Energy consumption management system and method for monitoring equipment

Abstract

The invention discloses an energy consumption management system and method for monitoring equipment, and the method comprises the steps: collecting the power consumption of the monitoring equipment, and obtaining the power sequence data and a power consumption curve; according to the running time t of the monitoring equipment, data at the moment t are extracted from the electricity consumption curve and the electricity test change curve, current electricity data and current reference data are obtained, and correction data are obtained according to the current reference data; marking the equipment of which the comprehensive similarity between the electricity consumption curve and the electricity test change curve is greater than a first threshold value and the difference value between the correction data and the current electricity data is smaller than a second threshold value as normal equipment; otherwise, marking the equipment as abnormal equipment, and sending an early warning signal; analyzing the electric quantity change condition of normal equipment according to the electric quantity sequence data to obtain seriously aged equipment; and sending an equipment replacement signal according to the abnormal equipment and the seriously aged equipment. The invention relates to the field of energy consumption management, and solves the technical problem that an existing method is insufficient in refinement, intellectualization and accuracy.

Description

Technical Field

[0001] The invention belongs to the field of equipment management, relates to energy consumption monitoring technology, and specifically is an energy consumption management system and method for monitoring equipment. Background Art

[0002] In the context of global advocacy of energy conservation, emission reduction and sustainable development, energy consumption management has become the focus of attention in various industries. From the perspective of cost control, energy consumption management can significantly reduce energy expenses. In the field of industrial production, the amount of electricity consumed by a large number of monitoring equipment in continuous operation cannot be underestimated. Through reasonable energy consumption management, energy consumption peaks and troughs can be accurately identified, and equipment operation time and mode can be optimized, thereby reducing electricity expenses. From the perspective of equipment maintenance and life extension, energy consumption management helps to timely detect abnormal energy consumption of equipment. When equipment energy consumption fluctuates abnormally, it often means that there may be hidden faults inside the equipment. Through the monitoring and analysis of the energy consumption management system, equipment failures can be warned in advance, so that maintenance personnel can conduct inspections and repairs in time, and avoid serious damage to the equipment due to minor failures turning into major failures, thereby extending the service life of the equipment and reducing the cost of equipment replacement. From the perspective of environmental protection, energy consumption management can reduce energy consumption, thereby reducing carbon emissions and other pollutants.

[0003] However, the existing energy consumption management methods for monitoring equipment mostly use simple power monitoring systems. Although they can collect power data in real time through power sensors and transmit them to the central control system for display and recording, it is difficult to detect potential patterns and abnormalities in the data. When the energy consumption of the equipment changes slowly, it is impossible to accurately judge whether it is normal. The threshold setting of the early warning system based on threshold setting is usually lacking in scientificity and flexibility, and the early warning accuracy is insufficient, and it is impossible to accurately judge the actual operating status of the equipment, resulting in inaccurate energy consumption assessment of the equipment, and the inability to adjust the equipment's operating status and management strategy in a timely manner according to the actual situation. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an energy consumption management system and method for monitoring equipment, which are used to solve the technical problems of the shortcomings of existing energy consumption management methods in terms of refinement, intelligence and accuracy.

[0005] To achieve the above object, a first aspect of the present invention provides an energy consumption management system for monitoring equipment, comprising: Power collection module: used to collect the power consumption of the monitoring equipment according to the preset time interval to obtain the power sequence data that changes with time; Data storage module: used to store the power sequence data of the monitoring device in the power storage library, and store the power test change curve of the monitoring device in the power change template library; Abnormal warning module: used to extract the power test change curve from the power change template library, obtain abnormal devices and normal devices according to the current power sequence data and power test change curve of the monitoring device, and send warning signals according to the abnormal devices; Energy consumption analysis module: used to analyze the power changes of normal equipment according to the current power sequence data and obtain seriously aged equipment; Equipment management module: used to obtain equipment information of abnormal equipment and seriously aged equipment, and send equipment replacement signals.

