Railway station building carbon emission monitoring method, device and equipment based on block chain

Abstract

The invention provides a railway station building carbon emission monitoring method, device and equipment based on a block chain, and belongs to the field of carbon emission. The method comprises the following steps: calculating a carbon emission performance value of each regional unit at the current moment; generating a carbon emission information chain of the block chain according to the carbon emission data and the carbon emission performance value of each region unit at the current moment; acquiring carbon emission historical data of each region unit at all moments and a carbon emission performance value at the current moment from the carbon emission information chain, and predicting a carbon emission performance prediction value of each region unit at the current moment according to the carbon emission historical data; and if a target area unit of which the difference value between the carbon emission performance value of the area unit at the current moment and the carbon emission performance predicted value is greater than a preset threshold exists, sending an early warning signal to the target area unit. Accurate carbon emission data are provided through the block chain, prediction and early warning of a carbon emission performance value are realized, and green and low-carbon transformation of a railway station building is promoted.

Description

Technical Field

[0001] The present application relates to the field of carbon emissions, and in particular to a blockchain-based railway station carbon emission monitoring method, device and equipment. Background Art

[0002] With global climate change and increasing environmental protection requirements, carbon emissions management has become a critical issue across all industries. Railway stations, at a specific regional scale, face numerous challenges. Currently, carbon emissions monitoring, assessment, and management within railway stations are plagued by issues such as untimely data collection, opaque information transmission, and inefficient carbon emissions management, resulting in a lack of accurate monitoring of carbon emissions within railway stations. Summary of the Invention

[0003] In order to solve the above technical problems, the embodiments of the present application provide a blockchain-based railway station carbon emission monitoring method, device and equipment.

[0004] In a first aspect, an embodiment of the present application provides a blockchain-based railway station carbon emission monitoring method, the method comprising: Divide the railway station area into blocks according to its geographical location, functional layout and energy usage, and obtain multiple independently monitored regional units; Obtaining carbon emission data of each of the regional units at a current moment, and calculating a carbon emission performance value of each of the regional units at a current moment based on the carbon emission data; Generate a carbon emission information chain on the blockchain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment; Obtaining the carbon emission historical data of all times and the carbon emission performance value of the current time of each of the regional units from the carbon emission information chain, and predicting the carbon emission performance prediction value of the current time of each of the regional units based on the carbon emission historical data; If there is a target regional unit where the difference between the carbon emission performance value of the regional unit at the current moment and the carbon emission performance forecast value is greater than a preset threshold, a warning signal is sent to the target regional unit.

[0005] In one embodiment, the calculating the carbon emission performance value of each of the regional units at the current moment based on the carbon emission data includes: Based on the carbon emission data, the carbon emission performance value of each regional unit at the current moment is calculated through a weighted summation model. , the weighted sum model is formula (1); Formula (1): ; Where, is the energy efficiency index, is the carbon emission intensity indicator, To optimize indicators for economic structure, 、 and The energy efficiency indicators are 、The carbon emission intensity index and the economic structure optimization indicators The corresponding first-category weight coefficient.

[0006] In one embodiment, the method further comprises: Determine the first type weight coefficient by principal component analysis 、 and ,include: For historical data sets 、 and Perform Z-score standardization to obtain standardized data; Calculating the covariance matrix of the standardized data and extracting the eigenvector of the first principal component; Normalize the proportions of each component of the eigenvector of the first principal component to obtain the first-class weight coefficient corresponding to each indicator 、 and .

[0007] In one embodiment, the method further comprises: Obtain the economic structure optimization index The values ​​of the four target sub-indicators include the proportion of the tertiary industry, the proportion of clean energy investment, the intensity of green technology research and development, and the participation in carbon emissions trading; The economic structure optimization index in carbon emission data is obtained through formula (2) ; Formula (2): ; in, is the normalized value of the proportion of the tertiary industry, is the normalized value of the clean energy investment ratio, is the normalized value of the green technology R&D intensity, is the normalized value of the carbon emissions trading participation ratio, for The corresponding second type weight coefficient.

[0008] In one embodiment, a judgment matrix is ​​obtained, wherein the matrix elements of the judgment matrix are It represents the importance scale of the i-th project sub-indicator to the j-th project sub-indicator; Calculate the geometric mean of each row of the judgment matrix and normalize the geometric mean of each row to obtain the second type weight coefficient of each target sub-indicator , , , ; Calculate the consistency ratio based on the judgment matrix and the second type of weight coefficient. If the consistency ratio is less than the preset threshold, the second type of weight coefficient takes effect. If the consistency ratio is greater than or equal to the preset threshold, the judgment matrix needs to be readjusted.

