A construction equipment energy efficiency management method and system based on the Internet of Things

Abstract

The present invention belongs to the technical field of construction equipment management. The present invention provides an energy efficiency management method and system for construction equipment based on the Internet of Things. Within a historical period, high and low energy efficiency periods of construction equipment are identified, and the low energy efficiency periods are identified. Then, whether the low energy efficiency periods corresponding to the construction equipment are stable within multiple historical periods is analyzed. This not only helps to discover the causes of low energy efficiency of construction equipment and provides data support for subsequent energy efficiency management of construction equipment, but also helps management personnel to fully understand the stability status of low energy efficiency periods of construction equipment. According to the stability status of the analyzed low energy efficiency periods, a monitoring management window is set, and the monitoring and management strategy of the low energy efficiency period is adjusted and optimized, so as to dynamically adjust the monitoring window according to the actual situation, thereby improving the efficiency of monitoring and management of low energy efficiency periods of construction equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of construction equipment management, and specifically relates to an Internet of Things-based construction equipment energy efficiency management method and system. Background Art

[0002] In the construction industry, energy efficiency management of construction equipment is crucial. On the one hand, construction equipment is of various types and consumes huge amounts of energy. Unreasonable energy efficiency management will lead to energy waste and increase construction costs. On the other hand, the instability of equipment energy efficiency may also affect construction progress and quality, and even cause safety hazards.

[0003] In existing technologies, it is impossible to accurately identify the energy efficiency of construction equipment at different times, making it difficult to perform stability analysis on the low-efficiency periods of construction equipment. This results in an inability to make targeted adjustments based on the construction equipment, resulting in inefficient energy efficiency management of construction equipment. Furthermore, there is a lack of early warning of future low-efficiency periods for construction equipment, making it impossible to provide managers with effective adjustment time recommendations in advance, resulting in a delay in optimizing the equipment when it is operating at low energy efficiency.

[0004] This application: By identifying low-energy-efficiency periods and then analyzing whether the corresponding low-energy-efficiency periods of construction equipment are stable over multiple historical cycles, it helps to discover the causes of low energy efficiency of construction equipment. It also allows subsequent management personnel to fully understand the stability of low-energy-efficiency periods of construction equipment, set monitoring windows, and improve the efficiency of monitoring and management of low-energy-efficiency periods of construction equipment.

[0005] At the same time, based on the monitoring and management window, the real-time energy efficiency of the current construction equipment is monitored in real time, and combined with the historical energy efficiency values ​​of the construction equipment in the monitoring and management window within the historical period, by constructing a change curve and comparing and analyzing the slope of the curve, the early warning reference curve is screened out from multiple historical periods, and an early warning adjustment time model is constructed to obtain the early warning adjustment time, so as to predict in advance the time period when the equipment may have low energy efficiency, and provide corresponding adjustment time suggestions to help managers take measures in advance to adjust energy efficiency, optimize equipment operation, and improve energy utilization efficiency.

[0006] To this end, the present invention provides a method and system for energy efficiency management of construction equipment based on the Internet of Things. Summary of the Invention

[0007] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0008] The technical solution adopted by the present invention to solve its technical problem is:

[0009] In a first aspect, a method for energy efficiency management of construction equipment based on the Internet of Things comprises:

[0010] Obtain historical energy efficiency values ​​of building equipment over a historical period and conduct energy efficiency analysis to identify periods of low energy efficiency;

[0011] Conduct stability analysis on low energy efficiency periods within multiple historical cycles to assess whether the low energy efficiency periods corresponding to construction equipment are stable;

[0012] Based on the evaluated stability results, monitor and manage the low energy efficiency period and set the monitoring and management window;

[0013] Based on the monitoring and management window, real-time energy efficiency change curves and reference energy efficiency change curves are constructed respectively, and combined comparative analysis is carried out to build an early warning adjustment time model to carry out early warning management for low energy efficiency periods.

[0014] As a further solution of the present invention, the process of identifying the low energy efficiency period is as follows:

[0015] The historical period is evenly divided into several historical periods. The historical energy efficiency value of the construction equipment in each historical period is obtained and compared with the preset effective energy efficiency value. If the historical energy efficiency value is less than the preset energy efficiency value, it is recorded as a low energy efficiency period.

[0016] As a further solution of the present invention, when performing stability analysis on low energy efficiency periods in multiple historical cycles, a low energy interval stability value is obtained, and the process is as follows:

[0017] In the historical period, the time intervals between adjacent low-energy-efficiency periods are obtained and averaged, and the periodic low-energy time interval value is output;

[0018] The periodic low-energy time interval values ​​corresponding to multiple historical periods are processed by calculating the coefficient of variation, and the stable value of the low-energy time interval is output.

