Energy consumption online monitoring system based on Internet of Things technology

Abstract

The invention discloses an online energy consumption monitoring system based on Internet of Things technology, and relates to the technical field of Internet of Things; the system comprises: a data acquisition module, a database, an energy consumption evaluation module and an optimization management module; by controlling data acquisition to provide accurate energy consumption information, and performing energy consumption trend analysis and peak analysis to obtain energy consumption status parameters, comprehensive data monitoring and analysis are provided for various energy consumptions of equipment in a factory area, and comprehensive monitoring and evaluation of energy consumption are achieved, which is helpful to improve the level of equipment management and energy consumption optimization, and provide technical support for energy conservation, emission reduction and sustainable development of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an online energy consumption monitoring system based on the Internet of Things technology. Background Art

[0002] With the rapid increase in energy consumption and the continuous rise in energy unit prices, the globalization trend of the market has led to increasingly fierce market competition. First, energy costs are usually an important expenditure in corporate operations. Second, energy consumption is closely related to environmental impact, especially for large energy-consuming factories. Third, market competition is fierce, and companies need to continuously improve efficiency and reduce costs to maintain their competitive advantage. Therefore, online monitoring of energy consumption is particularly important.

[0003] In factories, multiple energy sources such as electricity, natural gas, fuel and steam are usually used to meet production and operation needs; however, traditional energy monitoring systems often only monitor and manage a single type of energy and cannot provide a comprehensive understanding of the factory's comprehensive energy consumption, making it difficult to conduct comprehensive energy consumption analysis. Summary of the invention

[0004] Based on this, it is necessary to provide an online energy consumption monitoring system based on Internet of Things technology to address the problems mentioned in the above background technology.

[0005] The purpose of the present invention can be achieved by the following technical solutions: an online energy consumption monitoring system based on Internet of Things technology, the system comprising: a data acquisition module, a database, an energy consumption evaluation module and an optimization management module;

[0006] The energy consumption assessment module performs trend analysis and peak analysis based on the energy consumption of various types of energy of various equipment in the factory to obtain energy consumption status parameters for evaluating the energy consumption status of the equipment, wherein the energy consumption status parameters include the energy consumption trend deviation index, peak distribution value and total migration value of various types of energy;

[0007] The optimization management module further analyzes the energy consumption status of the equipment based on the energy consumption status parameters to measure the energy consumption status of the equipment, identify the potential risks of the equipment, and generate corresponding optimization management strategies; the specific process is as follows:

[0008] Set each device type to correspond to an energy consumption base value, retrieve the device type and compare it with all the set device types to match the corresponding energy consumption base value, which is recorded as W;

[0009] Retrieve the energy consumption trend deviation index KPi, peak distribution value Dhi and total migration value Vbi of various energy sources of the equipment, and use the set formula to calculate ... Calculate to obtain energy consumption anomaly index Ai of each type of energy, where u1, u2, u3, u4 are respectively set proportional coefficients, e is a natural constant, i=1, 2, 3...I, I represents the total number of energy types corresponding to the equipment, and i is any one of the energy types; compare and analyze the energy consumption anomaly index with the set anomaly interval to divide the various types of energy corresponding to the equipment into highly abnormal energy, moderately abnormal energy and low abnormal energy; count the number of highly abnormal energy, moderately abnormal energy and low abnormal energy in each type of energy corresponding to the equipment, and record them as Q1, Q2 respectively. and Q3; when Q1>Q2+Q3, an equipment maintenance instruction is generated and sent to the corresponding maintenance engineer for equipment maintenance; when the equipment maintenance completion signal is received, a re-collection instruction is sent to the data acquisition module for data collection and energy consumption status evaluation until Q1>Q2+Q3 is no longer satisfied; when Q3>Q1+Q2, no operation is required; in other cases except Q1>Q2+Q3 and Q3>Q1+Q2, the energy consumption status parameters, first-level energy consumption time period, second-level energy consumption time period and third-level energy consumption time period corresponding to the consumption of highly abnormal energy are integrated into an equipment energy consumption optimization report.