[0009] In one embodiment, the carbon emission information chain of the blockchain is generated based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment, including: Packaging the carbon emission data of each regional unit at the current moment into a blockchain transaction, writing the blockchain transaction into the blockchain through a consensus mechanism, and generating a unique data identifier using a hash algorithm; Automatically verify through smart contracts whether the format of the carbon emission data of each regional unit meets the preset standards. If not, trigger a re-collection instruction to re-collect the carbon emission data of each regional unit; If they match, a data index of the carbon emission data of each regional unit at the current moment is constructed based on the data unique identifier and the Merkle tree; The carbon emission performance value is written into the blockchain and linked to the data index of the carbon emission data of each corresponding regional unit at the current moment through a smart contract to generate an auditable carbon emission information chain.

[0010] In one embodiment, the predicting of the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data includes: Based on the historical carbon emission data, obtaining the carbon emission forecast data of each regional unit at the current moment through machine learning; According to the carbon emission prediction data of each of the regional units at the current moment, the carbon emission performance prediction value of each of the regional units at the current moment is calculated by formula (1).

[0011] In a second aspect, an embodiment of the present application provides a blockchain-based railway station carbon emission monitoring device, the device comprising: A division module is used to divide the railway station area into blocks according to the geographical location, functional layout and energy usage of the railway station area to obtain multiple independently monitored regional units; a calculation module, configured to obtain carbon emission data of each of the regional units at a current moment, and calculate the carbon emission performance value of each of the regional units at a current moment based on the carbon emission data; A generation module, configured to generate a carbon emission information chain of the blockchain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment; A prediction module, configured to obtain the carbon emission historical data of each of the regional units at all times and the carbon emission performance value at the current moment from the carbon emission information chain, and predict the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data; The monitoring module is used to determine if there is a target regional unit whose difference between the carbon emission performance value of the regional unit at the current moment and the carbon emission performance forecast value is greater than a preset threshold, and then send an early warning signal to the target regional unit.

[0012] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is run by the processor, the blockchain-based railway station carbon emission monitoring method provided in the first aspect is executed.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when running on a processor, executes the blockchain-based railway station carbon emission monitoring method provided in the first aspect.

[0014] The blockchain-based railway station carbon emission monitoring method provided by the present application divides the railway station area into blocks according to the geographical location, functional layout and energy usage of the railway station area to obtain a plurality of independently monitored regional units; obtains the carbon emission data of each of the regional units at the current moment, and calculates the carbon emission performance value of each of the regional units at the current moment based on the carbon emission data; generates a blockchain carbon emission information chain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment; obtains the carbon emission historical data of all moments of each of the regional units and the carbon emission performance value at the current moment from the carbon emission information chain, and predicts the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data; if there is a target regional unit whose carbon emission performance value at the current moment and the carbon emission performance prediction value are greater than a preset threshold, an early warning signal is sent to the target regional unit. Accurate carbon emission data is provided by blockchain to realize the prediction and early warning of carbon emission performance value, and promote the green and low-carbon transformation of railway stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of this application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of this application and should not be regarded as limiting the scope of protection of this application. In each of the drawings, similar components are numbered similarly.

[0016] Figure 1 A flowchart of a blockchain-based railway station carbon emission monitoring method provided in an embodiment of the present application is shown; Figure 2 Another flowchart of the blockchain-based railway station carbon emission monitoring method provided in an embodiment of the present application is shown; Figure 3 Another flowchart of the blockchain-based railway station carbon emission monitoring method provided in an embodiment of the present application is shown; Figure 4 Another flowchart of the blockchain-based railway station carbon emission monitoring method provided in an embodiment of the present application is shown; Figure 5 Another flowchart of the blockchain-based railway station carbon emission monitoring method provided in an embodiment of the present application is shown; Figure 6 A structural schematic diagram of a railway station carbon emission monitoring device based on blockchain provided in an embodiment of the present application is shown.

[0017] Icon: 600-Blockchain-based railway station carbon emission monitoring device, 601-division module, 602-calculation module, 603-generation module, 604-prediction module, 605-monitoring module. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0019] The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but rather merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0020] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present application, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0021] Furthermore, the terms “first,” “second,” “third,” etc., are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0022] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as in the context of the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present application.

[0023] Example 1 The embodiments of the present application provide a blockchain-based method for monitoring carbon emissions in railway stations.

[0024] See also Figure 1 The blockchain-based railway station carbon emission monitoring method includes steps S101-S105: S101: Divide the railway station area into blocks according to the geographical location, functional layout and energy usage of the railway station area to obtain a plurality of independently monitored regional units.