[0006] Furthermore, the power test change curve is a curve chart showing the change of power over time obtained by testing the standard monitoring equipment throughout its life cycle under a standard environment; wherein the standard environment includes constant temperature, humidity, voltage and dust concentration, eliminating the influence of external interference factors on power consumption.

[0007] Based on the above technical modules, the present invention constructs a power storage library and a power change template library to conduct in-depth analysis of the power sequence data of the monitoring equipment, ensuring that the monitoring equipment is always in an efficient operating state, avoiding additional energy consumption caused by equipment abnormalities or aging, thereby reducing overall energy consumption. The equipment management module sends equipment replacement information in real time, optimizes the operating status of the equipment, reduces unnecessary energy consumption caused by equipment problems, and indirectly reduces carbon emissions.

[0008] Furthermore, obtaining abnormal devices and normal devices according to the current power sequence data of the monitoring device and the power test change curve includes: A1, draw a curve based on the power sequence data of the monitoring equipment to obtain the power consumption curve; A2, compare whether the comprehensive similarity between the power consumption curve and the power test change curve is greater than the first preset threshold; if yes, jump to A3; if no, mark the monitoring device as an abnormal device; A3, according to the running time t of the monitoring device, extract the data at time t from the power consumption curve and the power test change curve respectively, and obtain the current power data and the current reference data; A4, multiply the current reference data by the environmental adjustment coefficient to obtain the corrected data, and compare whether the difference between the current power data and the corrected data is greater than the second preset threshold; if yes, mark the monitoring device as an abnormal device; if not, mark the monitoring device as a normal device.

[0009] Furthermore, the method for obtaining the comprehensive similarity includes: A21, derive the power consumption curve and the power test change curve, record the starting point, end point and the point where the first-order derivative is zero and the second-order derivative is not zero, and obtain the characteristic point set P1={p 11,p 12 ,…,p 1n} and the characteristic point set P2={p 21 ,p 22 ,…,p 2m}; n and m represent the number of elements in the feature point set; A22, calculate the local curvature of each feature point in the feature point set, and obtain the local curvature set K1={k 11 ,k 12 ,…,k 1n} and the local curvature set K2={k 21 ,k 22 ,…,k 2m}; A23, using the dynamic time warping algorithm to align the feature point set P1 and the feature point set P2 on the time axis, to obtain the aligned feature point pair (p 1i ,p 2j ); A24, calculating the comprehensive similarity of the power consumption curve and the power test change curve according to the position similarity formula and the curvature similarity formula to obtain the comprehensive similarity.

[0010] By taking the derivative of the power consumption curve and the power test change curve and extracting the feature point set, the key features of the curve can be captured, reflecting the change trend and turning point of the curve, and further understanding the shape change of the curve based on the local curvature of the feature point. Finally, by comprehensively considering the position similarity and curvature similarity to calculate the comprehensive similarity, the similarity of the two curves can be comprehensively measured, so as to more accurately judge whether the energy consumption of the monitoring equipment is normal and filter out abnormal equipment.

[0011] Further, the comprehensive similarity between the power consumption curve and the power test change curve is calculated according to the position similarity formula and the curvature similarity formula, including: According to the location similarity formula Calculate the position similarity Sp ij ;in, and Respectively and The coordinates of Represents the maximum distance between all aligned feature point pairs; According to the curvature similarity formula Sc ij =1- k 1i -k 2j / k max Calculate the curvature similarity Sc ij; where k max represents the maximum value among the local curvature sets K1 and K2; According to the position similarity Sp ij and curvature similarity Sc ij The comprehensive similarity between the power consumption curve and the power test change curve is obtained.

[0012] Further, the position similarity Sp ij and curvature similarity Sc ij The comprehensive similarity between the power consumption curve and the power test change curve is obtained, including: According to the formula S ij =αSp ij +(1-α)Sc ij Calculate the similarity S of the feature point pair ij ; Among them, α represents the weight coefficient, which is used to adjust the relative importance of position similarity and curvature similarity, and is obtained through practical experience; The similarity S of all aligned feature points ij The average is calculated to obtain the comprehensive similarity S between the power consumption curve and the power test change curve.

