Carbon emission monitoring system and method based on road and bridge construction

Abstract

The invention discloses a carbon emission monitoring system and method based on road and bridge construction, relates to the technical field of green traffic infrastructure and carbon measurement, and solves the technical problem that the efficiency of a carbon emission monitoring system is relatively low due to the lack of an optimization scheme for predicting the output of carbon emission and after an excess condition occurs in the prior art. An actual carbon emission value is calculated according to stage data, material data, equipment data and environment data; calculating a plurality of prediction adjustment time windows according to the carbon emission data and the carbon emission actual value; generating a plurality of carbon emission estimated values according to the plurality of prediction adjustment time windows; generating a carbon optimization scheme according to the plurality of carbon emission estimated values; according to the method, adjustment is carried out according to the carbon optimization scheme, the error of the current stage is compared with the error range in the current time window, and the size of the prediction time window is dynamically adjusted, so that the carbon emission can be predicted more accurately, data support is provided for the carbon optimization scheme after the carbon emission exceeds the standard, and the accuracy and efficiency of a carbon emission monitoring system are improved.

Description

Technical Field

[0001] This application belongs to the field of green transportation infrastructure and carbon measurement technology, specifically a carbon emission monitoring system and method based on road and bridge construction. Background Art

[0002] Road and bridge construction refers to the construction of roads and bridges to improve traffic conditions and promote economic development. It includes the entire process from project approval, design, construction to completion acceptance, as well as subsequent maintenance and management. The scope of road and bridge construction is wide, involving not only the construction of roads and bridges, but also related tunnels, municipal facilities, etc. Carbon emissions refer to the process of carbon dioxide and other greenhouse gases entering the atmosphere during human activities. These greenhouse gases mainly come from the combustion of fossil fuels, such as coal, oil and natural gas, which are used for power generation, transportation, industrial production, etc. There is a close relationship between road and bridge construction and carbon emissions. As an important part of infrastructure construction, road and bridge construction will generate a large amount of carbon emissions during its construction and operation.

[0003] The prior art (invention patent application with publication number CN117007747A) discloses a carbon emission monitoring system and method during a highway construction period, which belongs to the technical field of transportation carbon emission monitoring, wherein the carbon emission monitoring system during a highway construction period includes a collection device, a transmission device, a data analysis device and a data storage device; the collection device is used to collect a first type of data in a preset area during the highway construction period, and the first type of data includes energy consumption metering data and exhaust emission data; the transmission device is communicatively connected to the collection device and the data analysis device, and is used to transmit the first type of data to the data analysis device; the data analysis device is used to analyze and calculate the first type of data to obtain first carbon emission data corresponding to the first type of data; the data storage device is communicatively connected to the data analysis device, and is used to store the first carbon emission data.

[0004] The above scheme calculates and sums the carbon emissions of each part during the highway construction period to obtain the final carbon emissions, thereby realizing the automated collection, accounting and analysis of carbon emissions; however, carbon emissions have already occurred, and if carbon emissions exceed the limit, it will lead to adverse consequences. The lack of foresight for carbon emissions, the lack of prediction of carbon emissions output and optimization plans after excess situations, makes the efficiency of the carbon emission monitoring system low; therefore, the carbon emission monitoring system still needs further improvement. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a carbon emission monitoring system and method based on road and bridge construction, which is used to solve the technical problem that the prior art lacks the ability to predict the output of carbon emissions and the optimization plan after excess occurs, resulting in low efficiency of the carbon emission monitoring system.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a carbon emission monitoring system based on road and bridge construction, including: a data acquisition module, a data analysis module, an early warning module and a database; The data acquisition module acquires stage data, material data, equipment data, environmental data and carbon emission data through data acquisition equipment; The data analysis module: calculates the actual value of carbon emissions according to stage data, material data, equipment data and environmental data; calculates a number of forecast adjustment time windows according to the carbon emission data and the actual value of carbon emissions; generates a number of carbon emission estimates according to the number of forecast adjustment time windows; generates a carbon optimization plan according to the number of carbon emission estimates; and makes adjustments according to the carbon optimization plan; The early warning module: gives a prompt according to the alarm signal and contacts the management personnel; The database is used to store data collected by the collection device and store historical data required for training the model.