[0025] In this embodiment, vector map data of the railway station building is obtained through a geographic information system, including the spatial distribution of building outlines, functional zones (such as waiting halls, platforms, equipment rooms, and office areas), and energy facilities (distribution rooms, air-conditioning units) in terms of geographical location. A K-means clustering algorithm is used to divide the station building into several candidate areas based on the geographical location and functional zone data, and constraints are set. The same functional area cannot be split, and the area of ​​the area must be greater than a minimum threshold (such as 500 m2).

[0026] Furthermore, candidate areas are analyzed for energy characteristics (e.g., energy intensity and load curve similarity) to further split or merge areas with significantly different energy usage patterns. For example, high-energy-consuming waiting halls can be separated from low-energy-consuming office areas, or areas with similar air conditioning loads can be merged to create multiple independently monitored regional units.

[0027] S102: Acquire carbon emission data of each of the regional units at a current moment, and calculate the carbon emission performance value of each of the regional units at a current moment based on the carbon emission data.

[0028] In this embodiment, carbon emission data includes data such as total carbon emissions and energy consumption. By deploying multiple types of sensors in each regional unit, such as energy consumption sensors, real-time data on electricity (electricity meter), natural gas (flow meter), and machine hours (operating time / power) are collected. Energy consumption is obtained by collecting data such as electricity and natural gas, and data such as total carbon emissions are calculated by combining the carbon emission factors of various types of energy.

[0029] In one embodiment, the step S102 includes: calculating the carbon emission performance value of each of the regional units at the current moment through a weighted sum model based on the carbon emission data. , the weighted sum model is formula (1); formula (1): Where, is the energy efficiency index, is the carbon emission intensity indicator, To optimize indicators for economic structure, 、 and The energy efficiency indicators are 、The carbon emission intensity index and the economic structure optimization indicators The corresponding first-category weight coefficient.

[0030] In this embodiment, the energy efficiency index is first calculated , which is calculated as , where unit output value is the economic output value of the regional unit, and energy consumption is the total standard coal consumption of the regional unit; then calculate the carbon emission intensity index , which is calculated as The total carbon emissions are calculated by combining the carbon emission factors of various energy sources with the real-time monitoring data of electricity, gas and renewable energy usage; finally, the economic structure optimization index is calculated. , calculated through the normalized weighted values ​​of four sub-indicators: the proportion of the tertiary industry, the proportion of clean energy investment, the intensity of green technology R&D, and the participation in carbon emissions trading; 、 and Energy efficiency index , carbon emission intensity index and economic structure optimization indicators The corresponding first-category weight coefficients are determined by statistically analyzing historical data using the principal component analysis method.

[0031] See also Figure 2 In one embodiment, the method further comprises determining the first type weight coefficient by principal component analysis. 、 and , including steps S201-S203: S201: Historical data set 、 and Z-score normalization was performed to obtain standardized data.

[0032] In this embodiment, in order to eliminate the 、 and Different dimensional differences in indicator data, calculate the mean and standard deviation of each indicator in the historical data set, and then calculate the mean and standard deviation of each indicator according to Z-score standardization is performed on different indicators, among which, is the data to be standardized, is the mean, is the variance.

[0033] S202: Calculate the covariance matrix of the standardized data and extract the eigenvector of the first principal component.

[0034] In this embodiment, according to 、 and The standardized data matrix is ​​constructed by the data after the indicators are standardized , the data matrix The dimension is dimension, is the sample coefficient, through the formula , and get the covariance matrix , solve the covariance matrix The eigenvalue of and eigenvectors , and sort the eigenvalues ​​by size, and determine the eigenvector corresponding to the largest eigenvalue as the eigenvector of the first principal component.

[0035] S203: Normalize the proportions of the components of the eigenvector of the first principal component to obtain the first type of weight coefficient corresponding to each indicator 、 and .

[0036] In this embodiment, the eigenvector of the first principal component The original component represents 、 and The contribution ratio of the indicator to the principal component is obtained by normalizing the original component of the eigenvector to obtain the first type of weight coefficient 、 and ,make sure .

[0037] In one embodiment, the method further comprises: obtaining the economic structure optimization index The values ​​of the four target sub-indicators of the carbon emission data are as follows: the proportion of the tertiary industry, the proportion of clean energy investment, the intensity of green technology R&D and the participation in carbon emission trading; the economic structure optimization index in the carbon emission data is obtained by formula (2): ;Formula (2): ;in, is the normalized value of the proportion of the tertiary industry, is the normalized value of the clean energy investment ratio, is the normalized value of the green technology R&D intensity, is the normalized value of the carbon emissions trading participation ratio, for The corresponding second type weight coefficient.