[0013] Furthermore, the calculation formula of the environmental adjustment coefficient is: K=(1+λ1[(T-T0) / T0])×(1+λ2[(H-H0) / H0])×(1+λ3[(D-D0) / D0]); wherein T represents the current ambient temperature, T0 represents the reference temperature of the standard environment, H represents the current ambient humidity, H0 represents the reference humidity of the standard environment, D represents the dust concentration of the current environment, D0 represents the reference dust concentration of the standard environment, λ1, λ2, and λ3 are the proportions of each indicator, which are used to adjust the influence of each environmental factor on the power consumption, and the proportions of each indicator are obtained through regression analysis.

[0014] The introduction of the environmental adjustment coefficient comprehensively considers the impact of factors such as current ambient temperature, humidity, and dust concentration on the power consumption of the equipment, making the judgment result more in line with the actual situation, reducing misjudgments caused by environmental changes, and improving the reliability of judgment.

[0015] Furthermore, analyzing the power change of normal devices according to the power sequence data includes: B1, obtain the power change sequence based on the power sequence data ; Where Q represents the total amount of data Q in the power change sequence, ,and , Indicates the power value of the qth data point; B2, counting the number of zero values ​​in the power change sequence to obtain the zero value count, and summing up several values ​​in the power change sequence to obtain the total change; B3, judging whether a normal device is a severely aged device based on the zero value count and the total amount of change.

[0016] Further, judging whether a normal device is a severely aged device according to the zero value count and the total amount of changes includes: B31, calculate the ratio of the zero value count to the total amount of data Q in the power change sequence to obtain the zero value ratio; B32, judging whether the zero value ratio is greater than a third preset threshold; if yes, jumping to B33; if no, marking the normal device as a severely aged device; B33: When the total amount of change is greater than a fourth preset threshold, the normal device is marked as a severely aged device.

[0017] When the zero value is relatively high, it indicates that the power consumption of the equipment fluctuates very little during this time period and the working state is relatively stable. When the zero value is relatively low, it means that the monitoring equipment frequently experiences power fluctuations, and there may be potential faults or serious equipment aging. The total change comprehensively considers the increase and decrease of power of the equipment in each operating stage. Whether it is the power change caused by normal working mode switching or the abnormal power fluctuation caused by equipment aging and performance degradation, it will also be reflected in the total change.

[0018] A second aspect of the present invention provides an energy consumption management method for a monitoring device, comprising: The power consumption of the monitoring equipment is collected according to the preset time interval to obtain the power sequence data and power consumption curve that change with time; According to the operation time t of the monitoring device, extract the data at time t from the power consumption curve and the power test change curve respectively to obtain the current power data and the current reference data, and multiply the current reference data by the environmental adjustment coefficient to obtain the correction data; and The monitoring device whose comprehensive similarity between the power consumption curve and the power test change curve is greater than the first preset threshold and whose difference between the corrected data and the current power data is less than the second preset threshold is marked as a normal device; otherwise, it is marked as an abnormal device, and a warning signal is sent according to the abnormal device; Analyze the power changes of normal equipment based on power sequence data and identify seriously aged equipment; Obtain device information of abnormal devices and severely aged devices, and send device replacement signals.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention determines whether the operating state of the monitoring device is normal by comparing the power consumption curve of the monitoring device and the power test change curve. By analyzing the characteristic points and local curvature of the curve, the change trend and shape characteristics of the curve can be accurately captured. The state of the device is secondarily judged in combination with the difference between the actual power consumption and the ideal power consumption, and the state of the device can be verified again from the perspective of energy consumption quantification. At the same time, the environmental adjustment coefficient is introduced to comprehensively consider the influence of the ambient temperature, humidity, dust concentration, etc. on the power consumption of the device, and the ideal power consumption is corrected according to environmental factors, so that the judgment result is more in line with reality and the reliability of the judgment is improved. In the energy consumption analysis module, the power change sequence of the monitoring equipment is deeply analyzed, and the judgment result of seriously aged equipment is obtained by counting the zero value count and the total change amount. In addition, combined with the abnormal equipment information, the equipment replacement signal is sent to the equipment management module, realizing real-time monitoring of the equipment energy consumption status, avoiding additional energy consumption caused by equipment abnormality or aging, and reducing overall energy consumption. Compared with the prior art, the present invention can more timely and effectively discover and handle equipment problems, prevent excessive energy consumption of equipment, and reduce unnecessary energy consumption caused by equipment problems by optimizing the equipment operation status, thereby indirectly reducing carbon emissions and helping to achieve sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A technical flow chart of an energy consumption management method for monitoring equipment provided by the present invention; Figure 2 A framework diagram of an energy consumption management system for a monitoring device provided by the present invention; Figure 3 A flowchart of an abnormal warning module in an energy consumption management system for a monitoring device provided by the present invention; Figure 4 A workflow diagram of an energy consumption analysis module in an energy consumption management system for monitoring equipment provided by the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1-Figure 4 The first aspect of the present invention provides an energy consumption management system for monitoring equipment, including: Power collection module: used to collect the power consumption of the monitoring equipment according to the preset time interval to obtain the power sequence data that changes with time; Data storage module: used to store the power sequence data of the monitoring device in the power storage library, and store the power test change curve of the monitoring device in the power change template library; Abnormal warning module: used to extract the power test change curve from the power change template library, obtain abnormal devices and normal devices according to the current power sequence data and power test change curve of the monitoring device, and send warning signals according to the abnormal devices; Energy consumption analysis module: used to analyze the power changes of normal equipment according to the power sequence data and obtain seriously aged equipment; Equipment management module: used to obtain equipment information of abnormal equipment and seriously aged equipment, and send equipment replacement signals.

