A road engineering carbon emissions analysis system based on the Internet of Things

Abstract

The present invention discloses a road engineering carbon emission analysis system based on the Internet of Things, and relates to the field of environmental monitoring technology. The road engineering carbon emission analysis system based on the Internet of Things includes: an initial data acquisition module, a real-time data acquisition module and a carbon emission assessment module. The present invention monitors the operating status of road engineering equipment in real time, and automatically takes a corresponding first data acquisition measure according to the operating status. Then, when the road engineering equipment is in the operating state, the carbon emission prediction index is obtained by collecting energy consumption data and mechanical characteristic data of the road engineering equipment within a preset time period to take a corresponding second data acquisition measure. Finally, the carbon emission assessment index is obtained by obtaining the emission data and energy consumption data to adjust the second data acquisition measure, thereby improving the accuracy of the carbon emission analysis of the road engineering, and solving the problem of inaccurate carbon emission analysis in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a road engineering carbon emission analysis system based on the Internet of Things. Background Art

[0002] As global climate change and environmental issues become increasingly severe, the monitoring and control of carbon emissions has become an important issue for all industries to achieve sustainable development. As a key area of ​​energy consumption and carbon emissions, road engineering involves a large amount of mechanical operations, material transportation and energy consumption during its construction process, and it is in urgent need of efficient carbon emission monitoring and management methods. The rapid development of Internet of Things technology has provided intelligent solutions for the road engineering field. By collecting energy consumption and carbon emission data in real time, combined with big data analysis and artificial intelligence optimization, accurate monitoring of carbon emissions throughout the process and optimization of green construction plans can be achieved.

[0003] Existing road engineering carbon emission monitoring technology mainly relies on manual statistics or independent equipment measurement. The data collection process is cumbersome and lacks real-time performance, making it difficult to meet the needs of refined management. At the same time, traditional carbon emission estimates are mostly based on fixed formulas or empirical data, which makes it difficult to accurately reflect the dynamic changes in complex construction scenarios. In addition, the data island problem between different equipment and processes is serious, resulting in a lack of integrity in carbon emission analysis. In recent years, with the development of the Internet of Things, big data, and artificial intelligence technologies, integrated and intelligent carbon emission monitoring solutions have gradually emerged, but most of them are still limited to single functions or small-scale experiments and have not been widely used in complex road engineering construction scenarios.

[0004] For example, the patent application with publication number: CN115524451A discloses a carbon emission monitoring and management method and equipment for high-energy-consuming enterprises based on the Internet of Things, including: a monitoring control box, on which a fixed carbon emission monitoring structure is connected and installed, the fixed carbon emission monitoring structure is connected to the monitoring control box through a pair of connecting wire pipes, and the monitoring control box is equipped with an Internet of Things data conversion and transmission structure.

[0005] For example, the invention patent with announcement number: CN118465196B discloses a system for monitoring vehicle carbon emissions on highways, including: a monitoring point deployment module, a meteorological monitoring module, a data processing module, a correction algorithm module and a data management module; the monitoring point deployment module is used to deploy detection sites along the highway, and each site is equipped with a carbon emission sensor.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, due to the loss of key emission data, that is, insufficient dynamic operating condition data, there is a problem of inaccurate carbon emission analysis of road projects. Summary of the invention

[0008] The embodiment of the present application solves the problem of inaccurate carbon emission analysis in the prior art by providing a road engineering carbon emission analysis system based on the Internet of Things, thereby improving the accuracy of carbon emission analysis of road engineering.

[0009] The embodiment of the present application provides a road engineering carbon emission analysis system based on the Internet of Things, including: an initial data acquisition module, a real-time data acquisition module and a carbon emission assessment module: wherein the initial data acquisition module is used to monitor the operating status of road engineering equipment in real time through industrial tools, and automatically take corresponding first data acquisition measures according to the operating status, and the first data acquisition measures include high-frequency acquisition, medium-frequency acquisition, low-frequency acquisition and ultra-low-frequency acquisition; the real-time data acquisition module is used to obtain a carbon emission prediction index by collecting energy consumption data and mechanical property data of the road engineering equipment within a preset time period when the road engineering equipment is in operation, and take corresponding second data acquisition measures according to the carbon emission prediction index, and the carbon emission prediction index is used to predict the road engineering equipment The degree of change of carbon emissions during operation; the carbon emission assessment module is used to obtain the carbon emission assessment index by acquiring the emission data and energy consumption data within a preset time period, and adjust the second data collection measure based on the carbon emission assessment index. The carbon emission assessment index is used to measure the difference between the actual carbon emissions and the predicted carbon emissions; the energy consumption data includes fuel flow, electricity consumption and heat release; the mechanical property data includes vibration frequency, noise intensity and equipment surface temperature; the emission data includes carbon emission concentration, particulate matter concentration, exhaust gas temperature and volatile organic compound concentration; the carbon emission concentration represents the emission concentration of carbon dioxide; the particulate matter concentration represents the instantaneous concentration of PM2.5; the exhaust gas temperature represents the exhaust gas temperature after fuel combustion, which is used to reflect the combustion efficiency.

[0010] Furthermore, the specific process of automatically taking the corresponding first data collection measure according to the operating status is as follows: if the operating status is startup, the first data collection measure automatically taken is high-frequency collection; if the operating status is running, the first data collection measure automatically taken is medium-frequency collection; if the operating status is standby, the first data collection measure automatically taken is low-frequency collection; if the operating status is shutdown, the first data collection measure automatically taken is ultra-low frequency collection.