[0007] The above steps of this application compare and analyze the emission error in the current stage with the error limit in the time window, and flexibly adjust the scale of the prediction time window based on this to improve the accuracy of carbon emission prediction; it provides a solid data foundation for formulating optimization strategies for responding to excessive carbon emissions, thereby enhancing the accuracy and operational efficiency of the carbon emission monitoring system.

[0008] Furthermore, the actual value of carbon emissions is calculated based on the stage data, material data, equipment data and environmental data, including: Acquire stage data, material data, equipment data and environmental data; the stage data includes stage ID and stage tag; the stage tag includes production tag, transportation tag and construction tag; the environmental data includes temperature, humidity, rainfall, average road slope and weather forecast data; the material data includes several material parameters; By formula Calculate the actual value of production carbon emissions STS; where i represents the material number, M represents the material mass, EF 材,i It is expressed as the carbon emission factor of material production when the i-th material is produced; and They are respectively expressed as the difference between the actual average temperature and the reference temperature during material production and the difference between the actual average humidity and the reference humidity during material production; α i and β iThey are expressed as temperature sensitivity coefficient and humidity sensitivity coefficient respectively; By formula Calculate the actual value of transportation carbon emissions YTS; where JL is the transportation distance, RX is the fuel efficiency of the transportation tool, γ is the rainfall influence coefficient, ε is the slope influence coefficient, γ and ε∈(0,1); is the road flatness and slope, with uphill being positive and downhill being negative; By formula Calculate the actual value of construction carbon emissions JTS; where m represents the number of power equipment during construction, n represents the number of fuel equipment, DL represents the power consumption, RL represents the fuel volume, COP (W m ) is the energy efficiency ratio of the equipment at temperature W; μ(P n ) is the air pressure correction factor; EF is the carbon emission factor.

[0009] Furthermore, the calculation of a plurality of forecast adjustment time windows according to the carbon emission data and the actual carbon emission value includes: Obtaining carbon emission data and a number of carbon emission actual values ​​TPS; the carbon emission data includes a number of carbon emission estimated values ​​TPY; By formula Calculate the emission error PW j,t ; Wherein, j represents the stage number and t represents the time number; Determine whether the emission error is within its corresponding error range; the error range is calculated based on historical emission errors; Yes, do nothing; No, generate several forecast adjustment time windows based on emissions errors.

[0010] Furthermore, the error range is calculated by historical emission errors, including: Obtaining historical emission errors corresponding to several stage labels; the historical emission errors are several historical emission errors within the current time window; Arrange several historical emission errors in the current time window from small to large to obtain the error sequence WL j ; Get WL j The median of Med j and interquartile range IQR j ; Error range WF j WF j ∈[Med j -IQR j , Med j +IQR j ].

[0011] Through the above steps, this application calculates a series of historical emission errors in the current time window to determine an adaptive error range, wherein the time window itself has the characteristic of dynamic adjustment; so that the error range can flexibly adapt to different situations, thereby providing a more accurate error definition, and providing a solid data foundation for the adaptive adjustment of the time window in the prediction process.

[0012] Furthermore, the generating of a plurality of prediction adjustment time windows according to the emission error includes: Get the emission error PW j,t and error margin WF j ; The error range includes the error range boundary value WFB j ; By formula Calculate the forecast adjustment time window SC j,t+1 ; Where σ represents the adjustment coefficient, σ∈(0,1); SC j,max and SC j,min They are respectively represented as the maximum and minimum values ​​of the j-th stage time window; Indicated as rounded up symbol; when the emission error is greater than the maximum value in its corresponding error range, WFB j The value of is equal to the maximum value in the error range. When the emission error is less than the minimum value in the corresponding error range, WFB j The value is equal to the minimum value in the error range.

[0013] Furthermore, the generating of a plurality of carbon emission estimates according to a plurality of forecast adjustment time windows includes: Acquire a number of forecast sequences and stage labels; the forecast sequences include material data, equipment data and environmental data within a number of forecast adjustment time windows and their corresponding actual carbon emission values; Several prediction sequences and stage labels are input into a carbon emission estimation model to obtain carbon emission estimation values ​​corresponding to the stage labels; the carbon emission estimation model is constructed through a machine learning model.