[0010] In some embodiments, the specific process of performing trend analysis on energy consumption of various energy sources is as follows:

[0011] Take any type of energy consumption of one of the devices, take time as the horizontal axis and energy consumption as the vertical axis to get the line graph of the energy consumption value of the device changing with time; draw a tangent line at the energy consumption point, use data fitting to get the expression of the tangent line, and perform a derivative operation on the tangent line expression to get the energy consumption derivative of each energy consumption point, record it as Sj (the energy consumption derivative Sij of each type of energy can be obtained); record the energy consumption derivative greater than zero as the positive energy consumption derivative, and sum them up to get the energy consumption increase degree, record it as K1; The energy consumption derivative less than zero is calculated by taking the absolute value to obtain the reverse energy consumption derivative, and the sum is calculated to obtain the energy consumption reduction degree and recorded as K2; the forward energy consumption derivative is compared and analyzed with the set forward interval to divide the forward energy consumption derivative into the first-level forward derivative, the second-level forward derivative and the third-level forward derivative, and the number of the first-level forward derivative, the second-level forward derivative and the third-level forward derivative is counted respectively, and recorded as P1, P2, P3 respectively; P1, P2, P3 and the energy consumption increase degree K1 are substituted into the set formula Calculation is performed to obtain the energy consumption increase deviation value Pa1 of the device, where a1, a2, and a3 are respectively set proportional coefficients;

[0012] Similarly, the reverse energy consumption derivative is compared and analyzed with the set reverse interval to divide the reverse energy consumption derivative into first-level reverse derivative, second-level reverse derivative and third-level reverse derivative; the number of first-level reverse derivative, second-level reverse derivative and third-level reverse derivative is counted respectively, and they are recorded as P4, P5 and P6 respectively; P4, P5 and P6 and the energy consumption reduction degree K2 are substituted into the set formula Calculation is performed to obtain the energy consumption reduction deviation value Pa2, where a4, a5, and a6 are respectively set proportional coefficients;

[0013] Substitute the energy consumption increase deviation value Pa1 and the energy consumption decrease deviation value Pa2 into the set formula Calculation is performed to obtain the energy consumption trend deviation index PK of the device, wherein a7 and a8 are respectively set proportional coefficients, and e is a natural constant; thus, the energy consumption trend deviation index PKi of each type of energy of the device can be obtained.

[0014] In some embodiments, the energy consumption assessment module is further used to analyze the energy consumption level of the device, and analyze the energy consumption data frequency requirement of the device in combination with the energy consumption derivative to generate a collection instruction; the specific process is:

[0015] The energy consumption corresponding to each device at different collection times is retrieved and analyzed in a formulaic manner to obtain the total consumption; the total consumption is compared and analyzed with the set consumption range to obtain the energy consumption level of each device;

[0016] Retrieve the energy consumption level, energy consumption derivative Sij and energy consumption trend deviation index Kpi of each device; set each energy consumption level to correspond to a collection base value, compare the energy consumption level of the device with all the set energy consumption levels to match the corresponding collection base value recorded as Y1; use the set formula Calculation is performed to obtain the acquisition frequency Yb, wherein b5, b6, and b7 are respectively set proportional coefficients; an acquisition instruction is generated according to the acquisition frequency, and the acquisition instruction is sent to the data acquisition module.

[0017] In some embodiments, the specific process of performing peak analysis on energy consumption of various energy sources is as follows:

[0018] 4-1: Take any type of energy consumption of any device, identify the energy consumption peak in the energy consumption over time curve and the start and end times of the energy consumption peak, calculate the start and end times of the energy consumption peak, and record the time difference between them to obtain the duration of the energy consumption peak as Cr; calculate the time difference between the end time of the previous energy consumption peak and the start time of the next energy consumption peak in two adjacent energy consumption peaks to obtain the peak interval as , where r=1, 2, 3...R, R is a positive integer, R represents the total number of energy consumption peaks, and r represents the sequence number of any energy consumption peak;

[0019] 4-2: Distribute the energy consumption peaks in the energy consumption versus time curve according to the peak interval and duration of the energy consumption peaks to obtain the peak distribution value;

[0020] 4-3: Overlap and compare the energy consumption curve of the same type of energy over time with the standard energy consumption curve to obtain the total migration value.

