Energy consumption monitoring and early warning system for green road construction

Abstract

The invention relates to the technical field of construction energy consumption monitoring, and discloses a green road construction energy consumption monitoring and early warning system which comprises a data collection module, a data processing module, a data analysis module, an output module and an early warning module. According to the energy consumption monitoring and early warning system for green road construction, the electricity consumption, the fuel consumption, the water consumption and the pollutant concentration fluctuation value are compared with corresponding threshold values, when the electricity consumption exceeds the corresponding threshold value, it is judged that the construction equipment operates abnormally or is used excessively, and when the fuel consumption exceeds the corresponding threshold value, it is judged that the construction equipment operates abnormally or is used excessively. When the water consumption exceeds a corresponding threshold value, it is judged that water leakage or improper water resource management occurs in the construction process, and when the pollutant concentration fluctuation value exceeds a corresponding threshold value, it is judged that pollution source emission is unstable or emission source equipment breaks down, and an early warning module initiates early warning; the method is used for assisting workers in quickly finding abnormal factors so as to take corresponding measures.

Description

Technical Field

[0001] The invention relates to the technical field of construction energy consumption monitoring, and in particular to a green highway construction energy consumption monitoring and early warning system. Background Art

[0002] Green highway construction is a modern highway construction model that aims to achieve resource conservation, ecological protection, energy conservation and high efficiency, and improved service quality. It is not only the practice of the transportation industry to implement the five development concepts of innovation, coordination, greenness, openness, and sharing, but also an important measure to promote the transformation and upgrading of the industry. The green highway construction energy consumption monitoring and early warning system is a comprehensive platform that aims to achieve real-time monitoring, statistical analysis, and early warning management of energy consumption during green highway construction. This system uses modern information technology to conduct statistical monitoring of energy consumption during highway construction and operation to reveal energy efficiency issues, take energy-saving measures in a timely manner, reduce resource waste, and promote the sustainable development of green highway construction.

[0003] At present, the monitoring process of construction energy consumption is affected by many factors, which makes the system more complicated. As a result, it is not easy to quickly diagnose the problem when energy consumption is abnormal. In addition, various pollutants may be generated during highway construction, such as dust, nitrogen oxides, sulfur oxides, etc. Monitoring the concentration of these pollutants can timely understand and control environmental pollution and reduce the negative impact on air quality. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a green highway construction energy consumption monitoring and early warning system, which has the advantages of being able to detect energy consumption through real-time monitoring of electricity consumption, fuel consumption and water consumption, and being able to promptly discover any abnormalities, thereby solving the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a green highway construction energy consumption monitoring and early warning system, comprising a data collection module, a data processing module, a data analysis module, an output module and an early warning module;

[0008] The data collection module includes an electric quantity measuring unit, a fuel measuring unit, a water quantity measuring unit and a pollutant concentration measuring unit, wherein;

[0009] The power measurement unit is used to collect real-time current D1 and real-time voltage Dy, the fuel measurement unit is used to collect real-time fuel flow Rlll, the water measurement unit collects real-time water flow Ysll, and the pollutant concentration measurement unit is used to collect real-time pollutant concentration Wrnd;

[0010] The data collection module collects multiple real-time currents D1, real-time voltages Dy, real-time fuel flows Rlll, real-time water flows Ysll, and real-time pollutant concentrations Wrnd collected in a time period to form real-time current data sets, real-time voltage data sets, real-time fuel flow data sets, real-time water flow data sets, and real-time pollutant concentration data sets, respectively, and the data processing module sends these data sets to the data processing module through the network;

[0011] The data processing module pre-processes the real-time current data set, the real-time voltage data set, the real-time fuel flow data set, the real-time water flow data set and the real-time pollutant concentration data set respectively, and obtains the real-time current qD1 after cleaning, the real-time voltage qDy after cleaning, the real-time fuel flow qRlll after cleaning, the real-time water flow qYsll after cleaning and the real-time pollutant concentration qWrnd after cleaning respectively. The data processing module is preset with an environmental impact coefficient Xs. The data processing module calculates the power consumption Yd1 according to the real-time current qD1 after cleaning, the real-time voltage qDy after cleaning and the environmental impact coefficient Xs. The data processing module calculates the fuel consumption Rlyl according to the real-time fuel flow qRlll after cleaning and the environmental impact coefficient Xs. The data processing module calculates the water consumption Ysl according to the real-time water flow qYsll after cleaning and the environmental impact coefficient Xs. The data processing module calculates the pollutant concentration fluctuation value Ndbd according to the real-time pollutant concentration qWrnd after cleaning. The data processing module sends these data to the data analysis module through the network;