[0019] As a further solution of the present invention, when performing stability analysis on low energy efficiency periods in multiple historical cycles, the wave width deviation value is obtained, and the process is as follows:

[0020] The overlapping low energy efficiency period is evenly divided into several historical monitoring nodes, the historical energy efficiency value corresponding to each historical monitoring node is obtained, and the low energy efficiency change curve is constructed;

[0021] Cut off the local curves corresponding to all peaks and troughs on the low energy efficiency change curve respectively,

[0022] The local curve of the peak is processed by the coordinate distance calculation formula to calculate the peak width and peak amplitude respectively;

[0023] The local curve of the trough is processed by the coordinate distance calculation formula, and the trough width and trough amplitude are obtained by analysis and calculation;

[0024] Combine adjacent peaks and valleys on the low-efficiency change curve to obtain multiple peak-valley combinations, and process them separately using the Manhattan distance formula to output the width deviation value and amplitude deviation value;

[0025] The wave width deviation value and the wave amplitude deviation value are summed and the variance is calculated to obtain the low-energy stability value.

[0026] As a further solution of the present invention, it is to evaluate whether the low energy efficiency period corresponding to the construction equipment is stable, and the process is as follows:

[0027] The low-energy time interval stability value and the low-energy stability degree value are summed to output a stability evaluation value;

[0028] If the stability evaluation value is greater than the stability evaluation threshold, a low energy efficiency stability signal is generated;

[0029] If the stability evaluation value is less than or equal to the stability evaluation threshold, a low energy efficiency fluctuation signal is generated.

[0030] As a further solution of the present invention, a monitoring management window is set up, and the process is as follows:

[0031] If a low energy efficiency stable signal is generated, a historical period is randomly selected from multiple historical periods as a historical target period, and the time interval between adjacent low energy efficiency periods in the historical target period is obtained as a monitoring management window;

[0032] If a low energy efficiency fluctuation signal is generated, a historical period is randomly selected from multiple historical periods. Within the historical period, the time intervals between all adjacent low energy efficiency periods are obtained, and the sizes are compared. The shortest time interval between adjacent low energy efficiency periods is extracted as the management window to be monitored, and the smallest management window to be monitored is extracted as the monitoring management window.

[0033] As a further solution of the present invention, a real-time energy efficiency change curve and a reference energy efficiency change curve are constructed respectively, and the process is as follows:

[0034] The current real-time monitoring time of the construction equipment is obtained as the current monitored time, and the ratio is calculated with the total duration of the monitoring management window to output the reliable comparison time ratio. If the reliable comparison time ratio is greater than or equal to the reliable comparison time threshold, a monitoring time reliability signal is generated. The duration of the monitoring management window is multiplied by the reliable comparison time ratio to output the reliable comparison time, which is evenly divided into several reliable comparison monitoring points.

[0035] Obtain the real-time energy efficiency value of current construction equipment at each reliable comparison monitoring point and construct a real-time energy efficiency change curve;

[0036] The monitoring management window within the historical period is equally divided into several monitoring sub-windows, the energy efficiency value of each monitoring sub-window and the historical energy efficiency value in each historical monitoring sub-window are obtained, the historical energy efficiency change curve is constructed, and the local historical energy efficiency change curve within the reliable comparison time is intercepted as the reference energy efficiency change curve.

[0037] As a further solution of the present invention, the construction process of the early warning adjustment time model is as follows:

[0038] Based on the historical monitoring sub-window and the monitoring management sub-window, several reference sub-curves and real-time sub-curves are obtained, and the reference sub-slopes and real-time sub-slopes are obtained respectively. All reference sub-slopes and all corresponding real-time sub-slopes are input into the Manhattan distance formula, and the curve trend comparison value is output;

[0039] The reference energy efficiency change curves corresponding to the monitoring and management windows in all historical periods are counted and compared with the real-time energy efficiency change curves for trend changes. The historical energy efficiency change curve corresponding to the minimum curve trend comparison value is extracted, all real-time sub-slopes are averaged, and the reference slope is output. Combined with the end point coordinates of the real-time energy efficiency change curve, an early warning adjustment time model is constructed.

[0040] As a further solution of the present invention, early warning management is performed during low energy efficiency periods, and the process is as follows:

[0041] The historical energy efficiency values ​​corresponding to the low energy efficiency periods in all historical cycles are averaged to obtain the low energy efficiency reference value;

[0042] The low-efficiency energy efficiency reference value is input into the early warning adjustment time model, and the early warning adjustment time is output.