[0021] In some embodiments, the peak distribution value is obtained by distributing the energy consumption peaks in the energy consumption versus time curve according to the peak interval and duration of the energy consumption peaks; the specific process is:

[0022] Compare and analyze the duration with the set duration interval to divide the energy consumption peak corresponding to the duration into the first-level energy consumption period, the second-level energy consumption period and the third-level energy consumption period; count the number of the first-level energy consumption period, the second-level energy consumption period and the third-level energy consumption period respectively, and record them as D1, D2 and D3 respectively; compare and analyze the peak interval with the set interval to divide the peak interval into a long interval, a medium interval and a short interval; count the number of the long interval, the medium interval and the short interval respectively, and select the interval with the largest number as the regular interval, so that the number of regular intervals is recorded as D4, and the number of the other two intervals except the regular interval is recorded as D5 and D6;

[0023] The peak interval , duration Cr, number of first-level energy consumption periods D1, number of second-level energy consumption periods D2, number of third-level energy consumption periods D3, number of regular intervals D4, and number of the other two intervals D5 and D6 except the regular interval are substituted into the set formula Calculate to get the peak distribution value Dh, where h3, h4, h5, and h6 are the set proportional coefficients, is the average duration of each energy consumption peak, is the average duration of each energy consumption peak.

[0024] In some embodiments, the specific process of performing a comparison analysis on the energy consumption variation curve of the same type of energy over time and the standard energy consumption curve is as follows:

[0025] The time difference is calculated by calculating the time difference between the energy consumption change curve of the same type of energy and the time corresponding to the peak value of the energy consumption peak with the same serial number in the standard energy consumption curve to obtain the time difference. When the time difference is greater than zero, the energy consumption peak time migration is accumulated once, and the time difference is used as the time migration amount of this energy consumption peak time migration; the peak values ​​corresponding to the energy consumption peaks with the same serial number are calculated to obtain the peak difference. When the peak difference is greater than zero, the energy consumption peak migration is accumulated once, and the peak difference is used as the peak migration amount of this energy consumption peak migration; thus, the energy consumption of the same type of energy changes with time. All time migrations and peak migrations between the time change curve and the standard energy consumption curve are counted as Vn and Vm respectively; the number of energy consumption peak time migrations and energy consumption peak migrations in the overlapped comparison between the energy consumption change curve of the same type of energy and the standard energy consumption curve are counted respectively, and they are recorded as N and M respectively; N and M are both positive integers, and N and M∈R; n represents any migration in the number of energy consumption peak time migrations; m represents any migration in the number of energy consumption peak-peak migrations; Substitute Vn, Vm, N and M into the set formula The calculation is performed to obtain the total migration value Vb, wherein b1, b2, b3, and b4 are respectively set proportional coefficients.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. Provide accurate energy consumption information by controlling data collection, and conduct energy consumption trend analysis and peak analysis to obtain energy consumption status parameters, provide comprehensive data monitoring and analysis for various energy consumption of equipment in the factory, realize comprehensive monitoring and evaluation of energy consumption, help improve equipment management and energy consumption optimization, and provide technical support for energy conservation, emission reduction and sustainable development of enterprises;

[0028] 2. Through comprehensive analysis of energy consumption status parameters to determine the various energy consumption status of equipment, it is possible to accurately locate abnormal equipment and abnormal energy types, timely discover potential problems of equipment, and generate corresponding maintenance instructions, and timely perform equipment maintenance and repair, thereby preventing equipment failures and improving equipment reliability and stability; at the same time, with the help of data collection and analysis technology, the evaluation and optimization process of equipment energy consumption status is more automated and intelligent, which can quickly and accurately determine the energy consumption of equipment and improve the scientificity and accuracy of management; realize online monitoring and management of different energy consumption in the factory, which helps save energy and improve efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0032] like Figure 1 As shown, an online energy consumption monitoring system based on Internet of Things technology includes: a data acquisition module, a database, an energy consumption monitoring module and an optimization management module;