[0024] Electricity is the most direct reflection of equipment energy consumption. By analyzing the electricity, we can accurately understand the energy consumption level of the equipment at different times and in different working modes. Therefore, in the present invention, by collecting and analyzing the electricity sequence data of the monitoring equipment, we can find out the abnormal conditions and serious signs of aging of the equipment during operation, so as to achieve effective management of equipment energy consumption, ensure that the equipment operates in a normal state, avoid additional energy consumption caused by equipment abnormality or aging, and ultimately achieve the purpose of energy conservation and emission reduction, while ensuring the reliability and service life of the equipment, and improving the overall performance and management efficiency of the equipment.

[0025] An energy consumption management system for monitoring equipment of the present invention includes a power collection module, a data storage module, an abnormal warning module, an energy consumption analysis module and an equipment management module, and it should be noted that the power collection module, the data storage module, the abnormal warning module, the energy consumption analysis module and the equipment management module are communicatively connected.

[0026] The power collection module of this embodiment is the basic part of the entire energy consumption management system. Its core function is to collect the power consumption of the monitoring device according to the preset time interval, so as to obtain the power sequence data that changes over time. This preset time interval can be flexibly set according to the characteristics of the monitoring device and actual needs, usually in hours or days; The power collection module can record the power consumption information of the monitoring equipment from the beginning of operation to the present. By arranging the power data collected at each time point in chronological order, a complete curve of the power consumption of the monitoring equipment over time can be obtained to show the energy consumption characteristics of the equipment at different life cycle stages; At the same time, it can also extract the power consumption of the device in the recent period of time at any time, which is helpful for short-term monitoring and real-time analysis of the device's immediate performance.

[0027] The data storage module of this embodiment is used to store the power sequence data of the monitoring device in the power storage library, and store the power test change curve of the monitoring device in the power change template library.

[0028] In one embodiment, the data storage module can classify and store the power sequence data and the corresponding power test change curve according to the type and model of the monitoring device. At the same time, the power test change curve corresponding to the model is stored in the corresponding area in the power change template library, so as to realize classified storage, improve the management efficiency and query speed of data, and facilitate the subsequent modules to quickly and accurately locate the required information when calling the data.