[0014] Furthermore, the carbon emission estimation model is constructed through a machine learning model, including: Obtain several historical forecast sequences, historical stage labels and their corresponding historical carbon emission actual values; Divide several historical prediction sequences, historical stage labels and their corresponding historical carbon emission actual values ​​into training sets, test sets and validation sets; Select a machine learning model as the base model; Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtain a carbon emission estimation model whose input is a number of prediction sequences and stage labels, and whose output is the carbon emission estimation value corresponding to the stage label.

[0015] Furthermore, the generating of a carbon optimization scheme according to a number of carbon emission estimates includes: Obtain several carbon emission estimates; Determine whether certain carbon emission estimates are greater than their corresponding stage thresholds; If yes, generate a carbon emission exceeding standard alarm signal; obtain a stage label corresponding to the carbon emission estimate; and obtain a carbon optimization plan according to a rule base corresponding to the stage label; No, do nothing.

[0016] Furthermore, obtaining the carbon optimization solution according to the rule base corresponding to the stage label includes the following steps: Step 1: Obtain the stage label corresponding to the carbon emission estimate and the rule base corresponding to the stage label; Step 2: Create a temporary rule base for the stage; the temporary rule base for the stage is consistent with the rule base corresponding to the stage label; Step 3: Searching for several carbon optimization methods in the stage temporary rule base for the material data and equipment data corresponding to the stage label; deleting the several carbon optimization methods and their corresponding rule priorities from the stage temporary rule base; Step 4: Extract the carbon optimization methods corresponding to the highest rule priority values ​​in the rule base in turn; generate an optimized estimated value for the carbon optimization method through the carbon emission estimation model corresponding to the stage label; delete the carbon optimization method and its corresponding rule priority from the stage temporary rule base; determine whether the optimized estimated value is less than the stage threshold; if yes, organize several of the carbon optimization methods into a carbon optimization plan; if no, determine whether the stage temporary rule base is empty, if yes, generate an alarm signal that there is no optimization plan; if no, proceed to step 4.

[0017] Another aspect of the present invention provides a carbon emission monitoring method based on road and bridge construction, comprising: S0: Acquire phase data, material data, equipment data, environmental data and carbon emission data; S1: Calculate the actual value of carbon emissions based on stage data, material data, equipment data and environmental data; S2: Calculate a number of forecast adjustment time windows based on carbon emission data and actual carbon emission values; generate a number of carbon emission estimates based on the number of forecast adjustment time windows; S3: Generate a carbon optimization plan based on a number of carbon emission estimates; and make adjustments based on the carbon optimization plan; S4: Provide prompts based on alarm signals and contact management.

[0018] Compared with the prior art, the beneficial effects of this application are: 1. This application calculates the actual value of carbon emissions based on stage data, material data, equipment data and environmental data; calculates several forecast adjustment time windows based on carbon emission data and the actual value of carbon emissions; generates several carbon emission estimates based on several forecast adjustment time windows; generates a carbon optimization plan based on several carbon emission estimates; makes adjustments based on the carbon optimization plan, compares the error of the current stage with the error range in the current time window, and dynamically adjusts the size of the forecast time window to more accurately predict carbon emissions, provide data support for the carbon optimization plan after carbon emissions exceed the standard, and improve the accuracy and efficiency of the carbon emission monitoring system.

[0019] 2. This application divides the carbon emissions of road and bridge construction into stages, calculates the carbon emissions in each stage through different parameters, and considers the impact of environmental factors on carbon emissions, so that the actual value of carbon emissions in each stage can be more accurate, thereby improving the accuracy of the actual value of carbon emissions.