[0012] The data analysis module presets a useful power threshold value Ydl yz , fuel usage threshold Rlyl yz , Water consumption threshold Ysl yz And the pollutant concentration fluctuation threshold Ndbd yz The data analysis module compares the power consumption Ydl, fuel consumption Rlyl, water consumption Ysl and pollutant concentration fluctuation value Ndbd with the corresponding threshold values ​​respectively, and sends the comparison result to the output module through the network. When any value is abnormal, the output module sends the abnormal signal to the early warning module through the network;

[0013] The early warning module sends an early warning signal to the staff.

[0014] Preferably, the real-time current data set expression is {Dl1, Dl2, ..., Dl n}, the real-time voltage data set expression is {Dy1, Dy2, ..., Dy n}, the real-time fuel flow data set expression is {Rlll1, Rlll2, ..., Rlll n}, the real-time water flow data set expression is {Ysll1, Ysll2, ..., Ysll n}, the real-time pollutant concentration data set expression is {Wrnd1, Wrnd2, ..., Wrnd n}, and the number of data in each data set is the same, the collection period is the same, and the time interval between each data in the data set is the same. The subscript n in each data set means that there are n data in each data set.

[0015] Preferably, the real-time current qD1 after cleaning is expressed as follows:

[0016]

[0017] In the formula, i=1 in the table means that the calculation starts from the first data in the real-time current data set, i.e., Dl1, and n means that the calculation ends at the nth data in the data set, i.e., until Dl n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time current qDl after cleaning;

[0018] The algorithm expression of the real-time voltage qDy after cleaning is as follows:

[0019]

[0020] In the formula, i=1 in the table means that the calculation starts from the first data in the real-time voltage data set, that is, Dy1, and n means that the calculation ends at the nth data in the data set, that is, Dy1. n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time voltage qDy after cleaning;

[0021] The real-time fuel flow rate qR111 after cleaning is expressed as follows:

[0022]

[0023] In the formula, i=1 in the formula indicates that the calculation starts from the first data in the real-time fuel flow data set, i.e., R111, and n indicates that the calculation ends at the nth data in the data set, i.e., R112. nSo far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time fuel flow rate qR111 after cleaning;

[0024] The real-time water flow rate qYsll after cleaning is expressed by the following algorithm:

[0025]

[0026] In the formula, i=1 in the table means that the calculation starts from the first data in the real-time water flow data set, that is, Ysll1, and n means that the calculation ends at the nth data in the data set, that is, Ysll. n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time water flow rate qYsll after cleaning;

[0027] The algorithm expression of the real-time pollutant concentration qWrnd after cleaning is as follows:

[0028]

[0029] In the formula, i=1 in the table indicates that the calculation starts from the first data in the real-time pollutant concentration data set, that is, Wrnd1, and n indicates that the calculation ends at the nth data in the data set, that is, Wrnd n So far, N in the denominator means that there are N data in the data set, and the calculated result is the mean of all the data in the data set after summing up, which is the real-time pollutant concentration qWrnd after cleaning.

[0030] Preferably, the power consumption Ydl algorithm expression is as follows:

[0031] Ydl=qDl*qDy*Glys*Yxsj*Xs

[0032] In the formula, Ydl represents power consumption, qDl represents real-time current after cleaning, qDy represents real-time voltage after cleaning, Glys represents power factor, Yxsj represents operating time, and Xs represents environmental impact coefficient.

[0033] Preferably, the fuel consumption Rlyl algorithm expression is as follows:

[0034] Rlyl=qRlll*Yxsj*Xs

[0035] In the formula, Rlyl represents the fuel consumption, qRlll represents the real-time fuel flow after cleaning, Yxsj represents the operating time, and Xs represents the environmental impact coefficient.