[0038] In this embodiment, the original values ​​of railway station economic structure related indicators are collected from various data sources, such as the proportion of the tertiary industry in each regional unit obtained from the regional economic report of the local statistical bureau. The proportion of the tertiary industry reflects the proportion of the service industry in the economic structure. The higher the value, the better the low-carbon performance. For example, the proportion of clean energy investment in each regional unit can be obtained from the station energy management ledger. The clean energy investment ratio refers to the percentage of renewable energy (such as photovoltaic and wind energy) investment in total energy investment; for example, the green technology R&D intensity of each regional unit can be obtained from the enterprise R&D investment report. Green technology R&D intensity refers to the proportion of green technology R&D funds to the total R&D investment of the station; for example, the carbon emission trading participation of each regional unit can be obtained from the carbon trading platform registration records. ,Participation in carbon emission trading refers to the level of active participation in carbon market ,trading, and reflects the enthusiasm for emission reduction.

[0039] Furthermore, the extreme value method is used to normalize the data of each indicator to the range of [0, 1] through formula (3) to eliminate the dimensional difference.

[0040] , in, Optimizing indicators for economic structure The values ​​of each sub-indicator of and They are the historical extreme values ​​of each sub-indicator in the same regional unit.

[0041] Through formula (2): The normalized sub-indicators are weighted and summed to obtain the economic structure optimization index. ,in, is the normalized value of the proportion of the tertiary industry, is the normalized value of the clean energy investment ratio, is the normalized value of the green technology R&D intensity, is the normalized value of the carbon emissions trading participation ratio, for The corresponding second type weight coefficient.

[0042] See also Figure 3 In one embodiment, the method further includes steps S301-S303: S301: Obtain a judgment matrix, the matrix elements of which are It represents the importance scale of the i-th project indicator to the j-th project indicator.

[0043] In this example, a judgment matrix was obtained, which was generated by railway industry experts comparing the importance of four economic structure indicators pairwise. A 1-9 scale was used, where 1 indicates that both indicators are equally important, 3 indicates that the former is slightly more important than the latter, 5 indicates significantly important, 7 indicates strongly important, 9 indicates extremely important, and 2, 4, 6, and 8 are intermediate values. This 4×4 judgment matrix consists of four indicators: the proportion of the tertiary industry, the proportion of clean energy investment, the intensity of green technology R&D, and participation in carbon emissions trading. The specific values ​​reflect the relative importance of each indicator.

[0044] S302: Calculate the geometric mean of each row of the judgment matrix and normalize the geometric mean of each row to obtain the second type weight coefficient of each target sub-indicator , , , .

[0045] In this embodiment, the geometric mean of the four elements in each row of the judgment matrix is ​​calculated, and the geometric mean of other rows is calculated in turn. The geometric mean of all rows is summed up, and each geometric mean is divided by the sum to obtain the weight of each indicator. , , , .

[0046] S303: Calculate the consistency ratio based on the judgment matrix and the second type of weight coefficient. If the consistency ratio is less than a preset threshold, the second type of weight coefficient takes effect. If the consistency ratio is greater than or equal to the preset threshold, the judgment matrix needs to be readjusted.

[0047] In this embodiment, the judgment matrix and the weight vector (given by , , , The product result matrix composed of) is calculated based on the product result matrix. The maximum eigenvalue is calculated based on the product result matrix. , according to the maximum eigenvalue , and by the formula Get consistency indicators, where is the number of target sub-indicators, and according to the matrix order of the judgment matrix (that is, the number of target sub-indicators ) Look up the table to get the random consistency index , and by the formula Obtain the consistency ratio. If the consistency ratio is less than the preset threshold, the second type of weight coefficient takes effect. If the consistency ratio is greater than or equal to the preset threshold, the judgment matrix needs to be readjusted. S103: Generate a carbon emission information chain of the blockchain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment.

[0048] In this embodiment, the distributed ledger technology of the blockchain is used to store the carbon emission data and carbon emission performance indicators of each regional unit on the chain, ensuring that the data cannot be tampered with and is traceable, thereby solving the problems of data falsification and lack of trust in traditional carbon emission accounting.

[0049] See also Figure 4 In one embodiment, the S103 includes steps S1031-S1034: S1031: Packaging the carbon emission data of each of the regional units at the current moment into a blockchain transaction, writing the blockchain transaction into the blockchain through a consensus mechanism, and generating a unique data identifier using a hash algorithm.

[0050] In this embodiment, an independent carbon emissions monitoring system, including sensors and data acquisition equipment, is deployed within each regional unit. These sensors and data acquisition equipment monitor air quality, temperature, energy consumption, and related carbon emissions data (e.g., electricity, natural gas, and machine hours) in real time. The collected carbon emissions data for each regional unit is packaged into blockchain transactions in a pre-defined format. Each transaction contains key fields such as a timestamp, regional unit ID, and data value, forming a structured data packet.

