after hashing the multi-dimensional carbon emission data D multi . K session is the symmetric encryption session key, encrypted by E pub2 . ⊕ represents the bitwise exclusive OR operation, and ∥ represents the concatenation operation of the encryption results; S23. A data storage unit, connected to the data encryption unit, for storing the encrypted multi-dimensional carbon emission data C multi in the blockchain in the form of blocks to generate a distributed carbon emission record, and each block contains the following set of data fields: Block = {C multi , H prev , T block} Among them, Block represents the set of data fields of the block, and C multi represents the encrypted multi-dimensional carbon emission data, and H prev represents the hash value of the previous block, and T block represents the timestamp generated by the current block; S24. A data verification unit, connected to the data storage unit, which is used to verify the integrity of the block before adding a new block to the blockchain: Hash block = H(H prev || H(E pub1 (H(D multi )) ⊕ K session ) || E pub2 (K session )) ∥T block ) Among them, Hash block is the hash value of the current block, H(·) represents the calculation of the hash function, and H prev represents the hash value of the previous block, and E pub1 (H(D multi ) ⊕ K session ) ∥ E pub2 (K session ) is the encrypted multi-dimensional carbon emission data and its encrypted session key, and T block represents the generation timestamp of the current block; S25. A data access control unit, connected to the data storage unit, which provides authorized users with access rights to the multi-dimensional carbon emission data stored in the blockchain according to the permission control strategy.

3. The low-carbon construction system for buildings according to claim 1, characterized in that, The specific content of S3 includes: S31. Carbon emission optimization strategy definition unit, which pre-defines carbon emission optimization strategies {P1, P2,..., P n} according to the carbon emission monitoring requirements during the whole life cycle of the building. Each strategy includes a target stage, an optimization goal, and execution conditions, where the optimization goal includes a carbon emission threshold T r and an energy utilization rate improvement parameter E r ; S32. The smart contract generation unit, connected to the carbon emission optimization strategy definition unit, converts each carbon emission optimization strategy P i into a smart contract SC on the blockchain i , and attaches a unique identifier ID to each smart contract SC and a trigger condition C trigger : Among them, C trigger represents the calculation result of the trigger condition, D x represents the carbon emission data of the x-th stage, w x represents the weight parameter of the x-th stage, which is used to reflect the relative importance of this stage to the total carbon emissions and is a positive real number. m represents the total number of stages in the whole life cycle of the building, T r represents the predefined carbon emission threshold in the carbon emission optimization strategy. If C trigger ≤0, the smart contract is triggered to execute; S33. The smart contract deployment unit is connected to the smart contract generation unit, deploys the generated smart contract SC i to the blockchain, and associates the smart contract with the blockchain data management module; S34. The smart contract execution unit is connected to the blockchain data management module and the smart contract deployment unit, and according to the trigger condition C trigger performs real-time detection on the carbon emission data in the current stage. When the trigger condition is met, it calls the corresponding smart contract SC i to execute: Among them, D opt represents the optimized carbon emission data, and α x represents the optimization adjustment coefficient in the x-th stage, which is used to reflect the adjustment weight of carbon emissions in this stage. D x represents the carbon emission data in the x-th stage, m represents the total number of stages in the whole life cycle of the building, and E r represents the energy utilization rate improvement parameter, which is a non-negative real number; S35. Execution result blockchain unit, connected to the smart contract execution unit, records the hash value H(R SC ) of the execution result R sC corresponding to the smart contract onto the blockchain and updates the carbon emission record block of the current stage. The hash value of the new block is expressed as: H(Block ′ ) = H prev + H(R sC ) + T block ′ Among them, H(Block ′ ) represents the hash value of the new block, H prev represents the hash value of the previous block, H(R SC ) represents the hash value of the execution result corresponding to the smart contract, T block ′ represents the timestamp when the current new block is generated.

4. A building low-carbon construction system according to claim 3, characterized in that, The specific content of S31 includes: S311. A target stage definition unit, according to the carbon emission data during the whole life cycle of the building, divides the building life cycle into multiple target stages, including material production stage, transportation stage, construction stage, operation stage, and demolition stage, and generates a carbon emission stage matrix for each stage; M x = [E x , P x , R x ​ Among them, M x represents the carbon emission phase matrix in the x-th phase, E x represents the total energy consumption within the phase, P x represents the proportion vector of energy types, R x represents the stage resource recovery rate; S312. Optimization target setting unit, connected to the target stage definition unit, sets an optimization target for each target stage, and the optimization target includes a carbon emission threshold T r : Among them, and respectively represent the baseline energy consumption and target energy consumption in the x-th stage, and represent the baseline proportion and target proportion of the energy type, represents the target resource recovery rate in the x-th stage, and m is the total number of stages in the whole life cycle of the building; S313. An energy utilization rate improvement parameter calculation unit, connected to the optimization target setting unit, calculates an energy utilization rate improvement parameter E r : Among them, η x represents the energy utilization efficiency improvement factor in the x-th stage, which is a non-negative real number greater than 0; S314. An execution condition definition unit, connected to the optimization target setting unit and the energy utilization rate improvement parameter calculation unit, which defines the execution conditions according to the optimization target and the energy utilization rate improvement parameters; C exec = (T r ≤ T threshold ) ∧ (E r ≥ E min ) Among them, C exec represents the execution condition, T threshold is the predefined carbon emission threshold limit value, and E min is the minimum energy utilization rate improvement parameter; S315, a policy combination unit, is connected to the target stage definition unit, the optimization goal setting unit, and the execution condition definition unit, and is used to generate carbon emission optimization strategies {P1, P2,..., P n}, and each strategy is as follows: P i = [M x , T r , E r , C exec ​ Among them, P i represents the i-th carbon emission optimization strategy, M x is the carbon emission stage matrix, T r is the carbon emission threshold for the carbon emission reduction target, E r is the parameter for improving the energy utilization rate, C exec is the corresponding execution condition.