[0043] In the second aspect, an energy efficiency management system for construction equipment based on the Internet of Things includes:

[0044] Low energy efficiency identification module: obtains the historical energy efficiency values ​​of building equipment in the historical period, performs energy efficiency analysis, and identifies low energy efficiency periods;

[0045] Low energy efficiency assessment module: This module conducts stability analysis on low energy efficiency periods within multiple historical cycles to assess whether the low energy efficiency periods corresponding to construction equipment are stable;

[0046] Monitoring window setting module: Based on the evaluated stability results, monitor and manage the low energy efficiency period and set the monitoring management window;

[0047] Model construction and early warning module: Based on the monitoring and management window, the real-time energy efficiency change curve and the reference energy efficiency change curve are constructed respectively, and combined comparative analysis is carried out to build an early warning adjustment time model to carry out early warning management for low energy efficiency periods.

[0048] The beneficial effects of the present invention are as follows:

[0049] The present invention identifies high and low energy efficiency periods of construction equipment within a historical period, identifies the low energy efficiency period, and then analyzes whether the low energy efficiency period corresponding to the construction equipment is stable within multiple historical periods. This not only helps to discover the cause of the low energy efficiency of the construction equipment and provides data support for subsequent energy efficiency management of the construction equipment, but also helps management personnel to fully understand the stability of the low energy efficiency period of the construction equipment, and provides an important basis for formulating targeted energy efficiency management strategies. In addition, according to the stability of the analyzed low energy efficiency period, a monitoring and management window is set, and the monitoring and management strategy of the low energy efficiency period is adjusted and optimized, so as to dynamically adjust the monitoring window according to the actual situation, thereby improving the efficiency of monitoring and management of the low energy efficiency period of the construction equipment.

[0050] The present invention is based on the monitoring and management window, monitors the real-time energy efficiency of the current construction equipment in real time, and combines the historical energy efficiency values ​​of the construction equipment in the monitoring and management window in the historical period. By constructing a change curve and comparing and analyzing the slope of the curve, the early warning reference curve is screened out from multiple historical periods, and the difference between the current energy efficiency change trend and the historical situation is discovered in time. The similarity between the real-time energy efficiency change curve and the historical energy efficiency change curve is quantified, and more accurate reference data for the early warning work of the low energy efficiency period of the construction equipment is screened out. Based on the reference slope corresponding to the early warning reference curve and combined with the real-time energy efficiency change curve, an early warning adjustment time model is constructed to obtain the early warning adjustment time, thereby predicting in advance the period when the equipment may have low energy efficiency, and providing corresponding adjustment time suggestions to help management personnel take measures to adjust energy efficiency in advance, optimize equipment operation, and improve energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below with reference to the accompanying drawings.

[0052] Figure 1 This is a flowchart of the steps of a method for energy efficiency management of construction equipment based on the Internet of Things of the present invention;

[0053] Figure 2 This is a schematic diagram of an energy efficiency management system for construction equipment based on the Internet of Things according to the present invention. DETAILED DESCRIPTION

[0054] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0055] Example 1

[0056] See also Figure 1 As shown, the method for energy efficiency management of construction equipment based on the Internet of Things according to an embodiment of the present invention includes the following steps:

[0057] Step 1: Obtain the historical energy efficiency values ​​of construction equipment within the historical period, conduct high and low energy efficiency analysis, and identify high and low energy efficiency periods;

[0058] In some embodiments, the historical period is equally divided into a number of historical time periods;

[0059] It should be noted that the duration of each divided historical period is equal;

[0060] Use smart sensors to obtain the historical energy efficiency values ​​of construction equipment in each historical period and compare them with the preset effective energy efficiency values. The process is as follows:

[0061] If the historical energy wash value is greater than or equal to the preset energy efficiency value, it means that the effective energy efficiency of the construction equipment is high during the analyzed historical period, and it is marked as a high energy efficiency period;

[0062] If the historical energy efficiency value is less than the preset energy efficiency value, it means that the effective energy efficiency of the construction equipment in the analyzed historical period is low, and it is marked as a low energy efficiency period;

[0063] It should be noted that the historical energy efficiency value means: quantifying the efficiency of energy utilization of construction equipment in previous cycles;

[0064] Step 2: Conduct stability analysis on low energy efficiency periods within multiple historical cycles to assess whether the low energy efficiency periods corresponding to construction equipment are stable and obtain stability assessment results;

[0065] In some embodiments, within a historical period, time intervals between adjacent low energy efficiency periods are obtained, and averaged, and a periodic low energy interval value is output;