[0036] Preferably, the water consumption Ysl algorithm expression is as follows:

[0037] Ysl=qYsll*Yxsj*Xs

[0038] In the formula, Ysl represents the water consumption, qYsll represents the real-time water flow after cleaning, Yxsj represents the operating time, and Xs represents the environmental impact coefficient.

[0039] Preferably, the pollutant concentration fluctuation value Ndbd algorithm expression is as follows:

[0040] Ndbd=|qWrnd-qWrnd q |

[0041] In the formula, qWrnd q Indicates the real-time pollutant concentration after cleaning qWrnd calculated this time, which is the real-time pollutant concentration after cleaning calculated by the algorithm for the previous pollutant concentration data set of the current pollutant concentration data set. |qWrnd-qWrnd q | indicates the absolute value of the difference between the two.

[0042] Preferably, the data analysis module compares the power consumption Ydl, fuel consumption Rlyl and water consumption Ysl with the power consumption threshold Ydl yz , fuel usage threshold Rlyl yz And the water consumption threshold Ysl yz For comparison, when the electricity consumption Ydll exceeds the corresponding threshold, it is determined that the construction equipment is operating abnormally or is overused; when the fuel consumption Rlyl exceeds the corresponding threshold, it is determined that the construction machinery is inefficient or the fuel is incompletely burned; when the water consumption Ysl exceeds the corresponding threshold, it is determined that water leakage or improper water resource management occurs during the construction process.

[0043] Preferably, the data analysis module compares the pollutant concentration fluctuation value Ndbd with the pollutant concentration fluctuation value threshold Ndbd yz By comparison, when the pollutant concentration fluctuation value Ndbd exceeds the corresponding threshold, it is determined that the pollution source emission is unstable or the emission source equipment fails.

[0044] Preferably, the early warning module includes a display terminal and an audio terminal, the display terminal is used to display factors exceeding corresponding thresholds, and the audio terminal is used to play factors exceeding corresponding thresholds, so as to assist staff to quickly find abnormal factors and take corresponding measures.

[0045] Compared with the prior art, the present invention provides a green highway construction energy consumption monitoring and early warning system, which has the following beneficial effects:

[0046] The present invention compares the electricity consumption, fuel consumption, water consumption and pollutant concentration fluctuation value with the corresponding threshold values ​​respectively. When the electricity consumption exceeds the corresponding threshold value, it is determined that the construction equipment is operating abnormally or is overused. When the fuel consumption exceeds the corresponding threshold value, it is determined that the use efficiency of the construction machinery is reduced or the fuel is not burned completely. When the water consumption exceeds the corresponding threshold value, it is determined that water leakage or improper water resource management occurs during the construction process. When the pollutant concentration fluctuation value exceeds the corresponding threshold value, it is determined that the pollution source emission is unstable or the emission source equipment fails. The output module sends the abnormal signal to the early warning module through the network. The early warning module initiates an early warning to assist the staff to quickly find the abnormal factors and take corresponding measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] See also Figure 1 , a green highway construction energy consumption monitoring and early warning system, including a data collection module, a data processing module, a data analysis module, an output module and an early warning module, the data collection module includes an electric quantity measurement unit, a fuel measurement unit, a water quantity measurement unit and a pollutant concentration measurement unit;

[0050] The power measurement unit is used to collect real-time current Dl and real-time voltage Dy, the fuel measurement unit is used to collect real-time fuel flow Rlll, the water measurement unit collects real-time water flow Ysll, and the pollutant concentration measurement unit is used to collect real-time pollutant concentration Wrnd;

[0051] In the green highway construction energy consumption monitoring and early warning system, detecting water consumption, electricity consumption and fuel consumption is an important measure to ensure that the project meets the requirements of energy conservation and emission reduction. The monitoring of these indicators not only helps to improve resource utilization efficiency, but also promotes the realization of environmental protection and sustainable development goals. By real-time monitoring of water consumption, electricity consumption and fuel consumption, resource consumption can be effectively tracked and controlled to avoid waste.