[0051] Furthermore, a practical Byzantine Fault Tolerance (PBFT) consensus mechanism is employed, with multiple verification nodes verifying transactions. Once verified, the transaction is written into a new block, and a block hash value is generated as the global identifier for that batch of data. A SHA-256 hash value is then calculated for each area unit's data, serving as the digital fingerprint of that data block. For example, the power consumption data for area unit A is hashed to generate a fixed-length string (e.g., "a1b2c3..."), ensuring that the data cannot be tampered with.

[0052] S1032: Automatically verify through a smart contract whether the format of the carbon emission data of each of the regional units meets the preset standard. If not, trigger a re-collection instruction to re-collect the carbon emission data of each of the regional units.

[0053] In this embodiment, the smart contract deployed on the chain automatically verifies whether the collected data format complies with the ISO14064 standard, including checking field integrity (such as whether the timestamp is missing), verifying the value range (such as whether the energy consumption value is non-negative), and confirming the validity of the data signature.

[0054] Furthermore, if there is data that does not meet the standards, the smart contract automatically triggers the sending of re-collection instructions to the data collection terminal and records the abnormal event (including error type, time, etc.) on the blockchain.

[0055] S1033: If it is in compliance, construct a data index of the carbon emission data of each regional unit at the current moment according to the data unique identifier and the Merkle tree.

[0056] In this embodiment, if compliance data is marked as "verified," the subsequent index construction process is triggered. The hash values ​​of each regional unit in the same time period are used as leaf nodes, and a Merkle tree root hash is generated through layer-by-layer hashing. For example, the hash values ​​H_A, H_B, and H_C of regions A, B, and C are first hashed pairwise to obtain H_AB and H_BC, and then hashed to obtain the root H_root. The root H_root is bound to the block header to form an unalterable data snapshot. For example, to verify the carbon emissions record of regional unit C on a specific day, one only needs to provide the data hash and its Merkle path (H_D, H_AB, etc.) to verify its authenticity by comparing it with the root hash.

[0057] S1034: Write the carbon emission performance value into the blockchain and link it to the data index of the carbon emission data of each corresponding regional unit at the current moment through a smart contract to generate an auditable carbon emission information chain.

[0058] In this embodiment, by adding a metadata pointer of the performance result to the header of the original data block, a bidirectional link is established between the carbon emission performance value and the Merkle index of the corresponding area. Each new block contains the hash value of the previous block, forming a chain structure sorted by time, and finally obtaining an auditable carbon emission information chain. The carbon emission information chain can use the Merkle index to view the emission data of all areas at a certain moment at the same time, and can trace back the historical emission trajectory of a specific area along the blockchain.

[0059] S104: Obtaining the carbon emission historical data of all moments of each of the regional units and the carbon emission performance value at the current moment from the carbon emission information chain, and predicting the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data.

[0060] In this embodiment, since the blockchain ensures the integrity and non-tamperability of historical carbon emission data, it provides high-quality training data for the prediction model. Through the input of historical data, dynamic prediction of carbon emission performance at the current moment can be achieved, thereby supporting immediate decision-making.

[0061] See also Figure 5 In one embodiment, step S104 includes S1041-S1042.

[0062] S1041: Based on the carbon emission historical data, obtain the carbon emission prediction data of each regional unit at the current moment through machine learning.

[0063] In this embodiment, based on the historical carbon emission data stored in the blockchain, a machine learning model is used to dynamically predict the carbon emission performance-related indicators of the regional units. The machine learning model is a machine learning model suitable for time series prediction (such as deep learning models such as LSTM, GRU, Transformer, or integrated models such as XGBoost, LightGBM, etc.).

[0064] Specifically, first extract the complete time series data of each regional unit from the blockchain, including the historical energy efficiency , carbon emission intensity and economic structure optimization indicators Detailed composition data of a certain area unit in the past period of time, for example 、 Indicators and The four sub-indicators (such as the proportion of the tertiary industry) are aligned in time series to form a multi-dimensional feature matrix. Then, the machine learning model is used to learn the time-dependent characteristics of the data, that is, the evolution law of each indicator itself, such as the lag effect of clean energy investment on carbon emission intensity; the coupling characteristics of the indicators are learned, that is, the learning 、 Indicators and The nonlinear relationship between each sub-indicator, such as the promoting effect of the increase in the proportion of the tertiary industry on the energy utilization efficiency index, is learned through the feature fitting loss function model, and finally the carbon emission forecast data of each regional unit at the current moment is obtained.

[0065] S1042: Based on the carbon emission prediction data of each of the regional units at the current moment, the carbon emission performance prediction value of each of the regional units at the current moment is calculated using formula (1).

[0066] In this embodiment, the prediction results of each indicator of the regional unit are still calculated using the original weight formula: Calculate the predicted carbon emission performance value , but among them 、 Indicators and The four sub-indicators all use predicted values.