[0033] Sensors and metering devices are installed on each device in the factory to monitor the consumption information of various energy sources in real time. When the data acquisition module receives the acquisition instruction, it communicates with the sensors and metering devices corresponding to each device to collect the corresponding consumption of each device;

[0034] The energy consumption assessment module conducts trend analysis and peak analysis on the energy consumption of various types of equipment in the factory to evaluate the energy consumption status of the equipment and obtain energy consumption status parameters; specifically:

[0035] The consumption corresponding to each device is retrieved and recorded as Lij (energy consumption refers to the amount of energy type i consumed in one hour), where i=1, 2, 3...I, j=1, 2, 3...J, I and J are positive integers, i is any one of the energy types, j is any one of the collection moments; I represents the total number of energy types corresponding to the device, and J is the total number of collection moments; thus, the energy consumption corresponding to any collection moment of each device can be obtained Lij; using the set formula Calculate to get the total consumption Lh, where h1 and h2 are respectively the set proportional coefficients; compare and analyze the total consumption with the set consumption range. When the total consumption is greater than the maximum value in the set consumption range, it means that the equipment is a large energy consumption equipment, and the equipment is recorded as a high energy consumption equipment; when the total consumption is within the set consumption range, the equipment is recorded as a medium energy consumption equipment; when the total consumption is less than the minimum value in the set consumption range, the equipment is recorded as a low energy consumption equipment; thus, the equipment in the plant area can be classified according to its energy consumption to obtain the corresponding energy consumption level;

[0036] Energy consumption trend analysis: Take any type of energy consumption of one of the devices, obtain the energy consumption Lj of the device, construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and energy consumption as the vertical coordinate, record the position of the energy consumption in the coordinate system as the energy consumption point, use a smooth curve to connect the energy consumption points in sequence to obtain the line graph of the energy consumption value of the device over time; draw a tangent to the curve at the energy consumption point, use data fitting to obtain the expression of the tangent, and derive the tangent expression to obtain the energy consumption derivative of each energy consumption point and record it as Sj (the energy consumption derivative Sij of each type of energy can be obtained); record the energy consumption derivative greater than zero as the positive energy consumption derivative, and sum them to obtain the energy consumption increase degree and record it as K1; record the energy consumption derivative less than zero as the positive energy consumption derivative. The energy consumption derivative is calculated by taking the absolute value to obtain the reverse energy consumption derivative, and the sum is calculated to obtain the energy consumption reduction degree and recorded as K2; the forward energy consumption derivative is compared and analyzed with the set forward interval. When the forward energy consumption derivative is greater than the maximum value in the set forward interval, the forward energy consumption derivative is recorded as the first-level forward derivative; when the forward energy consumption derivative is within the set forward interval, the forward energy consumption derivative is recorded as the second-level forward derivative; when the forward energy consumption derivative is less than the minimum value in the set forward interval, the forward energy consumption derivative is recorded as the third-level forward derivative; the number of the first-level forward derivative, the second-level forward derivative and the third-level forward derivative are counted respectively, and recorded as P1, P2, and P3 respectively; using the set formula Calculate to obtain the energy consumption increase deviation value Pa1 of the device, where a1, a2, and a3 are respectively the set proportional coefficients; similarly, compare and analyze the reverse energy consumption derivative with the set reverse interval. When the reverse energy consumption derivative is greater than the maximum value in the set reverse interval, the reverse energy consumption derivative is recorded as the first-level reverse derivative; when the reverse energy consumption derivative is within the set reverse interval, the reverse energy consumption derivative is recorded as the second-level reverse derivative; when the reverse energy consumption derivative is less than the minimum value in the set reverse interval, the reverse energy consumption derivative is recorded as the third-level reverse derivative; count the number of the first-level reverse derivative, the second-level reverse derivative, and the third-level reverse derivative, and record them as P4, P5, and P6 respectively; use the set formula Calculate to get the energy consumption reduction deviation value Pa2, where a4, a5, and a6 are the set proportional coefficients respectively; the energy consumption increase deviation value Pa1 and the energy consumption reduction deviation value Pa2 are calculated by the set formula Calculation is performed to obtain the energy consumption trend deviation index PK of the equipment, where a7 and a8 are respectively the set proportional coefficients, and e is a natural constant; thus, the energy consumption trend deviation index PKi of various energy sources of the equipment can be obtained; it should be noted that when the equipment is in normal operation, its energy consumption is usually relatively stable. When the change in energy consumption shows a large deviation, it means that the energy consumption of the equipment is abnormal and the possibility of hidden dangers is greater;