[0052] The data collection module collects multiple real-time currents D1, real-time voltages Dy, real-time fuel flows Rlll, real-time water flows Ysll, and real-time pollutant concentrations Wrnd collected in a time period to form real-time current data sets, real-time voltage data sets, real-time fuel flow data sets, real-time water flow data sets, and real-time pollutant concentration data sets, respectively. The expressions of each data set are as follows:

[0053] The real-time current data set expression is {Dl1, Dl2, ..., Dl n};

[0054] The real-time voltage data set expression is {Dy1, Dy2, ..., Dy n};

[0055] The real-time fuel flow data set expression is {Rlll1, Rlll2, ..., Rlll n};

[0056] The expression of real-time water flow data set is {Ysll1, Yssl2, ..., Ysll n};

[0057] The real-time pollutant concentration dataset expression is {Wrnd1, Wrnd2, ..., Wrnd n};

[0058] In the above-mentioned data sets, the number of data in each data set is the same, the collection period is the same, and the time interval of each data in the data set is the same. The subscript n in each data set indicates that there are n data in each data set. The same number of data, the same collection period, and the same time interval of each data in the data set can ensure the consistency of the data, which is conducive to subsequent processing and analysis. At the same time, no additional data alignment processing is required, thereby simplifying data processing;

[0059] The data processing module sends these data sets to the data processing module through the network;

[0060] The data processing module preprocesses the real-time current data set, the real-time voltage data set, the real-time fuel flow data set, the real-time water flow data set and the real-time pollutant concentration data set, and obtains the real-time current qD1 after cleaning, the real-time voltage qDy after cleaning, the real-time fuel flow qRlll after cleaning, the real-time water flow qYsll after cleaning and the real-time pollutant concentration qWrnd after cleaning. The specific cleaning algorithm is as follows:

[0061] The real-time current qDl algorithm expression after cleaning is as follows:

[0062]

[0063] In the formula, i=1 in the table means that the calculation starts from the first data in the real-time current data set, i.e., Dl1, and n means that the calculation ends at the nth data in the data set, i.e., until Dl n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time current qDl after cleaning;

[0064] The real-time voltage qDy algorithm expression after cleaning is as follows:

[0065]

[0066] In the formula, i=1 in the table means that the calculation starts from the first data in the real-time voltage data set, that is, Dy1, and n means that the calculation ends at the nth data in the data set, that is, Dy1. n So far, N in the denominator means that there are N data in the data set. The calculated result is the mean of all data in the data set after summing, which is the real-time voltage qDy after cleaning. The data processing module calculates the power consumption Ydl according to the real-time current qDl after cleaning and the real-time voltage qDy after cleaning. The algorithm expression of power consumption Ydl is as follows:

[0067] Ydl=qDl*qDy*Glys*Yxsj*Xs

[0068] In the formula, Ydl represents power consumption, qDl represents real-time current after cleaning, qDy represents real-time voltage after cleaning, Glys represents power factor, Yxsj represents operating time, and Xs represents environmental impact coefficient;

[0069] By monitoring power consumption in real time, we can identify equipment and processes with high power consumption and implement energy-saving transformation and optimization measures, such as adjusting equipment operation time, optimizing energy efficiency ratio, and reducing unnecessary power consumption. At the same time, monitoring power consumption changes can help detect abnormal operation or failure of equipment, and perform maintenance and repair in advance to maintain efficient operation of equipment.

[0070] The real-time fuel flow rate qRlll after cleaning is expressed as follows:

[0071]

[0072] In the formula, i=1 in the formula indicates that the calculation starts from the first data in the real-time fuel flow data set, i.e., the calculation starts from qR111, and n indicates that the calculation ends at the nth data in the data set, i.e., the calculation ends at R111. nSo far, N in the denominator means that there are N data in the data set. The calculated result is the mean of all data in the data set after summing, which is the real-time fuel flow rate qRlll after cleaning. The data processing module calculates the fuel consumption Rlyl according to the real-time fuel flow rate qRlll after cleaning. The algorithm expression of the fuel consumption Rlyl is as follows:

[0073] Rlyl=qRlll*Yxsj*Xs

[0074] In the formula, Rlyl represents the fuel consumption, qRlll represents the real-time fuel flow after cleaning, Yxsj represents the operating time, and Xs represents the environmental impact coefficient;

[0075] By monitoring fuel usage in real time, it is possible to identify equipment and processes with high fuel consumption and implement energy-saving measures, such as optimizing machinery use, improving combustion efficiency or using more efficient fuels. Monitoring changes in fuel usage can also help detect equipment failures or reduced efficiency, and timely maintenance and repairs can be performed to keep the equipment running normally.