[0020] 3. This application calculates the error range by using several historical emission errors within the current time window. At the same time, the time window also changes dynamically, making the error range adaptive and able to provide a more accurate error range, providing accurate data support for predicting the adaptive changes of the adjustment time window. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is a schematic diagram of the carbon emission monitoring system based on road and bridge construction in this application; Figure 2 Generate a flow chart for the carbon optimization scheme of this application; Figure 3 This is a flow chart of the carbon emission monitoring method based on road and bridge construction in this application. DETAILED DESCRIPTION

[0023] The technical solution of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0024] See also Figure 1 , the first aspect of the present application provides a carbon emission monitoring system based on road and bridge construction, including: a data acquisition module, a data analysis module, an early warning module and a database; Data acquisition module: acquires stage data, material data, equipment data, environmental data and carbon emission data through data acquisition equipment; the data acquisition equipment includes several sensors, etc.; Data analysis module: Calculate the actual value of carbon emissions based on stage data, material data, equipment data and environmental data. The actual value of carbon emissions refers to the actual value of carbon emissions generated in each stage; calculate several forecast adjustment time windows based on carbon emission data and actual value of carbon emissions; the forecast adjustment time window refers to the range of forecast sequences required for carbon emission forecasting in each stage; generate several carbon emission estimates based on several forecast adjustment time windows. The carbon emission estimate refers to the predicted value of carbon emissions; generate carbon optimization plans based on several carbon emission estimates. The carbon optimization plan refers to the optimization plan that needs to be made when the carbon emission estimate exceeds the set stage threshold; make adjustments based on the carbon optimization plan; Early warning module: Prompts are given based on alarm signals and management personnel are contacted; alarm signals include alarm signals for no optimization solutions and alarm signals for excessive carbon emissions, etc.; The database is used to store the data collected by the acquisition equipment and the historical data required for training the model.

[0025] In this embodiment, the actual value of carbon emissions is calculated based on the stage data, material data, equipment data and environmental data, including: Acquire stage data, material data, equipment data and environmental data; stage data includes stage ID and stage label; stage label includes production label, transportation label and construction label, etc.; environmental data includes temperature, humidity, rainfall, average road slope and weather forecast data; material data includes several material parameters; By formula Calculate the actual value of production carbon emissions STS; where i represents the material number, M represents the material mass, EF 材,i It is represented as the material production carbon emission factor when the i-th material is produced. The specific value is set according to the material parameters. The material production carbon emission factors of different materials are different. and They are respectively represented by the difference between the actual average temperature and the reference temperature during material production, and the difference between the actual average humidity and the reference humidity during material production. The reference temperature and reference humidity are set based on experience. In this embodiment, the reference temperature and reference humidity are set to 25°C and 60% respectively; α i and β iThey are respectively expressed as temperature sensitivity coefficient and humidity sensitivity coefficient. The temperature sensitivity coefficient refers to the change ratio of the carbon emission factor when the actual average temperature deviates from the reference temperature by 1°C; the humidity sensitivity coefficient refers to the change ratio of the carbon emission factor when the actual average humidity deviates from the reference humidity by 1%. The temperature sensitivity coefficient and humidity sensitivity coefficient are set according to experience. The temperature sensitivity coefficient and humidity sensitivity coefficient in the production process of different materials are different. In this embodiment, the temperature sensitivity coefficient in the production of steel materials is set to 0.003, and the temperature sensitivity coefficient in the production of concrete materials is set to 0.005. When the types of materials produced are fixed, as the mass of the produced materials increases and the deviations of temperature and humidity increase, the actual value of production carbon emissions will also increase accordingly. By formula Calculate the actual value of transportation carbon emissions YTS; where JL is the transportation distance, RX is the fuel efficiency of the transportation tool, and the fuel efficiency corresponding to different transportation tools is different. The specific value is set according to experience. In this embodiment, the fuel efficiency of the transportation tool is set to 0.25km / L, that is, the fuel efficiency of the transportation tool of a fuel truck; γ is the rainfall influence coefficient, ε is the slope influence coefficient, γ and ε∈(0,1), and the specific value is set according to experience. When driving on roads with different slopes and different rainfall amounts, the amount of fuel required by the transportation tool is different, and the impact on the carbon emissions caused is also different. In this embodiment, ε is set to 0.12; is the road flatness and slope, with uphill being positive and downhill being negative. The greater the rainfall, the longer the transportation distance, and the uphill state of the transportation section, the actual value of transportation carbon emissions will increase accordingly; By formula Calculate the actual value of construction carbon emissions JTS; where m represents the number of power equipment during construction, n represents the number of fuel equipment, DL represents the power consumption, RL represents the fuel volume, COP (W m ) is expressed as the energy efficiency ratio of the device at temperature W, for example, when the temperature is 25°C, COP=1; μ(P n ) is expressed as the air pressure correction coefficient, which is the influence coefficient of altitude pressure on fuel efficiency; EF is expressed as the carbon emission factor. The carbon emission factors corresponding to different forms of energy are inconsistent, and the specific values ​​are set according to experience; on the basis of the energy efficiency ratio and altitude pressure remaining unchanged, as the electricity consumption and fuel amount increase, the carbon content emitted through the energy will increase, and therefore the actual value of carbon emissions will increase accordingly.