[0066] The periodic low-energy interval values ​​corresponding to multiple historical periods are processed by calculating the coefficient of variation to output the stable value of the low-energy interval. , the process is as follows:

[0067] The periodic low-energy time interval values ​​corresponding to multiple historical periods are averaged and the low-energy time interval average is output;

[0068] Calculate the standard deviation of the periodic low-energy interval values ​​corresponding to multiple historical periods, and output the low-energy interval standard deviation value;

[0069] The coefficient of variation is calculated using the formula: , the low energy time interval stability value is calculated ,in, Expressed as the standard deviation of the low-energy time interval, It is expressed as the mean of low-energy time intervals;

[0070] The low energy efficiency period is evenly divided into several historical monitoring nodes, where the time intervals between adjacent historical monitoring nodes are equal;

[0071] Use smart sensors to obtain the historical energy efficiency values ​​corresponding to each historical monitoring node, and construct a low energy efficiency change curve with the X-axis as time and the Y-axis as historical energy efficiency values;

[0072] intercepting the local curves corresponding to all peaks and troughs on the low energy efficiency change curve respectively;

[0073] For example, taking the local curve corresponding to the peak as an example, the endpoint coordinates of the local curve are obtained, and the peak width is obtained by using the coordinate distance calculation formula;

[0074] Taking the local curve corresponding to the trough as an example, obtain the endpoint coordinates of the local curve and use the coordinate distance calculation formula to obtain the trough width;

[0075] It should be noted that the endpoint coordinates of the local curve corresponding to the peak are the coordinates of the adjacent trough on the low energy efficiency change curve, and the endpoint coordinates of the local curve corresponding to the trough are the coordinates of the adjacent peak on the low energy efficiency change curve;

[0076] For example, taking the local curve corresponding to the peak as an example, the endpoint coordinates of the local curve and the vertex coordinates of the local curve are obtained, and after calculation using the coordinate distance calculation formula, the average processing is performed to obtain the peak amplitude ;

[0077] It should be noted that the endpoint coordinates of the local curve corresponding to the peak are the coordinates of the adjacent troughs on the low energy efficiency change curve, and the vertex coordinates of the local curve are the coordinates of the peak between the adjacent troughs on the local curve;

[0078] Specifically, ;

[0079] ;

[0080] ;

[0081] in,( , )、( , ) are the endpoint coordinates of the local curve corresponding to the peak, ( , ) are the vertex coordinates of the local curve, and They are respectively expressed as the distance between the peak vertex and the peak endpoint coordinates;

[0082] Similarly, taking the local curve corresponding to the trough as an example, the endpoint coordinates of the local curve and the vertex coordinates of the local curve are obtained, and after calculation using the coordinate distance calculation formula, the mean value is solved to obtain the valley amplitude. ;

[0083] Specifically, ;

[0084] ;

[0085] ;

[0086] in,( , )、( , ) are the endpoint coordinates of the local curve corresponding to the peak, ( , ) are the vertex coordinates of the local curve, and They are respectively expressed as the distance between the peak vertex and the peak endpoint coordinates;

[0087] Combining adjacent peaks and valleys on the low energy efficiency change curve to obtain multiple peak-valley combinations;

[0088] For example, if there are peak A, trough B, peak C, and trough D on the low energy efficiency change curve, then the adjacent peak A and trough B form a peak-trough combination, the adjacent trough B and peak C form a peak-trough combination, and the adjacent peak C and trough D form a peak-trough combination;

[0089] The peak widths and valley widths corresponding to the multiple peak-valley combinations are input into the Manhattan distance calculation formula respectively, and the output is the width deviation value;

[0090] Specifically, the Manhattan distance formula is: , calculate the width deviation value ,in, It is expressed as the peak width in the mth peak-valley combination, It is expressed as the trough width in the mth peak-trough combination, Expressed as the total number of peak-valley combinations;

[0091] The peak amplitudes and valley amplitudes corresponding to the multiple peak-valley combinations are input into the Manhattan distance calculation formula respectively, and the amplitude deviation value is output;

[0092] Specifically, the Manhattan distance formula is: , calculate the amplitude deviation value ,in, Expressed as the peak amplitude within the mth peak-to-valley combination, is the valley amplitude within the mth peak-valley combination, Expressed as the total number of peak-valley combinations;

[0093] In detail, the role of using the Manhattan distance formula is:

[0094] Function 1: Manhattan distance can measure the distance and change trend between peaks and valleys, which helps to comprehensively evaluate whether the energy efficiency changes of construction equipment during low energy efficiency periods are stable. It also provides data support for discovering the causes of low energy efficiency and subsequent energy efficiency management of construction equipment.