[0029] In one implementation, the power test change curve in the power change template library is obtained by testing various standard monitoring devices throughout their life cycle under a standard environment. The standard environment covers conditions such as constant temperature, humidity, voltage, and dust concentration, and eliminates the influence of external interference factors on power consumption to the greatest extent, so as to obtain the purest curve of the device's power change over time under ideal conditions. For example, when testing a certain model of sensor, the ambient temperature is set to a constant 25°C, the humidity is controlled at 50%, the voltage is stabilized at 220V, and the dust concentration is maintained at an extremely low level (for example, around 0.03-0.05mg / m³). The sensor is allowed to complete the entire process from the start of operation to the end of its service life in such an environment, and the power data at each time point is recorded to finally generate a power test change curve; The power test change curve provides a scientific basis for the overall energy consumption management decision. Based on this curve, managers can understand the energy consumption level of the equipment under ideal conditions, and use this as a goal to optimize the equipment operating environment, adjust the working mode, etc., so that the actual energy consumption of the equipment is close to or reaches the ideal state, thereby achieving the purpose of energy conservation, emission reduction and lowering operating costs.

[0030] The abnormal warning module of this embodiment is used to extract the corresponding power test change curve from the power change template library according to the category of the monitoring device, obtain normal devices and abnormal devices according to the current power sequence data and power test change curve of the monitoring device, and alarm the abnormal device.

[0031] In one implementation, obtaining abnormal devices and normal devices according to the current power sequence data of the monitoring device and the power test change curve includes: A1, draw a curve based on the power sequence data of the monitoring equipment to obtain the power consumption curve; A2, calculating the comprehensive similarity between the power consumption curve and the power test change curve, and determining whether the first similarity is greater than a first preset threshold; And the calculation process of comprehensive similarity is as follows: A21, feature point extraction: Derivative the power consumption curve and the power test change curve, record the starting point, end point and the point where the first-order derivative is zero and the second-order derivative is not zero, and obtain the feature point set P1={p 11 ,p 12 ,…,p 1n} and the characteristic point set P2={p 21 ,p 22 ,…,p 2m}; n and m represent the number of elements in the feature point set; A22, local curvature calculation: A22, calculate the local curvature of each feature point in the feature point set, and obtain the local curvature set K1={k 11 ,k 12 ,…,k 1n} and the local curvature set K2={k 21 ,k 22 ,…,k 2m}; A23, time axis alignment: Use the dynamic time warping algorithm to align the feature point set P1 and the feature point set P2 on the time axis to obtain the aligned feature point pair (p 1i ,p 2j ); A24, comprehensive similarity calculation: calculate the comprehensive similarity of the power consumption curve and the power test change curve according to the position similarity formula and the curvature similarity formula; Among them, the position similarity formula is: , and Respectively and The coordinates of Represents the maximum distance between all aligned feature point pairs; The curvature similarity formula is: Sc ij =1- k 1i -k 2j / k max , k max represents the maximum value of all values ​​in the local curvature sets K1 and K2; Then, according to formula S ij =αSp ij +(1-α)Sc ij Calculate the similarity S of the feature point pair ij ; α represents the weight coefficient, which is obtained based on historical experience; Finally, the similarity S of all aligned feature points is ij The average is calculated to obtain the comprehensive similarity S of the power consumption curve and the power test change curve; If the first similarity is greater than a first preset threshold, the monitoring device is marked as a normal device; otherwise, the monitoring device is marked as an abnormal device; A3, when the comprehensive similarity is greater than the first preset threshold, according to the operation time t of the monitoring device, the data at time t is extracted from the power consumption curve and the power test change curve respectively to obtain the current power data and the current reference data; For example, suppose there is an intelligent surveillance camera as a monitoring device. The power collection module collects power data at intervals of 15 minutes. After a whole day of monitoring, the power sequence data for the day is [0.05, 0.052, 0.051, 0.053, …, 0.054] (unit: degree). At the same time, it is known that the camera has been running for 1000 hours, or 60,000 minutes, since it was put into use. The last power data in the power sequence data for the day, that is, the data at time t in the power consumption curve, is extracted as the current power data. Similarly, by conducting a full life cycle test on the same model of camera under a standard environment, we obtained a power test change curve, and then extracted the power data at 60,000 minutes from the power test change curve to obtain the current reference data; A4, multiply the current reference data by the environmental adjustment coefficient to obtain the corrected data; where the environmental adjustment coefficient K=(1+λ1[(T-T0) / T0])×(1+λ2[(H-H0) / H0])×(1+λ3[(D-D0) / D0]) takes into account the difference between the current ambient temperature T, humidity H, dust concentration D and the reference temperature T0, humidity H0, and dust concentration D0 of the standard environment, where λ1, λ2, and λ3 are the proportions of each indicator, obtained by regression analysis, reflecting the degree of influence of each environmental factor on power consumption. The reference data is corrected by the calculated environmental adjustment coefficient to make it more in line with the ideal situation under the current environmental conditions; Next, compare whether the difference between the current power data and the corrected data is greater than a second preset threshold. If so, mark the monitoring device as an abnormal device; otherwise, mark it as a normal device.