[0026] This embodiment implements a phased segmentation of carbon emissions during the road and bridge construction process, and uses differentiated parameters for detailed carbon emissions calculations at each stage; in this process, the potential impact of environmental factors on carbon emissions is fully incorporated, thereby ensuring the high accuracy of the actual values ​​of carbon emissions at each stage and significantly improving the accuracy of the overall carbon emissions assessment.

[0027] In this embodiment, several prediction adjustment time windows are calculated based on the carbon emission data and the actual carbon emission value, including: Obtain carbon emission data and several actual carbon emission values ​​TPS; carbon emission data includes several estimated carbon emission values ​​TPY; By formula Calculate the emission error PW j,t ; Where j represents the stage number and t represents the time number; the greater the gap between the actual carbon emission value and the estimated carbon emission value, the greater the emission error; Determine whether the emission error is within its corresponding error range; the error range is calculated based on historical emission errors; Yes, do nothing; that is, the emission error can be ignored, and no change is needed to the prediction time window; No, generate several forecast adjustment time windows based on emissions errors.

[0028] The error range in this embodiment is calculated by historical emission errors, including: Get the historical emission errors corresponding to several stage labels; the historical emission errors are several historical emission errors within the current time window; the number of historical sequences within the current time window is equal to the number of historical emission errors; Arrange several historical emission errors in the current time window from small to large to obtain the error sequence WL j ; Error sequence WL j It is represented as the error sequence corresponding to the jth stage; Get WL j The median Med j and interquartile range IQR j ;Median j The calculation of can be obtained as follows: If the error sequence has an odd number of emission errors, the median Med j is equivalent to the emission error corresponding to the middle value of the error sequence; if the error sequence has an even number of emission errors, the median Med j Equivalent to half the sum of the two emission errors in the middle of the error sequence; interquartile range IQR j It can be calculated by a method based on linear interpolation; Error range WF j WF j ∈[Med j -IQR j , Med j +IQR j ].

[0029] In this embodiment, a number of prediction adjustment time windows are generated according to the emission error, including: Get the emission error PW j,t and error margin WF j ; The error range includes the error range boundary value WFB j ; By formula Calculate the forecast adjustment time window SC j,t+1 ; Among them, σ represents the adjustment coefficient, represents the sensitivity of the control adjustment, σ∈(0,1), and the specific value is set according to experience; SC j,max and SC j,min They are respectively represented as the maximum and minimum values ​​of the j-th stage time window. The specific values ​​are set according to experience. In this embodiment, the SC of the production stage is j,max and SC j,min Set to 72 days and 6 days respectively; It is expressed as a round-up symbol. The round-up symbol is used to make the prediction adjustment time window an integer, which is convenient for subsequent processes. When the emission error is greater than the maximum value in its corresponding error range, WFB j The value of is equal to the maximum value in the error range. When the emission error is less than the minimum value in the corresponding error range, WFB j The value is equal to the minimum value in the error range; as the emission error becomes larger than the maximum value in the error range, it means that the historical carbon emission estimate corresponding to the current actual carbon emission value is not accurate enough, and more historical sequences are needed for prediction, so the prediction adjustment time window will increase accordingly.

[0030] Through the above steps, this embodiment dynamically generates a prediction adjustment time window based on the degree of deviation of the current emission error within the error range, so that the prediction effect for future moments can be more accurate. At the same time, it ensures that the prediction adjustment time window can be adjusted within a certain range to avoid the problem of low prediction efficiency caused by too large or too small time window, thereby improving the accuracy and efficiency of the prediction.