[0095] Function 2: Quantifying the characteristics of peaks and troughs in the low energy efficiency change curve helps analyze the amplitude and frequency of energy efficiency changes. It also reflects the duration of energy efficiency conversion between high and low during the low energy efficiency period, making it easier to understand the fluctuation of energy efficiency changes of construction equipment during the low energy efficiency period.

[0096] The width deviation value and the amplitude deviation value are summed to obtain the period stability value, and the period stability values ​​corresponding to all low energy efficiency periods are calculated for variance, and the low energy stability value is output;

[0097] The low-energy time interval stability value and the low-energy stability degree value are summed to output a stability evaluation value;

[0098] It can be understood that the stability assessment value means: a comprehensive reflection of the overall stability of construction equipment during periods of low energy efficiency. On the one hand, the low-energy interval stability value reflects the stability of the time intervals of low energy efficiency periods in multiple historical cycles. On the other hand, the low-energy stability value reflects the changes in energy efficiency during low energy efficiency periods in multiple historical cycles. This helps managers fully understand the stability of construction equipment during periods of low energy efficiency and provides an important basis for formulating targeted energy efficiency management strategies.

[0099] The stability assessment value is compared with the stability assessment threshold as follows:

[0100] If the stability evaluation value is greater than the stability evaluation threshold, it means that the time intervals of low energy efficiency periods in multiple historical cycles are relatively stable, and the low energy efficiency level in the low energy efficiency period is relatively stable, generating a low energy efficiency stability signal;

[0101] If the stability evaluation value is less than or equal to the stability evaluation threshold, it means that the time intervals of low energy efficiency periods in multiple historical cycles are relatively unstable, and the low energy efficiency levels in the low energy efficiency periods are relatively unstable, generating a low energy efficiency fluctuation signal;

[0102] Step 3: Based on the stability assessment results, set the monitoring management window and adjust and optimize the monitoring management strategy for low energy efficiency periods;

[0103] In some embodiments, if a low energy efficiency stable signal is generated, a historical period is randomly selected from a plurality of historical periods as a historical target period;

[0104] During the historical target period, the time interval between adjacent low-energy-efficiency periods is obtained as a monitoring and management window;

[0105] If a low energy efficiency fluctuation signal is generated, a historical period is randomly selected from multiple historical periods. Within the historical period, the time intervals between all adjacent low energy efficiency periods are obtained and compared. The shortest time interval between adjacent low energy efficiency periods is extracted as the management window to be monitored.

[0106] Compare the sizes of the management windows to be monitored corresponding to all historical periods, and extract the smallest management window to be monitored as the monitoring management window;

[0107] Based on the monitoring and management windows obtained, implement a management strategy for monitoring and managing the low energy efficiency periods of construction equipment;

[0108] The specific scheme of the embodiment of the present invention is: within the historical period, the high and low energy efficiency periods of construction equipment are identified, the low energy efficiency periods are identified, and then whether the low energy efficiency periods corresponding to the construction equipment are stable in multiple historical periods are analyzed. This not only helps to discover the causes of the low energy efficiency of the construction equipment, provides data support for the subsequent energy efficiency management of the construction equipment, but also helps management personnel to fully understand the stability status of the low energy efficiency periods of the construction equipment, and provides an important basis for formulating targeted energy efficiency management strategies. In addition, according to the stability of the analyzed low energy efficiency periods, a monitoring and management window is set, and the monitoring and management strategy of the low energy efficiency period is adjusted and optimized, so as to dynamically adjust the monitoring window according to the actual situation, thereby improving the efficiency of monitoring and management of the low energy efficiency periods of construction equipment.

[0109] Example 2

[0110] See also Figure 1 As shown, the energy efficiency management method of construction equipment based on the Internet of Things according to an embodiment of the present invention includes:

[0111] Step 4: Based on the monitoring and management window, monitor the real-time energy efficiency of current construction equipment in real time, and conduct comparative analysis based on the historical energy efficiency of construction equipment within the monitoring and management window over the historical period, build an early warning adjustment time model, and complete the early warning management work for the low energy efficiency period of construction equipment;

[0112] In some embodiments, the time of current real-time monitoring of the construction equipment is obtained as the current monitored time;

[0113] Calculate the ratio of the current monitored time to the total duration of the monitoring management window, and output the reliable comparison time ratio;

[0114] Compare the reliable comparison time ratio with the reliable comparison time threshold. The process is as follows:

[0115] If the reliable comparison time ratio is greater than or equal to the reliable comparison time threshold, it means that the monitoring time of the current construction equipment is relatively reliable, and a monitoring time reliability signal is generated;