[0037] Peak analysis:

[0038] Take any type of energy consumption of one of the devices, retrieve the energy consumption versus time curve, use the peak detection algorithm to identify the energy consumption peak in the energy consumption versus time curve, identify the start and end times of the energy consumption peak, and calculate the time difference to obtain the duration of the energy consumption peak, recorded as Cr; calculate the time difference between the end time of the previous energy consumption peak and the start time of the next energy consumption peak in two adjacent energy consumption peaks to obtain the peak interval, recorded as , where r=1, 2, 3...R, R is a positive integer, R represents the total number of energy consumption peaks, and r represents the serial number of any energy consumption peak; the duration is compared and analyzed with the set duration interval. When the duration is greater than the maximum value in the set duration interval, the time period from the start time to the end time of the energy consumption peak corresponding to the duration is recorded as the first-level energy consumption period; when the duration is within the set duration interval, the time period from the start time to the end time of the energy consumption peak corresponding to the duration is recorded as the second-level energy consumption period; when the duration is less than the minimum value in the set duration interval, the time period from the start time to the end time of the energy consumption peak corresponding to the duration is recorded as the third-level energy consumption period. energy consumption period; respectively count the number of first-level energy consumption period, second-level energy consumption period and third-level energy consumption period, and record them as D1, D2 and D3 respectively; compare and analyze the peak interval with the set interval, when the peak interval is greater than the maximum value in the set interval, a long interval is accumulated; when the peak interval is within the set interval, a medium interval is accumulated; when the peak interval is less than the minimum value in the set interval, a short interval is accumulated; respectively count the number of long intervals, medium intervals and short intervals, and select the interval with the largest number as the regular interval, so that the number of regular intervals is recorded as D4, and the number of the other two intervals except the regular interval is recorded as D5 and D6; using the set formula Calculate to get the peak distribution value Dh, where h3, h4, h5, and h6 are the set proportional coefficients, is the average duration of each energy consumption peak, is the mean value of the interval duration of each energy consumption peak; from the formula, it can be seen that when the duration and interval duration of each energy consumption peak are more unevenly distributed, the greater the peak distribution value, indicating that the energy consumption of the equipment is abnormal and the possibility of hidden dangers is greater; it should be noted that when the equipment is in normal operation, the peak interval and duration are relatively regular, that is, the size of the peak interval and duration is relatively stable; when the size of the peak interval and duration is discrete, it means that the energy consumption of the equipment is abnormal and the possibility of hidden dangers is greater;

[0039] It is assumed that there are standard energy consumption curves for various types of energy corresponding to various devices in the database, and the energy consumption change curve of the same type of energy over time is overlapped and compared with the standard energy consumption curve. The time difference corresponding to the peak value of the energy consumption peak with the same serial number is calculated to obtain the time difference. When the time difference is greater than zero, the energy consumption peak time migration is accumulated once, and the time difference is used as the time migration amount of this energy consumption peak time migration; the peak values ​​corresponding to the energy consumption peak with the same serial number are calculated to obtain the peak value difference. When the peak value difference is greater than zero, the energy consumption peak migration is accumulated once, and the peak value difference is used as the peak value migration amount of this energy consumption peak migration; thus, it can be obtained that The number of energy consumption peak time migration and energy consumption peak migration in the overlap comparison between the energy consumption variation curve of the same type of energy and the standard energy consumption curve is counted, and they are recorded as N and M respectively; thus, all time migration and peak migration between the energy consumption variation curve of the same type of energy and the standard energy consumption curve are obtained, and they are recorded as Vn and Vm respectively; where n=1, 2, 3...N; m=1, 2, 3...M; N and M are both positive integers, and N and M∈R; n represents any migration in the number of energy consumption peak time migrations; m represents any migration in the number of energy consumption peak-peak migrations; using the set formula Calculation is performed to obtain the total migration value Vb, where b1, b2, b3, and b4 are respectively set proportional coefficients;