[0032] In one implementation, obtaining the ratio of each indicator by regression analysis may include the following steps: Through the control variable method, first fix all environmental factors and equipment operating conditions except one environmental factor. For example, when studying the effect of temperature on equipment power consumption, keep other factors such as humidity, dust concentration, and equipment workload at a constant level; On this basis, change the selected environmental factors (such as temperature) and record the corresponding power consumption of the device at different temperature values. Collect multiple sets of such data points to form a data set about temperature and power consumption. You can set the temperature to gradually increase from a lower value to a higher value, such as from 10℃ to 30℃ in 2℃ intervals, and record the power consumption of the device each time; Then, for this data set, a simple linear regression analysis is used to fit the data. Assuming that power consumption is the dependent variable and temperature is the independent variable, the fitted linear equation is y=a+λ1x+c, where a is the intercept, λ1 is the temperature index ratio, and c is the error term. Solving this linear regression equation using methods such as the least squares method, we get the value of λ1, which represents the degree of influence of temperature changes on the power consumption of the equipment when other conditions remain unchanged; Similarly, experiments and analyses were conducted on humidity and dust concentration, and finally three different linear regression equations and corresponding coefficients λ1 (temperature), λ2 (humidity), and λ3 (dust concentration) were obtained.

[0033] In the abnormal warning module, by analyzing the key feature points and local curvature of the power consumption curve and the power test change curve, the similarity information of the two is obtained; among them, the key feature points are used to indicate the turning point of the power change trend of the equipment in different operation stages, and the local curvature describes the degree of curvature of the curve, which is used to indicate the stability of energy consumption changes. Based on the comprehensive consideration of the key feature points and local curvature, the similarity information of the two curves can be obtained more accurately, providing a basis for judging whether the equipment is abnormal.

[0034] In this embodiment, the abnormal warning module accurately identifies abnormal equipment by comprehensively considering information such as curve shape and environmental factors, providing timely and accurate information for equipment maintenance and management, helping to prevent energy waste and equipment damage caused by continued operation of the equipment under abnormal conditions, and improving equipment reliability and energy consumption management efficiency.

[0035] The energy consumption analysis module of this embodiment is used to analyze the power consumption changes of normal devices according to the current power consumption sequence data to obtain seriously aged devices.

[0036] Specifically, firstly, the power change sequence is obtained according to the current power sequence data ; Where Q represents the total amount of data Q in the power change sequence, ,and , Indicates the power value of the qth data point; Count the number of zero values ​​in the power change sequence to obtain the zero value count, and sum up several values ​​in the power change sequence to obtain the total change amount; Calculate the ratio of the zero value count to the total amount of data Q in the power change sequence to obtain the zero value ratio; Determine whether the zero value ratio is greater than a third preset threshold; if yes, jump to B33; if no, mark the normal device as a severely aged device; When judging whether the total amount of change is greater than the fourth preset threshold value; if yes, the normal device is marked as a severely aged device; if no, no processing is performed.