[0076] The real-time water flow rate qYsll after cleaning is expressed as follows:

[0077]

[0078] In the formula, i=1 in the table means that the calculation starts from the first data in the real-time water flow data set, that is, Ysll1, and n means that the calculation ends at the nth data in the data set, that is, Ysll. n So far, N in the denominator means that there are N data in the data set. The calculated result is the mean of all data in the data set after summing, which is the real-time water flow qYsll after cleaning. The data processing module calculates the water consumption Ysl according to the real-time water flow qYsll after cleaning. The water consumption Ysl algorithm expression is as follows:

[0079] Ysl=qYsll*Yxsj*Xs

[0080] In the formula, Ysl represents the water consumption, qYsll represents the real-time water flow after cleaning, Yxsj represents the operating time, and Xs represents the environmental impact coefficient;

[0081] Real-time monitoring of water consumption can identify locations and processes with high water consumption and implement water conservation measures, such as installing water-saving equipment, improving irrigation systems, and reducing leakage. Monitoring changes in water consumption can help detect pipe and equipment problems early, so that maintenance can be carried out in time to avoid more extensive damage.

[0082] The real-time pollutant concentration qWrnd after cleaning is expressed as follows:

[0083]

[0084] In the formula, i=1 in the table indicates that the calculation starts from the first data in the real-time pollutant concentration data set, that is, Wrnd1, and n indicates that the calculation ends at the nth data in the data set, that is, Wrnd n So far, N in the denominator means that there are N data in the data set. The calculated result is the mean of all data in the data set after summing up, which is the real-time pollutant concentration qWrnd after cleaning. The data processing module calculates the pollutant concentration fluctuation value Ndbd according to the real-time pollutant concentration qWrnd after cleaning. The algorithm expression of the pollutant concentration fluctuation value Ndbd is as follows:

[0085] Ndbd=|qWrnd-qWrnd q |

[0086] In the formula, qWrnd q Indicates the real-time pollutant concentration after cleaning qWrnd calculated this time, which is the real-time pollutant concentration after cleaning calculated by the algorithm for the previous pollutant concentration data set of the current pollutant concentration data set. |qWrnd-qWrnd q | indicates that the difference between the two is the absolute value;

[0087] By monitoring the concentration of pollutants, it is possible to promptly detect situations where emissions exceed the standard, take measures to reduce pollutant emissions, and protect the ecological environment. Monitoring the concentration of pollutants can help detect equipment or process problems, and make timely repairs and adjustments to keep the equipment running efficiently.

[0088] The useful power threshold Ydl is preset in the data analysis module yz , fuel usage threshold Rlyl yz , Water consumption threshold Ysl yz And the pollutant concentration fluctuation threshold Ndbd yz The data analysis module compares the electricity consumption Ydl, fuel consumption Rlyl, water consumption Ysl and pollutant concentration fluctuation value Ndbd with the corresponding thresholds respectively. When the electricity consumption Ydl exceeds the corresponding threshold, it is determined that the construction equipment is operating abnormally or overused. When the fuel consumption Rlyl exceeds the corresponding threshold, it is determined that the construction machinery is less efficient or the fuel is not burned completely. When the water consumption Ysl exceeds the corresponding threshold, it is determined that water leakage or improper water resource management occurs during the construction process. When the pollutant concentration fluctuation value Ndbd exceeds the corresponding threshold, it is determined that the pollution source emission is unstable or the emission source equipment fails.

[0089] The early warning module includes a display end and an audio end. The display end is used to display factors that exceed corresponding thresholds, and the audio end is used to play factors that exceed corresponding thresholds, so as to assist staff to quickly find abnormal factors and take corresponding measures.

[0090] This system compares the electricity consumption, fuel consumption, water consumption and pollutant concentration fluctuation values ​​with the corresponding thresholds. When the electricity consumption exceeds the corresponding threshold, it is determined that the construction equipment is operating abnormally or is overused. When the fuel consumption exceeds the corresponding threshold, it is determined that the construction machinery is inefficient or the fuel is not burned completely. When the water consumption exceeds the corresponding threshold, it is determined that there is leakage or improper water resource management during the construction process. When the pollutant concentration fluctuation value exceeds the corresponding threshold, it is determined that the pollution source emission is unstable or the emission source equipment fails. The output module sends the abnormal signal to the early warning module through the network. The early warning module initiates an early warning to assist the staff to quickly find the abnormal factors and take corresponding measures.