[0116] If the reliable comparison time is less than the reliable comparison time threshold, it means that the monitoring time of the current construction equipment is relatively unreliable, and monitoring needs to be continued until the reliable comparison time ratio is greater than or equal to the reliable comparison time threshold;

[0117] It should be noted that the reliable comparison time threshold is set by those skilled in the art;

[0118] It should be noted that the purpose of setting a reliable comparison time is: if the current monitoring time is too short, for example, the monitoring time of the current construction equipment is 10 seconds, when comparing the changes in real-time monitoring data within 10 seconds with the changes in historical data, the constructed early warning adjustment time model will have errors when performing prediction operations, and the accuracy will be low;

[0119] Multiply the duration of the monitoring management window by the proportion of the reliable comparison time to obtain the reliable comparison time.

[0120] Based on the reliable signal of monitoring time, the reliable comparison time is equally divided into several reliable comparison monitoring points;

[0121] It should be noted that the time intervals between each reliable comparison monitoring point are equal;

[0122] Through intelligent sensors, the real-time energy efficiency value of current construction equipment at each reliable comparison monitoring point is monitored in real time. The X-axis is time and the Y-axis is real-time energy efficiency value, and a real-time energy efficiency change curve is constructed;

[0123] Randomly select a monitoring management window within a historical period and divide it equally into several historical monitoring sub-windows;

[0124] Extract the historical energy efficiency values ​​within each historical monitoring sub-window, use the X-axis as time and the Y-axis as historical energy efficiency values ​​to construct a historical energy efficiency change curve;

[0125] The reliable comparison time is obtained by multiplying the monitoring management window in the historical period corresponding to the historical energy efficiency change curve by the proportion of the reliable comparison time. The local historical energy efficiency change curve within the reliable comparison time is intercepted as the reference energy efficiency change curve.

[0126] Count the reference energy efficiency change curves corresponding to the monitoring management windows in all historical periods;

[0127] The real-time energy efficiency change curve is compared with the reference energy efficiency change curve corresponding to the monitoring management window in all historical periods for trend changes. The process is as follows:

[0128] S1, arbitrarily extract the corresponding reference energy efficiency change curve within a historical period;

[0129] S2, taking the local curve between adjacent coordinates on the reference energy efficiency change curve as a reference sub-curve, and obtaining the slope corresponding to each reference sub-curve as a reference sub-slope;

[0130] The local curve between adjacent coordinates on the real-time energy efficiency change curve is used as a real-time sub-curve, and the slope corresponding to each real-time sub-curve is obtained as the real-time sub-slope;

[0131] S3: Combine the reference sub-slope with the corresponding real-time sub-slope to obtain multiple slope analysis groups, input the reference sub-slope and the real-time sub-slope in the multiple slope analysis groups into the Manhattan distance formula, and output the curve trend comparison value ;

[0132] Specifically, ;

[0133] in, Expressed as the number of slope analysis groups, Expressed as The reference sub-slope corresponding to the reference sub-curve within the slope analysis group, Expressed as The real-time sub-slope corresponding to the real-time sub-curve within each slope analysis group;

[0134] It should be noted that the reference subslope and the real-time subslope within the slope analysis group are consistent in the time dimension;

[0135] For example, if the time of the reference sub-curve corresponding to the reference sub-slope in the slope analysis group on the reference energy efficiency change curve is 8:30-9:00, then the time of the real-time sub-curve corresponding to the real-time sub-slope in the slope analysis group on the real-time energy efficiency change curve is 8:30-9:00;

[0136] It can be understood that the meaning of the curve trend comparison value is that the curve trend comparison value can quantify the similarity between the real-time energy efficiency change curve and the historical energy efficiency change curve within the reliable comparison time, reflecting the consistency between the current construction equipment energy efficiency change trend and the energy efficiency change trend in the past historical period, and extracting the historical energy efficiency change curve with a high degree of consistency as a reference, providing time data support for warning the energy efficiency adjustment of construction equipment in the future low energy efficiency period;

[0137] S4, comparing the curve trend comparison values ​​after comparing each historical energy efficiency change curve with the real-time energy efficiency change curve, and extracting the historical energy efficiency change curve corresponding to the minimum curve trend comparison value as the early warning reference curve;

[0138] On the early warning reference curve, all reference sub-slopes are averaged and output as the reference slope;

[0139] Based on the reference slope, the end point coordinates of the real-time energy efficiency change curve are extracted to build an early warning adjustment time model. ,in, Expressed as the reference slope, Expressed as a constant;