[0040] The energy consumption trend deviation index, peak distribution value and total migration value of each type of energy of the equipment are used as the energy consumption state parameters of the equipment; thereby, the energy consumption trend deviation index KPi, peak distribution value Dhi and total migration value Vbi of each type of energy corresponding to each equipment can be obtained;

[0041] Data collection instruction generation: retrieve the energy consumption level, energy consumption derivative Sij and energy consumption trend deviation index Kpi of each device; set a collection base value corresponding to each energy consumption level, compare the energy consumption level of the device with all the set energy consumption levels to match the corresponding collection base value recorded as Y1; use the set formula Calculate to obtain the acquisition frequency Yb, where b5, b6, and b7 are respectively set proportional coefficients; generate acquisition instructions according to the acquisition frequency, and send the acquisition instructions to the data acquisition module; through the generation of acquisition instructions and automated data acquisition, it is possible to reduce manual intervention, improve the accuracy and efficiency of data acquisition, and realize automated monitoring and management of equipment energy consumption;

[0042] By controlling data collection to provide accurate energy consumption information, and conducting energy consumption trend analysis and peak analysis to obtain energy consumption status parameters, comprehensive data monitoring and analysis are provided for various energy consumption of equipment in the factory, and comprehensive monitoring and evaluation of energy consumption are achieved, which helps to improve equipment management and energy consumption optimization, and provide technical support for the company's energy conservation, emission reduction and sustainable development.

[0043] The optimization management module evaluates energy efficiency based on energy consumption status parameters and identifies potential risks of equipment to generate corresponding optimization management strategies; specifically:

[0044] Each device type is set to correspond to an energy consumption base value, and the device type is retrieved and compared with all the set device types to match the corresponding energy consumption base value, which is recorded as W; it should be noted that the energy consumption base value is the basic value representing the energy consumption characteristics of the device;

[0045] Retrieve the energy consumption trend deviation index KPi, peak distribution value Dhi and total migration value Vbi of various energy sources of the equipment, and use the set formula Calculation is performed to obtain the energy consumption anomaly index Ai of each type of energy, where u1, u2, u3, and u4 are respectively set proportional coefficients, and e is a natural constant; the energy consumption anomaly index is compared and analyzed with the set anomaly interval. When the energy consumption anomaly index is greater than the maximum value in the set anomaly interval, this type of energy is recorded as highly abnormal energy consumption; when the energy consumption anomaly index is within the set anomaly interval, this type of energy is recorded as moderately abnormal energy consumption; when the energy consumption anomaly index is less than the minimum value in the set anomaly interval, this type of energy is recorded as low abnormal energy consumption; the number of highly abnormal energy consumption, moderately abnormal energy consumption, and low abnormal energy consumption in each type of energy corresponding to the equipment is counted respectively, and they are recorded as Q1, Q2, and Q3 respectively; when Q1>Q2+Q 3, it means that the consumption of various types of energy of the equipment is in an abnormal state, then an equipment maintenance instruction is generated and sent to the corresponding maintenance engineer for equipment maintenance; when the equipment maintenance completion signal is received, a re-collection instruction is sent to the data acquisition module for data collection and energy consumption status evaluation until Q1>Q2+Q3 is no longer satisfied; when Q3>Q1+Q2, it means that the energy consumption of various types of energy of the equipment is normal, and no operation is required; in other cases except Q1>Q2+Q3 and Q3>Q1+Q2, the energy consumption status parameters corresponding to the highly abnormal energy consumption, the first energy consumption period, the second energy consumption period and the third energy consumption period are integrated into an equipment energy consumption optimization report, and sent to the equipment management personnel to provide important reference and decision-making basis for energy consumption optimization;

[0046] By comprehensively analyzing the energy consumption status parameters to determine the various energy consumption status of the equipment, it is possible to accurately locate abnormal equipment and abnormal types of energy consumption, promptly discover potential problems with the equipment, and generate corresponding maintenance instructions to perform equipment maintenance and repair in a timely manner, thereby preventing equipment failures and improving equipment reliability and stability; at the same time, with the help of data collection and analysis technology, the evaluation and optimization process of the equipment energy consumption status is made more automated and intelligent, which can quickly and accurately determine the energy consumption of the equipment and improve the scientificity and accuracy of management.