[0067] S105: If there is a target regional unit where the difference between the current carbon emission performance value of the regional unit and the predicted carbon emission performance value is greater than a preset threshold, a warning signal is sent to the target regional unit.

[0068] In this example, if a railway station has a monthly output value of 1 million yuan, energy consumption of 50 tons of standard coal, and total carbon emissions of 200 tons, data on the following economic structure optimization indicators are collected, including the proportion of the tertiary industry: 65% (from the regional economic report of the local statistical bureau); the proportion of clean energy investment: 30% (station energy management ledger); green technology R&D intensity: 2.5% (enterprise R&D investment report); and carbon emissions trading participation: 80% (carbon trading platform registration record).

[0069] The economic structure optimization index data is normalized by formula (3), and the second type of weight coefficient of each economic structure optimization index is determined by the hierarchical analysis method, among which the proportion of the tertiary industry is , the proportion of clean energy ; Investment intensity of green R&D ;Participation in carbon trading ; Conduct principal component analysis on the previous carbon emission intensity data and build a weighted assessment model. The first type of weight coefficient 、 and 0.4, 0.3, 0.3 respectively, quantifying energy efficiency , carbon emission intensity and economic structure optimization The contribution of , , the economic structure optimization index is calculated by formula (2) , the final carbon emission performance is , and the current carbon emission performance forecast value and the current carbon emission performance value When the difference between the two reaches a preset threshold (the preset threshold can be 0.5), an early warning signal is sent to the target area unit. The rating standard is: when P<1, the rating is "poor"; when 1≤P≤5, the rating is "good"; when P>5, the rating is "excellent". The rating of this carbon emission is "good", and the rating results are sent to the target area unit at the same time.

[0070] The blockchain-based railway station carbon emission monitoring method provided in this embodiment divides the railway station area into blocks based on its geographical location, functional layout, and energy usage, obtaining multiple independently monitored regional units; obtains the carbon emission data of each regional unit at the current moment, and calculates the carbon emission performance value of each regional unit at the current moment based on the carbon emission data; generates a blockchain carbon emission information chain based on the carbon emission data and carbon emission performance value of each regional unit at the current moment; obtains the carbon emission historical data of each regional unit at all times and the carbon emission performance value at the current moment from the carbon emission information chain, and predicts the carbon emission performance prediction value of each regional unit at the current moment based on the carbon emission historical data; if there is a target regional unit whose carbon emission performance value at the current moment and the carbon emission performance prediction value are greater than a preset threshold, an early warning signal is sent to the target regional unit. By providing accurate carbon emission data through blockchain, the prediction and early warning of carbon emission performance values ​​are realized, promoting the green and low-carbon transformation of railway stations.

[0071] Example 2 In addition, an embodiment of the present application provides a blockchain-based railway station carbon emission monitoring device, which is applied to electronic equipment.

[0072] like Figure 6 As shown, the blockchain-based railway station carbon emission monitoring device 600 includes: A division module 601 is configured to divide the railway station area into blocks according to its geographical location, functional layout, and energy usage, thereby obtaining a plurality of independently monitored regional units; A calculation module 602 is configured to obtain carbon emission data of each of the regional units at a current moment, and calculate a carbon emission performance value of each of the regional units at a current moment based on the carbon emission data; A generation module 603 is configured to generate a carbon emission information chain of the blockchain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment; The prediction module 604 is configured to obtain the carbon emission historical data of each of the regional units at all times and the carbon emission performance value at the current moment from the carbon emission information chain, and predict the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data; The monitoring module 605 is configured to determine if there is a target regional unit whose difference between the current carbon emission performance value of the regional unit and the predicted carbon emission performance value is greater than a preset threshold, and then send an early warning signal to the target regional unit.

[0073] Optionally, the calculation module 602 is further configured to calculate the carbon emission performance value of each of the regional units at the current moment based on the carbon emission data using a weighted sum model. , the weighted sum model is formula (1); Formula (1): ; Where, is the energy efficiency index, is the carbon emission intensity indicator, To optimize indicators for economic structure, 、 and The energy efficiency indicators are 、The carbon emission intensity index and the economic structure optimization indicators The corresponding first-category weight coefficient.

[0074] Optionally, the calculation module 602 is further configured to determine the first type of weight coefficients by principal component analysis. 、 and ,include: For historical data sets 、 and Perform Z-score standardization to obtain standardized data; Calculating the covariance matrix of the standardized data and extracting the eigenvector of the first principal component; Normalize the proportions of each component of the eigenvector of the first principal component to obtain the first-class weight coefficient corresponding to each indicator 、 and .