[0011] Furthermore, the specific acquisition process of the carbon emission prediction index is as follows: energy consumption data and mechanical characteristic data are acquired, and data preprocessing is performed on the energy consumption data and mechanical characteristic data, wherein the data preprocessing includes data cleaning and data standardization; energy consumption weights and mechanical weights are acquired from a preset database, wherein the energy consumption weights include frequency weights, noise weights and temperature weights, and the mechanical weights include fuel weights, electric energy weights and thermal energy weights; preset time periods are numbered, and the energy consumption data, mechanical characteristic data, energy consumption weights and mechanical weights are processed to obtain the carbon emission prediction index;

[0012] The specific limiting expression of the carbon emission prediction index is as follows:

[0013]

[0014] Where n is the number of the preset time period. , Indicates the total number of preset time periods. Indicates the vibration frequency of the road engineering equipment in the nth preset time period, Indicates the noise intensity of road engineering equipment in the nth preset time period, Indicates the equipment surface temperature of the road engineering equipment within the nth preset time period, represents the fuel flow of the road engineering equipment during the nth preset time period, Indicates the power consumption of road engineering equipment in the nth preset time period, Indicates the heat energy release of road engineering equipment in the nth preset time period, represents the frequency weight, represents the noise weight, represents the temperature weight, represents the fuel weight, represents the electric energy weight, represents the thermal energy weight, Represents the carbon emission forecast index of road engineering equipment in the nth preset time period.

[0015] Furthermore, the specific steps of taking the corresponding second data collection measure according to the carbon emission prediction index are as follows: obtaining the prediction evaluation interval from a preset database, the prediction evaluation interval including the first prediction evaluation interval, the second prediction evaluation interval and the third prediction evaluation interval; if the carbon emission prediction index is within the first prediction evaluation interval, the second data collection measure taken is high-frequency collection; if the carbon emission prediction index is within the second prediction evaluation interval, the second data collection measure taken is medium-frequency collection; if the carbon emission prediction index is within the third prediction evaluation interval, the second data collection measure taken is low-frequency collection.

[0016] Furthermore, the specific acquisition process of the carbon emission assessment index is as follows: energy consumption data and emission data are acquired, and data preprocessing is performed on the energy consumption data and emission data, and the carbon emission factor is acquired from a preset database; the result of the ratio operation of the emission data processing result and the energy consumption data processing result is processed to obtain the carbon emission assessment index; the emission data processing result represents the value of the mean operation of the sum of the results of the hyperbolic tangent processing of the emission data; the energy consumption data processing result represents the product of the value of the mean operation of the sum of the results of the hyperbolic tangent processing of the energy consumption data and the carbon emission factor.

[0017] Furthermore, the specific steps for obtaining the carbon emission factor are as follows: Step 1, dividing the obtained test data into first test data and second test data according to a preset ratio, and the test data includes energy consumption data and corresponding emission data; Step 2, substituting the first test data into the linear regression equation to obtain an initial model of the carbon emission factor, and the initial model of the carbon emission factor is used to establish a linear relationship between the emission data and the energy consumption data to obtain a first emission; Step 3, obtaining the first emission according to the established initial model of the carbon emission factor, and drawing an emission image through the first emission and the second emission, and obtaining the carbon emission factor according to the drawn emission image, and the emission image is used to visualize the changing trend between the first emission and the second emission; Step 4, substituting the second test data into the initial model of the carbon emission factor to obtain the predicted emissions, and deriving the mean square error based on the predicted emissions and the second emissions. If the obtained mean square error is within the reference error range, the current carbon emission factor is adopted, otherwise the steps are repeated until the obtained mean square error is within the reference error range.

[0018] Furthermore, the specific steps of regulating the second data collection measure based on the carbon emission assessment index are as follows: obtaining an assessment threshold from a preset database, and the assessment threshold is used to determine the degree of consistency between the carbon emission forecast and the actual carbon emission; if the carbon emission assessment index is not less than the assessment threshold, the second data collection measure is not regulated; if the carbon emission assessment index is less than the assessment threshold, the second data collection measure is regulated; regulating the second data collection measure means increasing the data collection frequency corresponding to the second data collection measure by one level.

[0019] Furthermore, a road engineering carbon emission analysis system based on the Internet of Things is characterized by: it also includes: if the carbon emission evaluation index after adjusting the second data collection measure within the next preset time period is less than the evaluation threshold, then stop adjusting the second data collection measure and obtain equipment operation data; combine the acquired equipment operation data with the mechanical characteristic data to obtain an equipment condition evaluation index, and judge whether to perform equipment maintenance according to the equipment condition evaluation index, the equipment condition evaluation index is used to evaluate the operation compliance of road engineering equipment; the method for obtaining the equipment condition evaluation index is as follows: obtain the vibration frequency in the mechanical characteristic data and the equipment operation data, the equipment operation data includes operating power and equipment load; obtain reference data from a preset database, the reference data includes reference vibration frequency, reference operating power and reference equipment load; obtain the equipment condition evaluation index by processing the absolute deviation of the vibration frequency in the mechanical characteristic data, the equipment operation data and the corresponding reference data respectively.

[0020] Furthermore, the specific process of determining whether to perform equipment maintenance based on the equipment condition assessment index is as follows: a maintenance threshold is obtained from a preset database, and the maintenance threshold is used to determine whether to perform equipment maintenance; if the equipment condition assessment index is not less than the maintenance threshold, equipment maintenance is not performed; if the equipment condition assessment index is less than the maintenance threshold, equipment maintenance is performed; the equipment maintenance includes fault diagnosis, calibration adjustment, and recording of maintenance logs.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. By real-time monitoring of the operating status of road engineering equipment, and automatically taking corresponding first data collection measures according to the operating status, then when the road engineering equipment is in operation, by collecting energy consumption data and mechanical characteristic data of the road engineering equipment within a preset time period, a carbon emission prediction index is obtained to take corresponding second data collection measures, and finally a carbon emission assessment index is obtained through the obtained emission data and energy consumption data to regulate the second data collection measures, thereby improving the efficiency of data collection, and further achieving the improvement of the accuracy of carbon emission analysis of road engineering, and effectively solving the problem of inaccurate carbon emission analysis of road engineering in the prior art.

[0023] 2. By obtaining energy consumption data and mechanical characteristic data, and preprocessing the energy consumption data and mechanical characteristic data, energy consumption weights and mechanical weights are obtained from the preset database, and the preset time periods are numbered, and the energy consumption data, mechanical characteristic data, energy consumption weights and mechanical weights are processed to obtain the carbon emission prediction index, so as to more accurately quantify and estimate the carbon emission level according to the characteristics of the equipment, thereby achieving more comprehensive data collection.