[0047] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. Energy consumption online monitoring system based on Internet of Things technology, It is characterized in that include: Energy consumption assessment module and optimization management module; The energy consumption assessment module performs trend analysis and peak analysis based on the energy consumption of various types of energy of various equipment in the factory to obtain energy consumption status parameters for evaluating the energy consumption status of the equipment, wherein the energy consumption status parameters include the energy consumption trend deviation index, peak distribution value and total migration value of various types of energy; The optimization management module further analyzes the energy consumption status of the equipment based on the energy consumption status parameters to measure the energy consumption status of the equipment, identify the potential risks of the equipment, and generate corresponding optimization management strategies; the specific process is as follows: Set each device type to correspond to an energy consumption base value, retrieve the device type and compare it with all the set device types to match the corresponding energy consumption base value, which is recorded as W; Retrieve the energy consumption trend deviation index KPi, peak distribution value Dhi and total migration value Vbi of various energy sources of the equipment, and use the set formula to calculate ... Calculation is performed to obtain the energy consumption abnormality index Ai of each type of energy, where u1, u2, u3, and u4 are respectively the set proportional coefficients, e is a natural constant, i=1, 2, 3…I, I represents the total number of energy types corresponding to the equipment, and i is any one of the energy types; the energy consumption abnormality index is compared and analyzed with the set abnormal interval to divide the various types of energy corresponding to the equipment into highly abnormal energy consumption, moderately abnormal energy consumption, and low abnormal energy consumption; the number of highly abnormal energy consumption, moderately abnormal energy consumption, and low abnormal energy consumption in each type of energy corresponding to the equipment is counted respectively, and recorded as Q1 respectively. , Q2 and Q3; when Q1>Q2+Q3, an equipment maintenance instruction is generated and sent to the corresponding maintenance engineer for equipment maintenance; when the equipment maintenance completion signal is received, a re-collection instruction is sent to the data acquisition module for data collection and energy consumption status evaluation until Q1>Q2+Q3 is no longer satisfied; when Q3>Q1+Q2, no operation is required; in other cases except Q1>Q2+Q3 and Q3>Q1+Q2, the energy consumption status parameters, first-level energy consumption time periods, second-level energy consumption time periods and third-level energy consumption time periods corresponding to the consumption of highly abnormal energy are integrated into an equipment energy consumption optimization report.

2. According to claim 1, the energy consumption online monitoring system based on Internet of Things technology, It is characterized in that The specific process of trend analysis of energy consumption of various energy sources is as follows: Take any type of energy consumption of one of the devices, take time as the horizontal coordinate and energy consumption as the vertical coordinate to obtain a line graph of the energy consumption value of the device changing with time; draw a tangent to the curve at the energy consumption point, use data fitting to obtain the expression of the tangent, and perform a derivative operation on the tangent expression to obtain the energy consumption derivative of each energy consumption point; record the energy consumption derivative greater than zero as the forward energy consumption derivative, and sum them up to obtain the energy consumption increase degree; take the absolute value of the energy consumption derivative less than zero to obtain the reverse energy consumption derivative, and sum them up to obtain the energy consumption reduction degree; compare and analyze the forward energy consumption derivative with the set forward interval to divide the forward energy consumption derivative into a first-level forward derivative, a second-level forward derivative and a third-level forward derivative, count the number of the first-level forward derivative, the second-level forward derivative and the third-level forward derivative respectively, and combine them with the energy consumption increase degree to perform a formulaic calculation and analysis to obtain the energy consumption increase deviation value; Similarly, the reverse energy consumption derivative is compared and analyzed with the set reverse interval to divide the reverse energy consumption derivative into a first-level reverse derivative, a second-level reverse derivative and a third-level reverse derivative; the number of the first-level reverse derivative, the second-level reverse derivative and the third-level reverse derivative is counted respectively, and the energy consumption reduction deviation value is obtained by formulating and analyzing the energy consumption reduction degree; The energy consumption trend deviation index is obtained by conducting comprehensive numerical analysis on the energy consumption increase deviation value and the energy consumption decrease deviation value; Thus, the energy consumption trend deviation index of each type of energy of the equipment can be obtained.