[0091] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A green highway construction energy consumption monitoring and early warning system, characterized by: It includes data collection module, data processing module, data analysis module, output module and early warning module; The data collection module includes an electric quantity measuring unit, a fuel measuring unit, a water quantity measuring unit and a pollutant concentration measuring unit, wherein; The power measurement unit is used to collect real-time current D1 and real-time voltage Dy, the fuel measurement unit is used to collect real-time fuel flow Rlll, the water measurement unit collects real-time water flow Ysll, and the pollutant concentration measurement unit is used to collect real-time pollutant concentration Wrnd; The data collection module forms a real-time current data set, a real-time voltage data set, a real-time fuel flow data set, a real-time water flow data set and a real-time pollutant concentration data set with multiple real-time currents D1, real-time voltages Dy, real-time fuel flows R111, real-time water flows Ts11 and real-time pollutant concentrations Wrnd collected in a time period, respectively, and the data processing module sends these data sets to the data processing module through the network; The data processing module pre-processes the real-time current data set, the real-time voltage data set, the real-time fuel flow data set, the real-time water flow data set and the real-time pollutant concentration data set respectively, and obtains the real-time current qD1 after cleaning, the real-time voltage qDy after cleaning, the real-time fuel flow qRlll after cleaning, the real-time water flow qYsll after cleaning and the real-time pollutant concentration qWrnd after cleaning respectively. The data processing module is preset with an environmental impact coefficient Xs. The data processing module calculates the power consumption Yd1 according to the real-time current qD1 after cleaning, the real-time voltage qDy after cleaning and the environmental impact coefficient Xs. The data processing module calculates the fuel consumption Rlyl according to the real-time fuel flow qRlll after cleaning and the environmental impact coefficient Xs. The data processing module calculates the water consumption Ysl according to the real-time water flow qYsll after cleaning and the environmental impact coefficient Xs. The data processing module calculates the pollutant concentration fluctuation value Ndbd according to the real-time pollutant concentration qWrnd after cleaning. The data processing module sends these data to the data analysis module through the network; The data analysis module presets a useful power threshold value Ydl yz , fuel usage threshold Rlyl yz , Water consumption threshold Ysl yz And the pollutant concentration fluctuation threshold Ndbd yz The data analysis module compares the power consumption Ydl, fuel consumption Rlyl, water consumption Ysl and pollutant concentration fluctuation value Ndbd with the corresponding threshold values ​​respectively, and sends the comparison result to the output module through the network. When any value is abnormal, the output module sends the abnormal signal to the early warning module through the network; The early warning module sends an early warning signal to the staff.

2. A green highway construction energy consumption monitoring and early warning system according to claim 1, characterized in that: The real-time current data set expression is {D11, D12, ..., D1 n }, the real-time voltage data set expression is {Dy1, Dy2, ..., Dy n }, the real-time fuel flow data set expression is {Rlll1, Rlll2, ..., Rlll n }, the real-time water flow data set expression is {Ysll1, Ysll2, ..., Ysll n }, the real-time pollutant concentration data set expression is {Wrnd1, Wrnd2, ..., Wrnd n }, and the number of data in each data set is the same, the collection period is the same, and the time interval between each data in the data set is the same. The subscript n in each data set means that there are n data in each data set.