[0140] The historical energy efficiency values ​​corresponding to the low energy efficiency periods in all historical cycles are averaged to obtain the low energy efficiency reference value;

[0141] Input the low-efficiency energy efficiency reference value into the early warning adjustment time model, and output the early warning adjustment time;

[0142] The specific scheme of this embodiment is: based on the monitoring and management window, the real-time energy efficiency of the current construction equipment is monitored in real time, and combined with the historical energy efficiency values ​​of the construction equipment in the monitoring and management window in the historical period, by constructing a change curve and comparing and analyzing the slope of the curve, the early warning reference curve is screened out from multiple historical periods, and the difference between the current energy efficiency change trend and the historical situation is discovered in time, and the similarity between the real-time energy efficiency change curve and the historical energy efficiency change curve is quantified, and more accurate reference data for the early warning work of the low energy efficiency period of the construction equipment is screened out. Based on the reference slope corresponding to the early warning reference curve, combined with the real-time energy efficiency change curve, an early warning adjustment time model is constructed to obtain the early warning adjustment time, thereby predicting in advance the period when the equipment may have low energy efficiency, and providing corresponding adjustment time suggestions to help management personnel take measures to adjust energy efficiency in advance, optimize equipment operation, and improve energy utilization efficiency.

[0143] Example 2

[0144] Based on the same inventive concept as the method and system for energy efficiency management of construction equipment based on the Internet of Things in the aforementioned embodiment, Figure 2 As shown, the present application provides an energy efficiency management system for construction equipment based on the Internet of Things, wherein the system specifically includes:

[0145] Low energy efficiency identification module: obtains the historical energy efficiency values ​​of building equipment in the historical period, performs energy efficiency analysis, and identifies low energy efficiency periods;

[0146] Low energy efficiency assessment module: This module conducts stability analysis on low energy efficiency periods within multiple historical cycles to assess whether the low energy efficiency periods corresponding to construction equipment are stable;

[0147] Monitoring window setting module: Based on the evaluated stability results, monitor and manage the low energy efficiency period and set the monitoring management window;

[0148] Model construction early warning module: Based on the monitoring and management window, it monitors the real-time energy efficiency of current construction equipment in real time, and combines the historical energy efficiency of construction equipment in the monitoring and management window within the historical period to build an early warning adjustment time model to carry out early warning management for low energy efficiency periods.

[0149] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for energy efficiency management of construction equipment based on the Internet of Things, characterized by: include: Obtain historical energy efficiency values ​​of building equipment over a historical period and conduct energy efficiency analysis to identify periods of low energy efficiency; Conduct stability analysis on low energy efficiency periods within multiple historical cycles to assess whether the low energy efficiency periods corresponding to construction equipment are stable; In the historical period, the time intervals between adjacent low-energy-efficiency periods are obtained and averaged, and the periodic low-energy time interval value is output; The periodic low-energy interval values ​​corresponding to multiple historical periods are processed by calculating the coefficient of variation, and the stable value of the low-energy interval is output; The low energy efficiency period is evenly divided into several historical monitoring nodes, the historical energy efficiency value corresponding to each historical monitoring node is obtained, and the low energy efficiency change curve is constructed; Cut off the local curves corresponding to all peaks and troughs on the low energy efficiency change curve respectively, The local curve of the peak is processed by the coordinate distance calculation formula to calculate the peak width and peak amplitude respectively; The local curve of the trough is processed by the coordinate distance calculation formula, and the trough width and trough amplitude are obtained by analysis and calculation; Combine adjacent peaks and troughs on the low-efficiency change curve, process them separately using the Manhattan distance formula, and output the width deviation value and amplitude deviation value; Calculate the width deviation value D c ,in, It is expressed as the peak width in the mth peak-valley combination, It is expressed as the trough width within the mth peak-trough combination, and m is the total number of peak-trough combinations; Calculate the amplitude deviation value D f ,in, Expressed as the peak amplitude within the mth peak-to-valley combination, is the valley amplitude within the mth peak-valley combination, and m represents the total number of peak-valley combinations; The width deviation value and the amplitude deviation value are summed to obtain the period stability value, and the period stability values ​​corresponding to all low energy efficiency periods are calculated for variance, and the low energy stability value is output; The low-energy time interval stability value and the low-energy stability degree value are summed to output a stability evaluation value; If the stability evaluation value is greater than the stability evaluation threshold, a low energy efficiency fluctuation signal is generated; If the stability evaluation value is less than or equal to the stability evaluation threshold, a low energy efficiency stability signal is generated; Based on the evaluated stability results, monitor and manage the low energy efficiency period and set the monitoring and management window; Based on the monitoring and management window, real-time energy efficiency change curves and reference energy efficiency change curves are constructed respectively, and combined comparative analysis is carried out to build an early warning adjustment time model to carry out early warning management for low energy efficiency periods.