3. According to claim 1, the energy consumption online monitoring system based on Internet of Things technology, It is characterized in that The energy consumption assessment module is also used to analyze the energy consumption level of the equipment, and analyze the energy consumption data frequency requirements of the equipment in combination with the energy consumption derivative to generate collection instructions; the specific process is: The energy consumption corresponding to each device at different collection times is retrieved and analyzed in a formulaic manner to obtain the total consumption; the total consumption is compared and analyzed with the set consumption range to obtain the energy consumption level of each device; Retrieve the energy consumption level, energy consumption derivative Sij and energy consumption trend deviation index Kpi of each device; set each energy consumption level to correspond to a collection base value, compare the energy consumption level of the device with all the set energy consumption levels to match the corresponding collection base value recorded as Y1; use the set formula Calculation is performed to obtain the acquisition frequency Yb, where b5, b6, and b7 are respectively set proportional coefficients, j=1, 2, 3...J, J is a positive integer, j is any acquisition moment, and J is the total number of acquisition moments; an acquisition instruction is generated according to the acquisition frequency and sent to the data acquisition module.

4. According to claim 1, the energy consumption online monitoring system based on Internet of Things technology, It is characterized in that The specific process of peak analysis of energy consumption of various energy sources is as follows: 4-1: Take any type of energy consumption of one of the devices, identify the energy consumption peak in the energy consumption versus time curve and the start and end time of the energy consumption peak, and obtain the peak interval and duration of the energy consumption peak accordingly; 4-2: Distribute the energy consumption peaks in the energy consumption versus time curve according to the peak interval and duration of the energy consumption peaks to obtain the peak distribution value; 4-3: Overlap and compare the energy consumption curve of the same type of energy over time with the standard energy consumption curve to obtain the total migration value.

5. According to claim 4, the online energy consumption monitoring system based on Internet of Things technology, It is characterized in that According to the peak interval and duration of the energy consumption peak, the distribution of the energy consumption peak in the energy consumption versus time curve is analyzed to obtain the peak distribution value; the specific process is: Compare and analyze the duration with the set duration interval to divide the energy consumption peak corresponding to the duration into primary energy consumption period, secondary energy consumption period and tertiary energy consumption period; count the number of primary energy consumption period, secondary energy consumption period and tertiary energy consumption period respectively; compare and analyze the peak interval with the set interval interval to divide the peak interval into long interval, medium interval and short interval; count the number of long interval, medium interval and short interval respectively, and select the interval with the largest number as the regular interval, thereby obtaining the number of regular intervals and the number of the other two intervals excluding the regular interval; The peak interval, duration, number of first-level energy consumption periods, number of second-level energy consumption periods, number of third-level energy consumption periods, number of regular intervals, and number of two other intervals excluding regular intervals are formulated and calculated and analyzed to obtain the peak distribution value.

6. According to claim 5, the energy consumption online monitoring system based on Internet of Things technology, It is characterized in that The specific process of comparing and analyzing the energy consumption curve of the same type of energy over time with the standard energy consumption curve is as follows: The time difference is calculated by calculating the time corresponding to the peak value of the energy consumption peak with the same sequence number in the energy consumption curve of the same type of energy and the standard energy consumption curve. When the time difference is greater than zero, the energy consumption peak time migration is accumulated once, and the time difference is used as the time migration amount of this energy consumption peak time migration; The peak values ​​corresponding to the energy consumption peaks with the same serial number are difference calculated to obtain the peak difference. When the peak difference is greater than zero, an energy consumption peak migration is accumulated, and the peak difference is used as the peak migration amount of this energy consumption peak migration. The energy consumption peak time migration and the number of energy consumption peak migrations that overlap with the time curve of the energy consumption of the same type of energy and the standard energy consumption curve are counted respectively, and the corresponding time migration amount and peak migration amount are analyzed by formula calculation to obtain the total migration value.

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