3. A green highway construction energy consumption monitoring and early warning system according to claim 2, characterized in that: The real-time current qD1 algorithm expression after cleaning is as follows: In the formula, i=1 in the table means that the calculation starts from the first data in the real-time current data set, i.e., Dl1, and n means that the calculation ends at the nth data in the data set, i.e., until Dl n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time current qDl after cleaning; The algorithm expression of the real-time voltage qDy after cleaning is as follows: In the formula, i=1 in the table means that the calculation starts from the first data in the real-time voltage data set, that is, Dy1, and n means that the calculation ends at the nth data in the data set, that is, Dy1. n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time voltage qDy after cleaning; The real-time fuel flow rate qR111 after cleaning is expressed as follows: In the formula, i=1 in the formula indicates that the calculation starts from the first data in the real-time fuel flow data set, i.e., R111, and n indicates that the calculation ends at the nth data in the data set, i.e., R112. n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time fuel flow rate qR111 after cleaning; The real-time water flow rate qYsll after cleaning is expressed by the following algorithm: In the formula, i=1 in the table means that the calculation starts from the first data in the real-time water flow data set, that is, Ysll1, and n means that the calculation ends at the nth data in the data set, that is, Ysll. n So far, N in the denominator means that there are N data in the data set, and the result obtained by calculation is the mean of all data in the data set after summing up, which is the real-time water flow rate qYsll after cleaning; The algorithm expression of the real-time pollutant concentration qWrnd after cleaning is as follows: In the formula, i=1 in the table indicates that the calculation starts from the first data in the real-time pollutant concentration data set, that is, Wrnd1, and n indicates that the calculation ends at the nth data in the data set, that is, Wrnd n So far, N in the denominator means that there are N data in the data set, and the calculated result is the mean of all the data in the data set after summing up, which is the real-time pollutant concentration qWrnd after cleaning.

4. A green highway construction energy consumption monitoring and early warning system according to claim 3, characterized in that: The power consumption Ydl algorithm expression is as follows: Ydl=qDl*qDy*Glys*Yxsj*Xs In the formula, Ydl represents power consumption, qDl represents real-time current after cleaning, qDy represents real-time voltage after cleaning, Glys represents power factor, Yxsj represents operating time, and Xs represents environmental impact coefficient.

5. A green highway construction energy consumption monitoring and early warning system according to claim 4, characterized in that: The fuel consumption Rlyl algorithm expression is as follows: Rlyl=qRlll*Yxsj*Xs In the formula, Rlyl represents the fuel consumption, qRlll represents the real-time fuel flow after cleaning, Yxsj represents the operating time, and Xs represents the environmental impact coefficient.

6. A green highway construction energy consumption monitoring and early warning system according to claim 5, characterized in that: The water consumption Ysl algorithm expression is as follows: Ysl=qYsll*Yxsj*Xs In the formula, Ysl represents the water consumption, qYsll represents the real-time water flow after cleaning, Yxsj represents the operating time, and Xs represents the environmental impact coefficient.

7. A green highway construction energy consumption monitoring and early warning system according to claim 6, characterized in that: The pollutant concentration fluctuation value Ndbd algorithm expression is as follows: Ndbd=|qWrnd-qWrnd q | In the formula, qWrnd q Indicates the real-time pollutant concentration after cleaning qWrnd calculated this time, which is the real-time pollutant concentration after cleaning calculated by the algorithm for the previous pollutant concentration data set of the current pollutant concentration data set. |qWrnd-qWrnd q | indicates the absolute value of the difference between the two.

8. A green highway construction energy consumption monitoring and early warning system according to claim 7, characterized in that: The data analysis module compares the power consumption Ydl, fuel consumption Rlyl and water consumption Ysl with the power consumption threshold Ydl yz , fuel usage threshold Rlyl yz And the water consumption threshold Ysl yz For comparison, when the electricity consumption Ydl exceeds the corresponding threshold, it is determined that the construction equipment is operating abnormally or is overused. When the fuel consumption Rlyl exceeds the corresponding threshold, it is determined that the construction machinery is inefficient or the fuel is not burned completely. When the water consumption Ysl exceeds the corresponding threshold, it is determined that water leakage or improper water resource management occurs during the construction process.

9. A green highway construction energy consumption monitoring and early warning system according to claim 8, characterized in that: The data analysis module compares the pollutant concentration fluctuation value Ndbd with the pollutant concentration fluctuation value threshold Ndbd yz By comparison, when the pollutant concentration fluctuation value Ndbd exceeds the corresponding threshold, it is determined that the pollution source emission is unstable or the emission source equipment fails.

10. A green highway construction energy consumption monitoring and early warning system according to claim 9, characterized in that: The early warning module includes a display end and an audio end. The display end is used to display factors exceeding corresponding thresholds, and the audio end is used to play factors exceeding corresponding thresholds, so as to assist staff to quickly find abnormal factors and take corresponding measures.

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