[0075] Optionally, the calculation module 602 is further used to obtain the economic structure optimization index The values ​​of the four target sub-indicators include the proportion of the tertiary industry, the proportion of clean energy investment, the intensity of green technology research and development, and the participation in carbon emissions trading; The economic structure optimization index in carbon emission data is obtained through formula (2) ; Formula (2): ; in, is the normalized value of the proportion of the tertiary industry, is the normalized value of the clean energy investment ratio, is the normalized value of the green technology R&D intensity, is the normalized value of the carbon emissions trading participation ratio, for The corresponding second type weight coefficient.

[0076] Optionally, the calculation module 602 is further configured to obtain a judgment matrix, wherein the matrix elements of the judgment matrix are It represents the importance scale of the i-th project sub-indicator to the j-th project sub-indicator; Calculate the geometric mean of each row of the judgment matrix and normalize the geometric mean of each row to obtain the second type weight coefficient of each target sub-indicator , , , ; Calculate the consistency ratio based on the judgment matrix and the second type of weight coefficient. If the consistency ratio is less than the preset threshold, the second type of weight coefficient takes effect. If the consistency ratio is greater than or equal to the preset threshold, the judgment matrix needs to be readjusted.

[0077] Optionally, the generating module 603 is further configured to package the carbon emission data of each of the regional units at the current moment into a blockchain transaction, write the blockchain transaction into the blockchain through a consensus mechanism, and generate a unique data identifier using a hash algorithm; Automatically verify through smart contracts whether the format of the carbon emission data of each regional unit meets the preset standards. If not, trigger a re-collection instruction to re-collect the carbon emission data of each regional unit; If they match, a data index of the carbon emission data of each regional unit at the current moment is constructed based on the data unique identifier and the Merkle tree; The carbon emission performance value is written into the blockchain and linked to the data index of the carbon emission data of each corresponding regional unit at the current moment through a smart contract to generate an auditable carbon emission information chain.

[0078] Optionally, the prediction module 604 is further configured to obtain carbon emission prediction data of each of the regional units at the current moment through machine learning based on the historical carbon emission data; According to the carbon emission prediction data of each of the regional units at the current moment, the carbon emission performance prediction value of each of the regional units at the current moment is calculated by formula (1).

[0079] The blockchain-based railway station carbon emission monitoring device 600 provided in this embodiment can implement the blockchain-based railway station carbon emission monitoring method provided in Example 1. To avoid repetition, it will not be described here.

[0080] The blockchain-based railway station carbon emission monitoring device provided in this embodiment divides the railway station area into blocks according to the geographical location, functional layout, and energy usage of the railway station area to obtain multiple independently monitored regional units; obtains the carbon emission data of each regional unit at the current moment, and calculates the carbon emission performance value of each regional unit at the current moment based on the carbon emission data; generates a blockchain carbon emission information chain based on the carbon emission data and carbon emission performance value of each regional unit at the current moment; obtains the carbon emission historical data of each regional unit at all times and the carbon emission performance value at the current moment from the carbon emission information chain, and predicts the carbon emission performance prediction value of each regional unit at the current moment based on the carbon emission historical data; if there is a target regional unit whose carbon emission performance value at the current moment and the carbon emission performance prediction value are greater than a preset threshold, an early warning signal is sent to the target regional unit. By providing accurate carbon emission data through blockchain, the prediction and early warning of carbon emission performance values ​​are realized, promoting the green and low-carbon transformation of railway stations.

[0081] Example 3 In addition, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program runs on the processor, it executes the blockchain-based railway station carbon emission monitoring method provided in Example 1.

[0082] The electronic device provided in the embodiment of the present invention can execute the steps of the blockchain-based railway station carbon emission monitoring method provided in the above-mentioned method embodiment 1. To avoid repetition, they will not be repeated here.

[0083] Example 4 The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the blockchain-based railway station carbon emission monitoring method provided in Example 1 is implemented.

[0084] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0085] The computer-readable storage medium provided in this embodiment can implement the blockchain-based railway station carbon emission monitoring method provided in Example 1. To avoid repetition, it will not be described here.

[0086] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or terminal comprising the element.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0088] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A blockchain-based railway station carbon emission monitoring method, characterized in that: The method comprises: Divide the railway station area into blocks according to its geographical location, functional layout and energy usage, and obtain multiple independently monitored regional units; Obtaining carbon emission data of each of the regional units at a current moment, and calculating a carbon emission performance value of each of the regional units at a current moment based on the carbon emission data; Generate a carbon emission information chain on the blockchain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment; Obtaining the carbon emission historical data of all times and the carbon emission performance value of the current time of each of the regional units from the carbon emission information chain, and predicting the carbon emission performance prediction value of the current time of each of the regional units based on the carbon emission historical data; If there is a target regional unit where the difference between the carbon emission performance value of the regional unit at the current moment and the carbon emission performance forecast value is greater than a preset threshold, a warning signal is sent to the target regional unit.