[0024] 3. By preprocessing the energy consumption data and emission data, obtaining the carbon emission factor from the preset database, and then processing the results of the emission data processing and the energy consumption data processing to obtain the carbon emission assessment index, the actual carbon emissions can be more accurately assessed, and more accurate feedback on the collection of carbon emission data can be given. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the structure of a road engineering carbon emissions analysis system based on the Internet of Things provided in an embodiment of the present application;

[0026] Figure 2 A schematic diagram of the equipment condition assessment index according to the vibration frequency provided in the embodiment of the present application;

[0027] Figure 3 A schematic diagram of the equipment condition evaluation index according to the operating power provided in the embodiment of the present application;

[0028] Figure 4 A schematic diagram of the equipment condition assessment index provided in an embodiment of the present application changing with equipment load. DETAILED DESCRIPTION

[0029] The embodiment of the present application solves the problem of inaccurate carbon emission analysis in the prior art by providing a road engineering carbon emission analysis system based on the Internet of Things. The system monitors the operating status of road engineering equipment in real time and automatically takes corresponding first data collection measures according to the operating status. When the road engineering equipment is in operation, energy consumption data and mechanical characteristic data are obtained, and data preprocessing is performed on the energy consumption data and mechanical characteristic data. At the same time, energy consumption weights and mechanical weights are obtained from a preset database, and preset time periods are numbered, and the energy consumption data, mechanical characteristic data, energy consumption weights and mechanical weights are processed to obtain a carbon emission prediction index to take corresponding second data collection measures. Then, data preprocessing is performed on the energy consumption data and emission data, and carbon emission factors are obtained from the preset database. Finally, the results of ratio operation of the emission data processing result and the energy consumption data processing result are processed to obtain a carbon emission assessment index to regulate the second data collection measures, thereby improving the accuracy of carbon emission analysis of road engineering.

[0030] The technical solution in the embodiment of the present application is to solve the above-mentioned problem of inaccurate carbon emission analysis, and the overall idea is as follows:

[0031] By monitoring the operating status of road engineering equipment in real time and automatically taking corresponding first data collection measures according to the operating status, and then when the road engineering equipment is in operation, by collecting energy consumption data and mechanical characteristic data of the road engineering equipment within a preset time period, the carbon emission prediction index is obtained to take corresponding second data collection measures, and finally the carbon emission assessment index is obtained through the obtained emission data and energy consumption data to regulate the second data collection measures, thereby achieving the effect of improving the accuracy of carbon emission analysis of road engineering.

[0032] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] like Figure 1 As shown, it is a structural schematic diagram of a road engineering carbon emission analysis system based on the Internet of Things provided in an embodiment of the present application. The road engineering carbon emission analysis system based on the Internet of Things provided in an embodiment of the present application includes: an initial data acquisition module, a real-time data acquisition module and a carbon emission assessment module: wherein the initial data acquisition module is used to monitor the operating status of road engineering equipment in real time through industrial tools, and automatically take corresponding first data acquisition measures according to the operating status, the operating status includes startup, operation, standby and shutdown, and the first data acquisition measures include high-frequency acquisition, medium-frequency acquisition, low-frequency acquisition and ultra-low-frequency acquisition; the real-time data acquisition module is used to obtain a carbon emission prediction index by collecting energy consumption data and mechanical characteristic data of the road engineering equipment within a preset time period when the road engineering equipment is in an operating state, and according to the carbon emission prediction index The corresponding second data collection measure is taken, and the carbon emission prediction index is used to predict the degree of change of carbon emissions of road engineering equipment during operation; the carbon emission assessment module is used to obtain the carbon emission assessment index by acquiring the emission data and energy consumption data within a preset time period, and adjust the second data collection measure based on the carbon emission assessment index, and the carbon emission assessment index is used to measure the difference between the actual carbon emissions and the predicted carbon emissions; the energy consumption data includes fuel flow, electricity consumption and heat energy release; the mechanical property data includes vibration frequency, noise intensity and equipment surface temperature; the emission data includes carbon emission concentration, particulate matter concentration, exhaust gas temperature and volatile organic compound concentration; the carbon emission concentration indicates the emission concentration of carbon dioxide; the particulate matter concentration indicates the instantaneous concentration of PM2.5; the exhaust gas temperature indicates the exhaust gas temperature after the fuel is burned, which is used to reflect the combustion efficiency.

[0034] In this embodiment, by real-time monitoring of the operating status of road engineering equipment, the corresponding first data collection measures can be automatically taken according to different operating statuses, thereby ensuring the timeliness and accuracy of data collection. At the same time, by comparing the actual carbon emission level with the predicted carbon emission level, the degree of difference between the two can be more accurately measured, which is helpful for the adjustment and optimization of carbon emission related data collection, thereby improving the accuracy of carbon emission analysis of road projects.

[0035] Among them, the fuel flow is measured and obtained by a fuel flow meter, the electric energy consumption is obtained in real time by an electric meter, and the heat energy release is obtained by detecting a heat flow sensor; the vibration frequency is collected by a vibration sensor, the noise intensity is obtained by a noise monitor, and the equipment surface temperature is collected by a surface temperature sensor; the carbon emission concentration is measured by an exhaust gas analyzer (such as a Fourier transform infrared spectrometer, a thermal conductivity gas analyzer), the particulate matter concentration is measured by a particulate matter counter (such as a laser scattering method), the exhaust gas temperature is recorded by a thermocouple or an infrared thermometer installed at the exhaust port, and the volatile organic compound concentration is measured by using a photoionization detector or a gas chromatograph; through the above data collection methods, different types of data can be accurately obtained and used to support the analysis of data collection under dynamic conditions.

[0036] Furthermore, the specific process of automatically taking the corresponding first data collection measure according to the operating status is as follows: if the operating status is startup, the first data collection measure automatically taken is high-frequency collection, and high-frequency collection means data collection is performed according to a preset high-frequency collection frequency; if the operating status is running, the first data collection measure automatically taken is medium-frequency collection, and medium-frequency collection means data collection is performed according to a preset medium-frequency collection frequency; if the operating status is standby, the first data collection measure automatically taken is low-frequency collection, and low-frequency collection means data collection is performed according to a preset low-frequency collection frequency; if the operating status is shutdown, the first data collection measure automatically taken is ultra-low-frequency collection, and ultra-low-frequency collection means data collection is performed according to a preset ultra-low-frequency collection frequency.