5. A building low-carbon construction system according to claim 1, characterized in that, The specific content of S4 includes: S41. A carbon emission data preprocessing unit, connected to the blockchain data management module, extracts historical carbon emission data D history and real-time carbon emission data D real-time from the distributed carbon emission records, and performs normalization processing on the extracted historical carbon emission data and real-time carbon emission data to generate the normalized carbon emission data D at the x-th stage x ′ ; S42. Carbon emission time series analysis unit, connected to the carbon emission data preprocessing unit, based on historical carbon emission data D history and real-time carbon emission data D real-time to construct a time series dynamic model, and the carbon emission trend model T x (t) is expressed as: Among them, T x (t) represents the dynamic trend of carbon emissions at time t in the x-th stage, D x ′ (t i ) represents the normalized carbon emission data in the x-th stage at time t i , γ x is the disturbance coefficient in the x-th stage, ω x represents the frequency of periodic change in the x-th stage, φ x represents the initial phase in the x-th stage, β x represents the time change adjustment coefficient in the x-th stage, t j represents the time point of the past moment, ∈ is a small positive number introduced to avoid the denominator being zero, k is the total number of data points within the time window, and N is the total number of sampling points; S43. A carbon emission hot spot identification unit, connected to the carbon emission time series analysis unit, calculates the carbon emission hot spot intensity H based on the carbon emission dynamic trend T x (t) x : Among them, H x represents the carbon emission hot spot intensity in the x-th stage, w x represents the weight parameter in the x-th stage, α is a positive definite smoothing parameter, is the sum of the carbon emission trends of all stages at time t, [t1, t2] is the analysis time interval, and m represents the total number of stages in the whole life cycle of the building; S44. A hotspot sorting unit, connected to the carbon emission hotspot identification unit, sorts each stage according to the calculated carbon emission hotspot intensity H x to generate a carbon emission hotspot priority list {H1, H2,..., H m}, where m represents the total number of stages in the whole life cycle of the building.

6. The low-carbon construction system for buildings according to claim 5, characterized in that, The specific content of S5 includes: S51. A carbon emission reduction target definition unit, connected to the hot spot sorting unit, sets the carbon emission reduction target T during the whole life cycle, and allocates the carbon emission reduction targets {T m} for each stage according to the carbon emission hot spot priority list {H1, H2,..., H total} and the total initial carbon emissions T during the whole life cycle of the building: goal r1 , T r2 ,..., T rm}:​ T goal = T total ·R target Among them, T rx represents the carbon emission reduction target for the x-th stage, and H x is the carbon emission hot spot intensity for the x-th stage. T goal is the total carbon emission reduction target over the entire life cycle. T total is the total initial carbon emissions over the building's entire life cycle, and R target is the carbon emission reduction ratio, with a value range of 0 to 1, and m is the total number of stages in the building's entire life cycle; S52, a time allocation unit, is connected to the carbon emission reduction target definition unit and sets the time allocation matrix T for optimized execution according to the carbon emission reduction targets {T r1 , T r2 ,..., T rm} and the construction and operation plans for each stage matrix : Among them, T matrix represents the time allocation matrix for optimized execution, and t ij represents the optimized time allocated to the j-th stage in the i-th stage; S53. An adjustment parameter generation unit, connected to the time allocation unit, calculates, according to the time allocation matrix T matrix and the carbon emission reduction target {T r1 , T r2 ,..., T rm}, the set of dynamic adjustment parameters {α1, α2,..., α m} for each stage: Among them, α x represents the dynamic adjustment parameter in the x-th stage, is the carbon emission reduction target in the x-th stage, t x is the actual optimization time in the x-th stage, η x is the energy utilization efficiency in the x-th stage; S54. The recommendation generation unit is connected to the adjustment parameter generation unit and combines the carbon emission reduction target {T r1 , T r2 ,..., T rm}, the time allocation matrix T matrix and the dynamic adjustment parameter set {α1, α2,..., α m} to generate low-carbon optimization recommendations {G1, G2,..., G m}, where each recommendation includes the target stage, the optimization time, and the adjustment parameter.

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