2. The method for energy efficiency management of construction equipment based on the Internet of Things according to claim 1, characterized in that: The process for identifying periods of low energy efficiency is as follows: The historical period is evenly divided into several historical periods. The historical energy efficiency value of the construction equipment in each historical period is obtained and compared with the preset effective energy efficiency value. If the historical energy efficiency value is less than the preset energy efficiency value, it is recorded as a low energy efficiency period.

3. The method for energy efficiency management of construction equipment based on the Internet of Things according to claim 1, characterized in that: To set up the monitoring management window, the process is as follows: If a low energy efficiency stable signal is generated, a historical period is randomly selected from multiple historical periods as a historical target period, and the time interval between adjacent low energy efficiency periods in the historical target period is obtained as a monitoring management window; If a low energy efficiency fluctuation signal is generated, a historical period is randomly selected from multiple historical periods. Within the historical period, the time intervals between all adjacent low energy efficiency periods are obtained, and the sizes are compared. The shortest time interval between adjacent low energy efficiency periods is extracted as the management window to be monitored, and the smallest management window to be monitored is extracted as the monitoring management window.

4. The method for energy efficiency management of construction equipment based on the Internet of Things according to claim 1, characterized in that: The real-time energy efficiency change curve and the reference energy efficiency change curve are constructed respectively. The process is as follows: The current real-time monitoring time of the construction equipment is obtained as the current monitored time, and the ratio is calculated with the total duration of the monitoring management window to output the reliable comparison time ratio. If the reliable comparison time ratio is greater than or equal to the reliable comparison time threshold, a monitoring time reliability signal is generated, and the duration of the monitoring management window is multiplied by the reliable comparison time ratio to output the reliable comparison time, which is evenly divided into several reliable comparison monitoring points. Obtain the real-time energy efficiency value of current construction equipment at each reliable comparison monitoring point and construct a real-time energy efficiency change curve; The monitoring management window within the historical period is equally divided into several monitoring sub-windows, the historical energy efficiency value within each historical monitoring sub-window is obtained, the historical energy efficiency change curve is constructed, and the local historical energy efficiency change curve within the reliable comparison time is intercepted as the reference energy efficiency change curve.

5. The method for energy efficiency management of construction equipment based on the Internet of Things according to claim 1, characterized in that: The construction process of the early warning adjustment time model is as follows: Based on the historical monitoring sub-window and the monitoring management sub-window, several reference sub-curves and real-time sub-curves are obtained, and the reference sub-slopes and real-time sub-slopes are obtained respectively. All reference sub-slopes and all corresponding real-time sub-slopes are input into the Manhattan distance formula, and the curve trend comparison value is output; The reference energy efficiency change curves corresponding to the monitoring management windows in all historical periods are counted and compared with the real-time energy efficiency change curves for trend changes. The historical energy efficiency change curve corresponding to the minimum curve trend comparison value is extracted as the early warning reference curve. On the early warning reference curve, all reference sub-slopes are averaged and output as the reference slope. Combined with the end point coordinates of the real-time energy efficiency change curve, an early warning adjustment time model is constructed.

6. The method for energy efficiency management of construction equipment based on the Internet of Things according to claim 5, characterized in that: The early warning management process for low energy efficiency periods is as follows: The historical energy efficiency values ​​corresponding to the low energy efficiency periods in all historical cycles are averaged to obtain the low energy efficiency reference value; The low-efficiency energy efficiency reference value is input into the early warning adjustment time model, and the early warning adjustment time is output.

7. An Internet of Things-based construction equipment energy efficiency management system, implementing the steps of the Internet of Things-based construction equipment energy efficiency management method according to any one of claims 1 to 6, characterized in that: Low energy efficiency identification module: obtains the historical energy efficiency values ​​of building equipment in the historical period, performs energy efficiency analysis, and identifies low energy efficiency periods; Low energy efficiency assessment module: This module conducts stability analysis on low energy efficiency periods within multiple historical cycles to assess whether the low energy efficiency periods corresponding to construction equipment are stable; Monitoring window setting module: Based on the evaluated stability results, monitor and manage the low energy efficiency period and set the monitoring management window; Model construction and early warning module: Based on the monitoring and management window, the real-time energy efficiency change curve and the reference energy efficiency change curve are constructed respectively, and combined comparative analysis is carried out to build an early warning adjustment time model to carry out early warning management for low energy efficiency periods.

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