[0031] In this embodiment, a number of carbon emission estimates are generated according to a number of prediction adjustment time windows, including: Obtaining several forecast sequences and stage labels; the forecast sequences include material data, equipment data and environmental data within several forecast adjustment time windows and their corresponding actual carbon emission values; Several prediction sequences and stage labels are input into the carbon emission estimation model to obtain the carbon emission estimation values ​​corresponding to the stage labels; the carbon emission estimation model is constructed through a machine learning model.

[0032] The carbon emission estimation model in this embodiment is constructed through a machine learning model, including: Obtain several historical forecast sequences, historical stage labels and their corresponding historical carbon emission actual values; Several historical prediction sequences, historical stage labels and their corresponding historical carbon emission actual values ​​are divided into training set, test set and validation set; the ratio between training set, test set and validation set is 7:2:1; Select a machine learning model as the basic model; machine learning models include LSTM models, etc. Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtain a carbon emission estimation model whose input is a number of prediction sequences and stage labels, and whose output is the carbon emission estimation value corresponding to the stage label.

[0033] In this embodiment, a carbon optimization scheme is generated based on several carbon emission estimates, including: Obtain several carbon emission estimates; Determine whether a number of carbon emission estimates are greater than their corresponding stage thresholds; the stage thresholds are set based on experience and vary from stage to stage; Yes, generate an alarm signal for excessive carbon emissions; obtain the stage label corresponding to the estimated carbon emissions; obtain the carbon optimization plan based on the rule base corresponding to the stage label; the rule base is constructed by experts based on low-carbon methods; No, do nothing.

[0034] See also Figure 2 In this embodiment, obtaining a carbon optimization solution according to a rule base corresponding to the stage label includes the following steps: Step 1: Obtain the stage label corresponding to the carbon emission estimate and the rule base corresponding to the stage label; Step 2: Create a temporary rule base for the stage; the temporary rule base for the stage is consistent with the rule base corresponding to the stage label; the purpose of creating a temporary rule base for the stage is to preserve the integrity of the original rule base and prevent incomplete data from occurring when operating the rule base; Step 3: Search for several carbon optimization methods in the temporary rule base of the stage for the material data and equipment data corresponding to the stage label; delete several carbon optimization methods and their corresponding rule priorities from the temporary rule base of the stage; that is, the currently implemented scheme may have used some carbon optimization methods in the rule base, and removing the used carbon optimization methods can reduce the number of repeated uses and improve the generation efficiency of carbon optimization schemes; Step 4: Extract the carbon optimization methods corresponding to the highest rule priority in the rule base in turn; generate optimized estimated values ​​for the carbon optimization methods through the carbon emission estimation model corresponding to the stage label; delete the carbon optimization methods and their corresponding rule priorities from the stage temporary rule base; determine whether the optimized estimated value is less than the stage threshold; if so, organize several carbon optimization methods into a carbon optimization plan; if not, determine whether the stage temporary rule base is empty, if so, generate an alarm signal that there is no optimization plan yet; if not, proceed to step 4; when the stage temporary rule base is empty, it means that all existing carbon optimization methods cannot adjust the carbon emission estimate corresponding to the current stage to the target state.

[0035] After the predicted carbon emission estimate exceeds the stage threshold, this embodiment searches for a carbon optimization method according to the rule priority in a preset rule base, and selects the method corresponding to the highest rule priority value for estimation in turn until the carbon emission estimate can meet the target requirements. This can respond in real time, meet the dynamic adjustment of the project status, and improve the efficiency of the carbon emission monitoring system.

[0036] See also Figure 3 Another aspect of the present application provides a carbon emission monitoring method based on road and bridge construction, including: S0: Acquire phase data, material data, equipment data, environmental data and carbon emission data; S1: Calculate the actual value of carbon emissions based on stage data, material data, equipment data and environmental data; S2: Calculate a number of forecast adjustment time windows based on carbon emission data and actual carbon emission values; generate a number of carbon emission estimates based on the number of forecast adjustment time windows; S3: Generate a carbon optimization plan based on a number of carbon emission estimates; and make adjustments based on the carbon optimization plan; S4: Provide prompts based on alarm signals and contact management.