[0037] In this embodiment, the high-frequency acquisition frequency is the fastest, and the ultra-low-frequency acquisition rate is the slowest. The preset high-frequency acquisition frequency is generally once per second, the preset medium-frequency acquisition frequency is generally once per minute, the preset low-frequency acquisition frequency is generally once every five minutes or once every ten minutes (depending on the specific situation), and the preset ultra-low-frequency acquisition frequency is generally once per hour. Different data acquisition frequencies are adopted according to different operating states of road engineering equipment, which can more comprehensively collect carbon emission related data. For example, when the operating state is startup, carbon emissions will increase instantaneously, and corresponding high-frequency acquisition is required. When the operating state is shutdown, the data acquisition frequency can be reduced to avoid waste of resources. By automatically adjusting the data acquisition frequency based on the operating state, resources can be used more effectively, while ensuring that accurate and useful data can be obtained in different states of road engineering equipment, which helps to improve the accuracy and efficiency of carbon emissions analysis and provide strong support for energy conservation, emission reduction and sustainable development of road engineering.

[0038] Furthermore, the specific process of obtaining the carbon emission prediction index is as follows: obtain energy consumption data and mechanical characteristic data, and perform data preprocessing on the energy consumption data and mechanical characteristic data, the data preprocessing includes data cleaning and data standardization; obtain energy consumption weights and mechanical weights from a preset database, the energy consumption weights include frequency weights, noise weights and temperature weights, the mechanical weights include fuel weights, electric energy weights and thermal energy weights; number the preset time periods, and process the energy consumption data, mechanical characteristic data, energy consumption weights and mechanical weights to obtain the carbon emission prediction index.

[0039] The specific limiting expression of the carbon emission prediction index is as follows:

[0040]

[0041] Where n is the number of the preset time period. , Indicates the total number of preset time periods. Indicates the vibration frequency of the road engineering equipment in the nth preset time period, Indicates the noise intensity of road engineering equipment in the nth preset time period, Indicates the equipment surface temperature of the road engineering equipment within the nth preset time period, represents the fuel flow of the road engineering equipment during the nth preset time period, Indicates the power consumption of road engineering equipment in the nth preset time period, Indicates the heat energy release of road engineering equipment in the nth preset time period, represents the frequency weight, represents the noise weight, represents the temperature weight, represents the fuel weight, represents the electric energy weight, represents the thermal energy weight, It represents the carbon emission prediction index of road engineering equipment in the nth preset time period. Represents an irrational number.

[0042] In this embodiment, the algorithm combines energy consumption data, mechanical property data, energy consumption weight and mechanical weight for comprehensive analysis to obtain a carbon emission prediction index, wherein, when the energy consumption weight and mechanical weight remain unchanged, as the energy consumption data and mechanical data increase, the carbon emission prediction index also increases. For example, assuming that the frequency weight, noise weight and temperature weight are 0.4, 0.3 and 0.3 respectively, assuming that the fuel weight, electric energy weight and thermal energy weight are 0.5, 0.2 and 0.3 respectively, a data change table of the carbon emission prediction index can be obtained, as shown in Table 1:

[0043] Table 1 Data changes of carbon emission prediction index

[0044]

[0045] It can be seen from Table 1 that as the energy consumption data and mechanical property data increase at the same time, the carbon emission prediction index also increases accordingly. For example, the fuel flow rate in the table increases from 0.5 in the first row to 0.7 in the fifth row, the electric energy consumption increases from 0.7 in the first row to 0.9 in the fifth row, the heat energy release increases from 0.6 in the first row to 0.7 in the fifth row, the vibration frequency increases from 0.3 in the first row to 0.8 in the fifth row, the noise intensity increases from 0.4 in the first row to 0.8 in the fifth row, the equipment surface temperature increases from 0.3 in the first row to 0.9 in the fifth row, and the corresponding carbon emission prediction index also increases from 0.67 in the first row to 0.74 in the fifth row. It can be seen that the energy consumption data and mechanical property data are positively correlated with the carbon emission prediction index.

[0046] Specifically, the specific expression of the sigmoid function is as follows:

[0047] ;

[0048] in, represents the input of the function, Represents the output of a function.

[0049] It should be noted that, as can be seen from Table 1, when only part of the energy consumption data or the mechanical property data increases, the carbon emission prediction index may not increase accordingly, and the influence of the energy consumption weight and the mechanical weight on the carbon emission prediction index cannot be ignored; in addition, the fuel flow determines the total energy input, the electric energy consumption is the useful work part of the fuel energy conversion, and the heat energy release is the inevitable loss in the energy conversion process. When the fuel flow is larger, the more fuel is burned, the more electric energy consumption and heat energy release may be generated; at the same time, the noise intensity and the surface temperature change of the equipment are often the reflection of the vibration frequency. When the vibration frequency is stable, the corresponding noise intensity and the surface equipment temperature will decrease; therefore, a detailed analysis of the comprehensive energy consumption data and mechanical property data will help to more accurately predict the carbon emission level, thereby achieving a more comprehensive collection of carbon emission related data to improve the accuracy of carbon emissions.

[0050] Specifically, the frequency weight is determined based on the weight that matches the vibration frequency in the preset database. This weight reflects the degree of influence of the vibration frequency on the carbon emission prediction index. In practical applications, the weight value corresponding to a specific vibration frequency can be directly retrieved from the preset database. This correspondence is established through a pre-set mapping relationship. For example, a one-to-one mapping set is formed between the vibration frequency and the corresponding carbon emission prediction index weight in the preset database. When it is needed, the real-time vibration frequency is input into this mapping set to obtain the corresponding weight value. In this example, the weight value range is set to between 0 and 1.

[0051] Specifically, the noise weight is determined based on the weight value that matches the noise intensity in the preset database. This weight value represents the degree of influence of the noise intensity on the carbon emission prediction index. In actual operation, the weight corresponding to a certain noise intensity can be directly searched and obtained from the preset database. This correspondence is achieved through a preset mapping relationship. For example, a one-to-one mapping set is formed between the noise intensity and the corresponding carbon emission prediction index weight in the preset database. When it is needed, the real-time noise intensity is input into this mapping set, and the corresponding weight value will be automatically returned. In this example, the weight value is limited to the range of 0 to 1.