2. The method according to claim 1, characterized in that The calculating, based on the carbon emission data, the carbon emission performance value of each of the regional units at the current moment includes: Based on the carbon emission data, the carbon emission performance value of each regional unit at the current moment is calculated through a weighted summation model. , the weighted sum model is formula (1); Formula (1): ; Where, is the energy efficiency index, is the carbon emission intensity indicator, To optimize indicators for economic structure, 、 and The energy efficiency indicators are 、The carbon emission intensity index and the economic structure optimization indicators The corresponding first-class weight coefficient.

3. The method according to claim 2, characterized in that The method further comprises: Determine the first type weight coefficient by principal component analysis 、 and ,include: For historical data sets 、 and Perform Z-score standardization to obtain standardized data; Calculating the covariance matrix of the standardized data and extracting the eigenvector of the first principal component; Normalize the proportions of each component of the eigenvector of the first principal component to obtain the first-class weight coefficient corresponding to each indicator 、 and .

4. The method according to claim 2, characterized in that The method further comprises: Obtain the economic structure optimization index The values ​​of the four target sub-indicators include the proportion of the tertiary industry, the proportion of clean energy investment, the intensity of green technology research and development, and the participation in carbon emissions trading; The economic structure optimization index in carbon emission data is obtained through formula (2) ; Formula (2): ; in, is the normalized value of the proportion of the tertiary industry, is the normalized value of the clean energy investment ratio, is the normalized value of the green technology R&D intensity, is the normalized value of the carbon emissions trading participation ratio, for The corresponding second type weight coefficient.

5. The method according to claim 4, characterized in that The method further comprises: Get the judgment matrix, the matrix elements of the judgment matrix It represents the importance scale of the i-th project sub-indicator to the j-th project sub-indicator; Calculate the geometric mean of each row of the judgment matrix and normalize the geometric mean of each row to obtain the second type weight coefficient of each target sub-indicator , , , ; Calculate the consistency ratio based on the judgment matrix and the second type of weight coefficient. If the consistency ratio is less than the preset threshold, the second type of weight coefficient takes effect. If the consistency ratio is greater than or equal to the preset threshold, the judgment matrix needs to be readjusted.

6. The method according to claim 1, characterized in that The carbon emission information chain of the blockchain is generated based on the carbon emission data and carbon emission performance value of each regional unit at the current moment, including: Packaging the carbon emission data of each regional unit at the current moment into a blockchain transaction, writing the blockchain transaction into the blockchain through a consensus mechanism, and generating a unique data identifier using a hash algorithm; Automatically verify through smart contracts whether the format of the carbon emission data of each regional unit meets the preset standards. If not, trigger a re-collection instruction to re-collect the carbon emission data of each regional unit; If they match, a data index of the carbon emission data of each regional unit at the current moment is constructed based on the data unique identifier and the Merkle tree; The carbon emission performance value is written into the blockchain and linked to the data index of the carbon emission data of each corresponding regional unit at the current moment through a smart contract to generate an auditable carbon emission information chain.

7. The method according to claim 2, characterized in that The predicting of the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data includes: Based on the historical carbon emission data, obtaining the carbon emission forecast data of each regional unit at the current moment through machine learning; According to the carbon emission prediction data of each of the regional units at the current moment, the carbon emission performance prediction value of each of the regional units at the current moment is calculated by formula (1).

8. A blockchain-based railway station carbon emission monitoring device, characterized in that: The device comprises: A division module is used to divide the railway station area into blocks according to the geographical location, functional layout and energy usage of the railway station area to obtain multiple independently monitored regional units; a calculation module, configured to obtain carbon emission data of each of the regional units at a current moment, and calculate the carbon emission performance value of each of the regional units at a current moment based on the carbon emission data; A generation module, configured to generate a carbon emission information chain of the blockchain based on the carbon emission data and carbon emission performance value of each of the regional units at the current moment; A prediction module, configured to obtain the carbon emission historical data of each of the regional units at all times and the carbon emission performance value at the current moment from the carbon emission information chain, and predict the carbon emission performance prediction value of each of the regional units at the current moment based on the carbon emission historical data; The monitoring module is used to determine if there is a target regional unit whose difference between the carbon emission performance value of the regional unit at the current moment and the carbon emission performance forecast value is greater than a preset threshold, and then send an early warning signal to the target regional unit.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is run on the processor, the method for monitoring carbon emissions of railway stations based on blockchain as described in any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that It stores a computer program, which, when running on a processor, executes the blockchain-based railway station carbon emission monitoring method according to any one of claims 1 to 7.

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