[0037] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0038] The working principle of this application is as follows: by acquiring stage data, material data, equipment data, environmental data and carbon emission data; calculating the actual value of carbon emissions based on the stage data, material data, equipment data and environmental data; calculating several forecast adjustment time windows based on the carbon emission data and the actual value of carbon emissions; generating several carbon emission estimates based on several forecast adjustment time windows; generating a carbon optimization plan based on several carbon emission estimates; making adjustments based on the carbon optimization plan; making prompts based on alarm signals and contacting management personnel to compare the error of the current stage with the error range in the current time window, and dynamically adjusting the size of the forecast time window to more accurately predict carbon emissions, provide data support for the carbon optimization plan after carbon emissions exceed the standard, improve the accuracy and efficiency of the carbon emission monitoring system, and avoid the problem of low efficiency of the carbon emission monitoring system due to the lack of technology to predict the output of carbon emissions and the optimization plan after the excess occurs.

[0039] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. The carbon emission monitoring system based on road and bridge construction is characterized by: include: Data collection module, data analysis module, early warning module and database; The data acquisition module acquires stage data, material data, equipment data, environmental data and carbon emission data through data acquisition equipment; The data analysis module: calculates the actual value of carbon emissions based on stage data, material data, equipment data and environmental data; calculates a number of forecast adjustment time windows based on the carbon emission data and the actual value of carbon emissions; generates a number of carbon emission estimates based on the number of forecast adjustment time windows; generates a carbon optimization plan based on the number of carbon emission estimates; and makes adjustments based on the carbon optimization plan.

2. The carbon emission monitoring system based on road and bridge construction according to claim 1 is characterized in that: The actual value of carbon emissions is calculated based on stage data, material data, equipment data and environmental data, including: Acquire stage data, material data, equipment data and environmental data; the stage data includes stage ID and stage tag; the stage tag includes production tag, transportation tag and construction tag; the environmental data includes temperature, humidity, rainfall, average road slope and weather forecast data; the material data includes several material parameters; By formula Calculate the actual value of production carbon emissions STS; where i represents the material number, M represents the material mass, EF 材,i It is expressed as the carbon emission factor of material production when the i-th material is produced; and They are respectively expressed as the difference between the actual average temperature and the reference temperature during material production and the difference between the actual average humidity and the reference humidity during material production; α i and β i They are expressed as temperature sensitivity coefficient and humidity sensitivity coefficient respectively; By formula Calculate the actual value of transportation carbon emissions YTS; where JL is the transportation distance, RX is the fuel efficiency of the transportation tool, γ is the rainfall influence coefficient, ε is the slope influence coefficient, γ and ε∈(0,1); is the road flatness and slope, uphill is positive and downhill is negative; By formula Calculate the actual value of construction carbon emissions JTS; where m represents the number of power equipment during construction, n represents the number of fuel equipment, DL represents the power consumption, RL represents the fuel volume, COP (W m ) is the energy efficiency ratio of the equipment at temperature W; μ(P n ) is the air pressure correction factor; EF is the carbon emission factor.

3. The carbon emission monitoring system based on road and bridge construction according to claim 1 is characterized in that: The calculation of a plurality of forecast adjustment time windows according to the carbon emission data and the actual carbon emission value includes: Obtaining carbon emission data and a number of carbon emission actual values ​​TPS; the carbon emission data includes a number of carbon emission estimated values ​​TPY; By formula Calculate the emission error PW j,t ; Wherein, j represents the stage number and t represents the time number; Determine whether the emission error is within its corresponding error range; the error range is calculated based on historical emission errors; Yes, do nothing; No, generate several forecast adjustment time windows based on emissions errors.

4. The carbon emission monitoring system based on road and bridge construction according to claim 3 is characterized in that: The error range is calculated using historical emissions errors, including: Obtaining historical emission errors corresponding to several stage labels; the historical emission errors are several historical emission errors within the current time window; Arrange several historical emission errors in the current time window from small to large to obtain the error sequence WL j ; Get WL j The median of Med j and interquartile range IQR j ; Error range WF j WF j ∈[Med j -IQR j , Med j +IQR j ].