[0052] Specifically, in this example, the sum of the frequency weight, the noise weight, and the temperature weight is 1.

[0053] Specifically, the fuel weight is determined based on the weight that matches the fuel flow in the preset database. This weight quantifies the degree of influence of the fuel flow on the carbon emission prediction index. In practical applications, the weight value corresponding to a specific fuel flow can be retrieved directly from the preset database. This correspondence is established through a preset mapping relationship. For example, a one-to-one mapping set is formed between the fuel flow and the corresponding carbon emission prediction index weight in the preset database. When the fuel weight needs to be calculated, the real-time fuel flow is input into this mapping set, and the corresponding weight value will be returned. In this example, the value range of the fuel weight is set to between 0 and 1, indicating that its influence changes from nothing to something.

[0054] Specifically, the electric energy weight is determined based on the weight value that matches the electric energy consumption in the preset database. This weight value is used to quantify the influence of electric energy consumption on the carbon emission prediction index. In actual operation, the weight corresponding to a certain electric energy consumption can be directly extracted from the preset database. This correspondence is established through a preset mapping relationship, that is, a one-to-one mapping set is formed between the electric energy consumption and the corresponding carbon emission prediction index weight in the preset database. When carbon emission prediction is required, the real-time electric energy consumption is input into this mapping set, and the corresponding weight value will be automatically returned. In this example, the value range of the electric energy weight is limited to between 0 and 1, which represents the different levels of influence of electric energy consumption on the carbon emission prediction index.

[0055] Specifically, in this example, the sum of the fuel weight, the electric energy weight, and the thermal energy weight is 1.

[0056] Furthermore, the specific steps for taking the corresponding second data collection measure according to the carbon emission prediction index are as follows: obtain the prediction evaluation interval from the preset database, the prediction evaluation interval includes the first prediction evaluation interval, the second prediction evaluation interval and the third prediction evaluation interval; if the carbon emission prediction index is within the first prediction evaluation interval, the second data collection measure taken is to adjust the current first data collection measure (i.e., data collection frequency) to high-frequency collection; if the carbon emission prediction index is within the second prediction evaluation interval, the second data collection measure taken is to adjust the current first data collection measure to medium-frequency collection; if the carbon emission prediction index is within the third prediction evaluation interval, the second data collection measure taken is to adjust the current first data collection measure to low-frequency collection.

[0057] In this embodiment, the data collection frequency is dynamically adjusted through the carbon emission prediction index, which can improve monitoring accuracy while optimizing resource allocation, reducing energy consumption, supporting intelligent management, and providing data support for graded early warning, ultimately achieving efficient, accurate and sustainable carbon emission monitoring and management.

[0058] Specifically, the prediction evaluation interval is obtained from a preset database. Since the carbon emissions during the operation of road engineering equipment are analyzed, the downtime is not considered, and the prediction evaluation interval is divided into three parts. In a specific embodiment, the energy consumption data and mechanical property data corresponding to the high-frequency collection in the historical data are substituted into the specific restriction expression of the carbon emission prediction index to obtain the corresponding data set, and the mean operation is performed to obtain the first mean value. The energy consumption data and mechanical property data corresponding to the low-frequency collection in the historical data are substituted into the specific restriction expression of the carbon emission prediction index to obtain the corresponding data set, and the mean operation is performed to obtain the second mean value. Then, the corresponding range from the first mean to 1 is recorded as the first prediction evaluation interval, the corresponding range from 0 to the second mean is recorded as the third prediction evaluation interval, and the corresponding range from the second mean to the first mean is recorded as the second prediction evaluation interval.

[0059] Furthermore, the specific process of obtaining the carbon emission assessment index is as follows: energy consumption data and emission data are obtained, and data preprocessing is performed on the energy consumption data and emission data, and the carbon emission factor is obtained from a preset database. The data preprocessing is used to make the energy consumption data and the emission data in the same dimension and de-unitized. The carbon emission factor is used to offset the energy consumption that cannot be avoided during the normal operation of road engineering equipment; the results of the ratio operation of the emission data processing results and the energy consumption data processing results are processed to obtain the carbon emission assessment index; the emission data processing result represents the value of the mean operation of the sum of the results of the hyperbolic tangent processing of the emission data; the energy consumption data processing result represents the product of the value of the mean operation of the sum of the results of the hyperbolic tangent processing of the energy consumption data and the carbon emission factor.

[0060] The specific limiting expression of the carbon emission assessment index is as follows:

[0061] Where n is the number of the preset time period. , Indicates the total number of preset time periods. represents the fuel flow of the road engineering equipment in the nth preset time period (assuming it is not 0), Indicates the power consumption of road engineering equipment in the nth preset time period (assuming it is not 0), Indicates the heat energy released by road engineering equipment in the nth preset time period (assuming it is not 0), represents the carbon emission factor, Indicates the carbon emission concentration of road engineering equipment in the nth preset time period, Indicates the concentration of particulate matter in road engineering equipment during the nth preset time period, Indicates the exhaust gas temperature of the road engineering equipment in the nth preset time period, Indicates the concentration of volatile organic compounds in road engineering equipment during the nth preset time period, Represents the carbon emission assessment index of road engineering equipment in the nth preset time period.

[0062] In this embodiment, the algorithm combines energy consumption data, emission data and carbon emission factors for comprehensive analysis to obtain a carbon emission assessment index. In the formula, as energy consumption data and emission data increase or decrease at the same time, the result of the ratio operation of emission data processing results and energy consumption data processing results is closer to 1, and the carbon emission assessment index is larger, indicating that the carbon emission prediction is more accurate and the collected carbon emission related data is more comprehensive. Among them, as the fuel flow, power consumption and heat energy release increase, the energy consumption data processing results also increase accordingly. As the carbon emission concentration, particulate matter concentration, exhaust gas temperature and volatile organic compound concentration increase, the emission data The larger the treatment result; among them, carbon emissions are usually mainly carbon dioxide and carbon monoxide, but incomplete combustion will produce carbonaceous particulate matter (such as black carbon or organic carbon), thereby increasing the concentration of particulate matter. High-temperature combustion usually helps the complete combustion of the fuel, reduces the emission of carbon monoxide (CO) and carbonaceous particulate matter, and increases the emission of carbon dioxide (CO2), and the carbon emission concentration increases accordingly; by combining emission data with energy consumption data for analysis, it is helpful to more objectively reflect the degree of match between carbon emission forecasts and actual carbon emissions, so as to take corresponding measures to adjust the frequency of data collection, which in turn helps to collect more comprehensive data related to carbon emissions.

[0063] Furthermore, the specific steps for obtaining the carbon emission factor are as follows: Step 1, dividing the test data obtained from the Internet of Things into first test data and second test data according to a preset ratio, and the test data includes energy consumption data and corresponding emission data; Step 2, substituting the first test data into the linear regression equation to obtain the initial model of the carbon emission factor, and the initial model of the carbon emission factor is used to establish a linear relationship between the emission data and the energy consumption data to obtain the first emission; Step 3, obtaining the first emission according to the established initial model of the carbon emission factor, and drawing an emission image through the first emission and the second emission, and obtaining the carbon emission factor according to the drawn emission image, the first emission represents the emission data corresponding to the energy consumption data, and the second emission represents the collected emission data, the emission image is used to visualize the change trend between the first emission and the second emission, and the carbon emission factor is the slope of the curve of the drawn image; Step 4, substituting the second test data into the initial model of the carbon emission factor to obtain the predicted emission, and deriving the mean square error according to the predicted emission and the second emission. If the obtained mean square error is within the reference error range, the current carbon emission factor is adopted, otherwise the steps are repeated until the obtained mean square error is within the reference error range.

[0064] In this embodiment, the preset ratio is usually divided into 7:3. According to the principle of conservation of energy, there is a linear relationship between energy consumption data and emission data. Therefore, the linear regression equation is selected to obtain the initial model of the carbon emission factor. The linear regression equation is a statistical method used to establish a linear relationship between two variables. In this embodiment, it is used to establish the relationship between emission data and energy consumption data; the mean square error is a statistical indicator for evaluating the error between the predicted value and the actual value, and is used to measure the accuracy of the model prediction.

[0065] Specifically, the linear regression equation , by using the least squares method to calculate the coefficient a and intercept b:

[0066]

[0067] Where n is the number of data points, is the sum of the products of the horizontal and vertical coordinates of all data points. is the sum of the horizontal coordinates of all data points, is the sum of the ordinates of all data points, is the sum of the squares of the horizontal coordinates of all data points; the equation expression obtained by substituting the coefficient a and intercept b into the linear regression equation is the initial model of the carbon emission factor, and the mean square error (MSE) is calculated by the following formula:

[0068]

[0069] Where N is the total number of data points, is the ith actual value, is the i-th prediction value. Through the above steps, the method of obtaining the carbon emission factor ensures the accuracy and reliability of the model. The process of data division, initial model establishment, time delay analysis, and model verification and optimization helps to accurately determine the relationship between emission data and energy consumption data, and improve the accuracy and application effect of the preset ratio.

[0070] Furthermore, the specific steps for regulating the second data collection measure based on the carbon emission assessment index are as follows: obtain an assessment threshold from a preset database, and the assessment threshold is used to determine the degree of consistency between the carbon emission forecast and the actual carbon emission; determine the relationship between the carbon emission assessment index and the assessment threshold: if the carbon emission assessment index is not less than the assessment threshold, the second data collection measure is not regulated; if the carbon emission assessment index is less than the assessment threshold, the second data collection measure is regulated; regulating the second data collection measure means increasing the data collection frequency corresponding to the second data collection measure by one level.

[0071] In this embodiment, the dynamic adjustment strategy based on evaluation index and threshold realizes the precision, intelligence and efficiency of data collection; at the same time, it has significant practical significance in improving the accuracy of carbon emission prediction, optimizing resource management, reducing costs and supporting sustainable development, and provides strong technical support for environmental monitoring and governance.

[0072] Specifically, the evaluation threshold is obtained from a preset database. In a specific embodiment, the energy consumption data and emission data corresponding to the data collection frequency problem in the historical data are substituted into the specific restriction expression of the carbon emission assessment index to obtain the corresponding data set, and the data set is averaged to obtain the evaluation threshold.

[0073] It should be understood that it also includes: if the carbon emission assessment index after adjusting the second data collection measures within the next preset time period is less than the assessment threshold, then stop adjusting the second data collection measures and obtain the equipment operation data; combine the acquired equipment operation data with the mechanical characteristic data to obtain the equipment condition assessment index, and determine whether to perform equipment maintenance based on the equipment condition assessment index. The equipment condition assessment index is used to evaluate the operational compliance of road engineering equipment; the method for obtaining the equipment condition assessment index is as follows: obtain the vibration frequency in the mechanical characteristic data and the equipment operation data, the equipment operation data including the operating power and the equipment load; obtain reference data from a preset database, the reference data including the reference vibration frequency, the reference operating power and the reference equipment load; obtain the equipment condition assessment index by processing the absolute deviations of the vibration frequency in the mechanical characteristic data, the equipment operation data and the corresponding reference data respectively.

[0074] The specific limiting expression of the equipment condition evaluation index is as follows:

[0075]

[0076] Where n is the number of the preset time period. , Indicates the total number of preset time periods. Indicates the vibration frequency of the road engineering equipment in the nth preset time period, Indicates the operating power of the road engineering equipment in the nth preset time period, Indicates the equipment load of the road engineering equipment in the nth preset time period, represents the reference vibration frequency, represents the reference operating power, represents the reference device load, Represents the equipment condition evaluation index of road engineering equipment in the nth preset time period.