5. The carbon emission monitoring system based on road and bridge construction according to claim 3 is characterized in that: The generating of a plurality of prediction adjustment time windows according to the emission error comprises: Get the emission error PW j,t and error margin WF j ; The error range includes the error range boundary value WFB j ; By formula Calculate the forecast adjustment time window SC j,t+1 ; Where σ represents the adjustment coefficient, σ∈(0,1); SC j,max and SC j,min They are respectively represented as the maximum and minimum values ​​of the j-th stage time window; Indicated as rounded up symbol; when the emission error is greater than the maximum value in its corresponding error range, WFB j The value of is equal to the maximum value in the error range. When the emission error is less than the minimum value in the corresponding error range, WFB j The value is equal to the minimum value in the error range.

6. The carbon emission monitoring system based on road and bridge construction according to claim 1 is characterized in that: The generating of a plurality of carbon emission estimates according to a plurality of forecast adjustment time windows comprises: Acquire a number of forecast sequences and stage labels; the forecast sequences include material data, equipment data and environmental data within a number of forecast adjustment time windows and their corresponding actual carbon emission values; Several prediction sequences and stage labels are input into a carbon emission estimation model to obtain carbon emission estimation values ​​corresponding to the stage labels; the carbon emission estimation model is constructed through a machine learning model.

7. The carbon emission monitoring system based on road and bridge construction according to claim 6 is characterized in that: The carbon emission estimation model is constructed through a machine learning model, including: Obtain several historical forecast sequences, historical stage labels and their corresponding historical carbon emission actual values; Divide several historical prediction sequences, historical stage labels and their corresponding historical carbon emission actual values ​​into training sets, test sets and validation sets; Select a machine learning model as the base model; Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtain a carbon emission estimation model whose input is a number of prediction sequences and stage labels, and whose output is the carbon emission estimation value corresponding to the stage label.

8. The carbon emission monitoring system based on road and bridge construction according to claim 1 is characterized in that: The generating of a carbon optimization scheme based on a number of carbon emission estimates includes: Obtain several carbon emission estimates; Determine whether certain carbon emission estimates are greater than their corresponding stage thresholds; If yes, generate a carbon emission exceeding standard alarm signal; obtain a stage label corresponding to the carbon emission estimate; and obtain a carbon optimization plan according to a rule base corresponding to the stage label; No, do nothing.

9. The carbon emission monitoring system based on road and bridge construction according to claim 8 is characterized in that: The step of obtaining a carbon optimization solution according to a rule base corresponding to the stage label includes the following steps: Step 1: Obtain the stage label corresponding to the carbon emission estimate and the rule base corresponding to the stage label; Step 2: Create a temporary rule base for the stage; the temporary rule base for the stage is consistent with the rule base corresponding to the stage label; Step 3: Searching for several carbon optimization methods in the stage temporary rule base for the material data and equipment data corresponding to the stage label; deleting the several carbon optimization methods and their corresponding rule priorities from the stage temporary rule base; Step 4: Extract the carbon optimization methods corresponding to the highest rule priority values ​​in the rule base in turn; generate an optimized estimated value for the carbon optimization method through the carbon emission estimation model corresponding to the stage label; delete the carbon optimization method and its corresponding rule priority from the stage temporary rule base; determine whether the optimized estimated value is less than the stage threshold; if yes, organize several of the carbon optimization methods into a carbon optimization plan; if no, determine whether the stage temporary rule base is empty, if yes, generate an alarm signal that there is no optimization plan; if no, proceed to step 4.

10. A carbon emission monitoring method based on road and bridge construction, applied to a carbon emission monitoring system based on road and bridge construction as claimed in any one of claims 1 to 9, characterized in that: include: S0: Acquire phase data, material data, equipment data, environmental data and carbon emission data; S1: Calculate the actual value of carbon emissions based on stage data, material data, equipment data and environmental data; S2: Calculate a number of forecast adjustment time windows based on carbon emission data and actual carbon emission values; generate a number of carbon emission estimates based on the number of forecast adjustment time windows; S3: Generate a carbon optimization plan based on a number of carbon emission estimates; and make adjustments based on the carbon optimization plan.

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