[0077] In this embodiment, the algorithm combines the vibration frequency and the equipment operation data and the reference data for comprehensive analysis to obtain the equipment condition evaluation index, wherein the operating power is obtained through the power sensor, and the equipment load is obtained through the load sensor; in the formula, when the vibration frequency is closer to the reference vibration frequency, the equipment condition evaluation index is larger, and similarly, when the operating power is closer to the reference operating power, the equipment condition evaluation index is also larger, and similarly, as the equipment load is smaller than the reference equipment load, the corresponding equipment condition evaluation index is also larger, wherein the reference vibration frequency is set to 35Hz, the reference operating power is set to 100kW, and the reference equipment load is set to 15tons, as shown in the formula: Figure 2 As shown, Figure 2 A schematic diagram of the equipment condition evaluation index according to the vibration frequency provided in the embodiment of the present application is shown in the figure. Assuming that the operating power is 100kW and the equipment load is 15ton, when the vibration frequency is closer to the reference vibration frequency, the image shows an upward trend, and when the vibration frequency is farther away from the reference vibration frequency, the image shows a downward trend; similarly, Figure 3 A schematic diagram of the equipment condition evaluation index according to the operating power provided in the embodiment of the present application. Assuming that the vibration frequency is 35 Hz and the equipment load is 15 tons, as the operating power approaches the reference operating power, the image shows an upward trend, and when the vibration frequency is further away from the reference operating power, the image shows a downward trend; Figure 4 A schematic diagram of the equipment condition assessment index changing with equipment load provided in an embodiment of the present application, assuming that the vibration frequency is 35 Hz and the operating power is 100 kW, when the equipment load is less than the reference equipment load, the larger the equipment condition assessment index is, the smaller the possibility of equipment abnormality is, and when the equipment load is greater than the reference equipment load, as the equipment load increases, the equipment condition assessment index decreases accordingly, indicating that the possibility of equipment abnormality is greater; wherein, the vibration frequency is affected by changes in equipment load and operating power, and generally shows a positive correlation, but under abnormal conditions (such as the presence of potential faults) may show a nonlinear change; by evaluating the operating status of road engineering equipment, it is helpful to promptly check whether the relevant data collection of carbon emissions is incomplete due to reasons of road engineering equipment, so as to take corresponding measures in time to reduce data loss and improve data collection efficiency.

[0078] Specifically, the reference data is obtained from a preset database. In a specific embodiment, the reference data is obtained through a technical manual provided by the equipment manufacturer, for example, the reference vibration frequency corresponding to a Caterpillar CS54B single-wheel vibratory roller in a low vibration mode is 30 Hz, the reference operating power corresponding to a VÖGELE Super 1800-3i paver is 150 kW, and the reference equipment load corresponding to a SANY STG230C dump truck is 20 t.

[0079] Furthermore, the specific process of judging whether to perform equipment maintenance according to the equipment condition assessment index is as follows: obtain the maintenance threshold from the preset database, and the maintenance threshold is used to judge whether to perform equipment maintenance; judge the size relationship between the equipment condition assessment index and the maintenance threshold: if the equipment condition assessment index is not less than the maintenance threshold, then the equipment maintenance is not performed; if the equipment condition assessment index is less than the maintenance threshold, then the equipment maintenance is performed; equipment maintenance includes fault diagnosis, calibration adjustment and recording maintenance logs; fault diagnosis means using diagnostic tools to detect whether there are faults in the operating parameters of road engineering equipment; calibration adjustment means prompting preset staff to calibrate the road engineering equipment to ensure that key parameters meet the construction requirements, and at the same time adjust the chain and belt tension and the trajectory of the walking system of the road engineering equipment to keep the road engineering equipment running stably; recording maintenance logs means recording the specific content and situation of each equipment maintenance for traceability.

[0080] In this embodiment, by maintaining road engineering equipment on demand and promptly discovering and handling potential failures and performance degradation problems, the reliability and stability of road engineering equipment are improved. Reasonable maintenance measures can reduce the wear and damage of road engineering equipment, extend the service life of road engineering equipment, and reduce the cost of replacement and repair of road engineering equipment. In addition, timely discovery and handling of equipment failures can reduce safety risks during construction and ensure the safety of personnel and equipment. At the same time, detailed records of maintenance logs facilitate subsequent tracing and management of equipment maintenance, providing strong support for long-term maintenance and management of equipment.

[0081] Specifically, the maintenance threshold is obtained from a preset database. In a specific embodiment, the equipment operation data and vibration frequency corresponding to the equipment maintenance required in the historical data are substituted into the specific restriction expression of the equipment condition evaluation index to obtain the corresponding data set, and the data set is averaged to obtain the maintenance threshold.

[0082] To summarize, the embodiment of the present application monitors the operating status of road engineering equipment in real time, and automatically takes corresponding first data collection measures according to the operating status; then, when the road engineering equipment is in operation, the carbon emission prediction index is obtained by collecting energy consumption data and mechanical characteristic data of the road engineering equipment within a preset time period to take corresponding second data collection measures; finally, the carbon emission assessment index is obtained through the acquired emission data and energy consumption data to regulate the second data collection measures, thereby improving the efficiency of data collection, and further improving the accuracy of carbon emission analysis of road engineering, effectively solving the problem of inaccurate carbon emission analysis of road engineering in the prior art.

[0083] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0087] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0088] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A road engineering carbon emissions analysis system based on the Internet of Things, characterized in that: Including initial data acquisition module, real-time data acquisition module and carbon emission assessment module: The initial data acquisition module is used to monitor the operating status of road engineering equipment in real time through industrial tools, and automatically take corresponding first data acquisition measures according to the operating status, wherein the first data acquisition measures include high frequency acquisition, medium frequency acquisition, low frequency acquisition and ultra-low frequency acquisition; The real-time data acquisition module is used to obtain a carbon emission prediction index by collecting energy consumption data and mechanical property data of the road engineering equipment within a preset time period when the road engineering equipment is in operation, and to take corresponding second data acquisition measures according to the carbon emission prediction index, wherein the carbon emission prediction index is used to predict the degree of change in carbon emissions of the road engineering equipment during operation; The carbon emission assessment module is used to obtain a carbon emission assessment index by acquiring emission data and energy consumption data within a preset time period, and to adjust the second data collection measure based on the carbon emission assessment index, wherein the carbon emission assessment index is used to measure the degree of difference between actual carbon emissions and predicted carbon emissions; The energy consumption data includes fuel flow, electrical energy consumption and heat energy release; The mechanical characteristic data include vibration frequency, noise intensity and equipment surface temperature; The emission data include carbon emission concentration, particulate matter concentration, exhaust gas temperature and volatile organic matter concentration; The carbon emission concentration refers to the emission concentration of carbon dioxide; The particle concentration represents the instantaneous concentration of PM2.5; The exhaust gas temperature indicates the exhaust gas temperature after fuel combustion, and is used to reflect the combustion efficiency; The specific process of obtaining the carbon emission prediction index is as follows: Acquiring energy consumption data and mechanical property data, and performing data preprocessing on the energy consumption data and the mechanical property data, wherein the data preprocessing includes data cleaning and data standardization; Acquire energy consumption weight and mechanical weight from a preset database, wherein the energy consumption weight includes frequency weight, noise weight and temperature weight, and the mechanical weight includes fuel weight, electric energy weight and thermal energy weight; The preset time periods are numbered, and the energy consumption data, mechanical characteristic data, energy consumption weights and mechanical weights are processed to obtain a carbon emission prediction index.

2. A road engineering carbon emissions analysis system based on the Internet of Things as claimed in claim 1, characterized in that: The specific process of automatically taking the corresponding first data collection measure according to the operating status is as follows: If the operating state is startup, the first data collection measure automatically taken is high-frequency collection; If the operation status is running, the first data collection measure automatically taken is intermediate frequency collection; If the operating state is standby, the first data collection measure automatically taken is low-frequency collection; If the operating state is shutdown, the first data collection measure automatically taken is ultra-low frequency collection.

3. The road engineering carbon emissions analysis system based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of taking the corresponding second data collection measures according to the carbon emission prediction index are as follows: Acquire a prediction evaluation interval from a preset database, wherein the prediction evaluation interval includes a first prediction evaluation interval, a second prediction evaluation interval, and a third prediction evaluation interval; If the carbon emission forecast index is within the first forecast assessment interval, the second data collection measure adopted is high-frequency collection; If the carbon emission prediction index is within the second prediction evaluation interval, the second data collection measure adopted is medium frequency collection; If the carbon emission prediction index is within the third prediction evaluation interval, the second data collection measure adopted is low-frequency collection.

4. The road engineering carbon emissions analysis system based on the Internet of Things as claimed in claim 1, characterized in that: The specific process of obtaining the carbon emission assessment index is as follows: Obtain energy consumption data and emission data, perform data preprocessing on the energy consumption data and emission data, and obtain carbon emission factors from a preset database; The results of the ratio operation of the emission data processing results and the energy consumption data processing results are processed to obtain the carbon emission assessment index; The emission data processing result represents the value of the mean value calculated by summing the results of hyperbolic tangent processing on the emission data; The energy consumption data processing result represents the product of the value obtained by performing an average operation on the sum of the results of performing hyperbolic tangent processing on the energy consumption data and the carbon emission factor.

5. A road engineering carbon emissions analysis system based on the Internet of Things as claimed in claim 4, characterized in that: The specific steps for obtaining the carbon emission factor are as follows: Step 1: dividing the acquired test data into first test data and second test data according to a preset ratio, wherein the test data includes energy consumption data and corresponding emission data; Step 2: Substituting the first test data into a linear regression equation to obtain an initial model of a carbon emission factor, wherein the initial model of a carbon emission factor is used to establish a linear relationship between the emission data and the energy consumption data to obtain a first emission amount; Step 3: obtaining a first emission amount according to the established initial model of carbon emission factors, and drawing an emission amount image through the first emission amount and the second emission amount, and obtaining a carbon emission factor according to the drawn emission amount image, wherein the emission amount image is used to visualize a change trend between the first emission amount and the second emission amount; Step four, substitute the second test data into the initial model of the carbon emission factor to obtain the predicted emissions, and obtain the mean square error based on the predicted emissions and the second emissions. If the obtained mean square error is within the reference error range, the current carbon emission factor is adopted, otherwise the steps are repeated until the obtained mean square error is within the reference error range.

6. The carbon emission analysis system for road engineering based on the Internet of Things as claimed in claim 4, characterized in that: The specific steps of regulating the second data collection measure based on the carbon emission assessment index are as follows; Obtaining an evaluation threshold from a preset database, wherein the evaluation threshold is used to determine the degree of consistency between the carbon emission forecast and the actual carbon emission; If the carbon emission assessment index is not less than the assessment threshold, the second data collection measure will not be adjusted; If the carbon emission assessment index is less than the assessment threshold, the second data collection measure is adjusted; The regulating the second data collection measure means increasing the data collection frequency corresponding to the second data collection measure by one level.

7. The road engineering carbon emissions analysis system based on the Internet of Things as claimed in claim 1, characterized in that: Also includes: If the carbon emission assessment index after adjusting the second data collection measure within the next preset time period is less than the assessment threshold, then the second data collection measure is stopped and the equipment operation data is obtained; The acquired equipment operation data is combined with the mechanical characteristic data to obtain an equipment condition evaluation index, and whether to perform equipment maintenance is determined according to the equipment condition evaluation index, wherein the equipment condition evaluation index is used to evaluate the operational compliance of road engineering equipment; The method for obtaining the equipment condition evaluation index is as follows: Acquire the vibration frequency and equipment operation data in the mechanical characteristic data, wherein the equipment operation data includes operation power and equipment load; Acquire reference data from a preset database, wherein the reference data includes a reference vibration frequency, a reference operating power, and a reference equipment load; The equipment condition assessment index is obtained by processing the vibration frequency in the mechanical characteristic data, the equipment operation data and the absolute deviation of the corresponding reference data respectively.

8. The road engineering carbon emissions analysis system based on the Internet of Things as claimed in claim 7, characterized in that: The specific process of judging whether to perform equipment maintenance according to the equipment condition evaluation index is as follows; Obtaining a maintenance threshold from a preset database, where the maintenance threshold is used to determine whether to perform equipment maintenance; If the equipment condition assessment index is not less than the maintenance threshold, no equipment maintenance will be performed; If the equipment condition assessment index is less than the maintenance threshold, equipment maintenance is performed; The equipment maintenance includes fault diagnosis, calibration adjustment and recording of maintenance logs.

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