[0037] The zero value ratio reflects the stable operation of the equipment within a certain period of time, and the total change reflects the total power consumption change of the equipment within a certain period of time. The combination of the two judgment indicators, the zero value ratio and the total change, takes into account both the short-term stable operation of the equipment and the long-term overall energy consumption fluctuations, which helps to find the signs of equipment aging as soon as possible, avoid the abnormal growth of continuous energy consumption and performance degradation caused by equipment aging, and also reduce the additional energy costs and potential failure risks caused by equipment aging for enterprises.

[0038] It should be noted that the first preset threshold, the second preset threshold, the third preset threshold and the fourth preset threshold in the present invention are obtained based on historical data analysis and expert experience.

[0039] The device management module of this embodiment is used to obtain the device information of abnormal devices and severely aged devices and send device replacement signals. The operation and maintenance personnel can replace abnormal devices and severely aged devices in time according to the device replacement signals to ensure the normal operation and stability of the overall system.

[0040] A second aspect of the present invention provides an energy consumption management method for a monitoring device, comprising: S1, collecting the power consumption of the monitoring equipment according to the preset time interval to obtain the power sequence data and power consumption curve that change with time; S2, storing the power sequence data of the monitoring device in a power storage library, and storing the power test change curve of the monitoring device in a power change template library; S3, extracting the power test change curve from the power change template library, and extracting the data at time t from the power consumption curve and the power test change curve respectively according to the running time t of the monitoring device, obtaining the current power data and the current reference data, and multiplying the current reference data by the environmental adjustment coefficient to obtain the correction data; and The monitoring device whose comprehensive similarity between the power consumption curve and the power test change curve is greater than the first preset threshold and whose difference between the corrected data and the current power data is less than the second preset threshold is marked as a normal device; otherwise, it is marked as an abnormal device, and a warning signal is sent according to the abnormal device; S4, analyzing the power changes of normal devices according to the current power sequence data to obtain seriously aged devices; S5, replace abnormal equipment and seriously aged equipment.

[0041] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0042] Working principle of the present invention: The present invention uses an electric quantity collection module to collect the electric quantity consumption of the monitoring device at preset time intervals to obtain electric quantity sequence data that changes with time, thereby providing a basic data source for the analysis of energy consumption management; uses a data storage module to store the electric quantity sequence data of the monitoring device in an electric quantity storage library, and stores the electric quantity test change curve in an electric quantity change template library, thereby effectively organizing and saving data, facilitating subsequent modules to call at any time, and facilitating comparative analysis; Then, through the abnormal warning module, the power test change curve is extracted from the power change template library, combined with the current power sequence data, the equipment operation status is judged, normal and abnormal equipment are distinguished, and abnormal conditions in equipment operation are accurately discovered and warning signals are issued to achieve effective monitoring of abnormal equipment status and avoid energy consumption increase caused by equipment abnormalities; through the energy consumption analysis module, the power change of normal equipment is analyzed, the power change sequence is calculated, the zero value count and the total change are counted, and whether the equipment is seriously aged is judged based on the set threshold, and the energy consumption changes caused by the decline in equipment performance are discovered in advance, providing a basis for equipment maintenance and avoiding long-term high energy consumption caused by equipment aging; Finally, through the equipment management module, the information of abnormal equipment and seriously aged equipment is obtained and equipment replacement signals are sent, the equipment update and maintenance process is coordinated to achieve effective management of energy consumption.

[0043] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An energy consumption management system for monitoring equipment, characterized in that: include: Power collection module: used to collect the power consumption of the monitoring equipment according to the preset time interval, and obtain the power sequence data and power consumption curve that change with time; Abnormal warning module: used to extract the data at time t from the power consumption curve and the power test change curve according to the running time t of the monitoring equipment, obtain the current power data and the current reference data, and multiply the current reference data by the environmental adjustment coefficient to obtain the correction data; as well as, The monitoring device whose comprehensive similarity between the power consumption curve and the power test change curve is greater than the first preset threshold value and whose difference between the corrected data and the current power data is less than the second preset threshold value is marked as a normal device; otherwise, it is marked as an abnormal device, and a warning signal is sent according to the abnormal device; wherein the power test change curve is a curve chart of power changes over time obtained by testing the standard monitoring device for the entire life cycle under a standard environment; wherein the standard environment includes constant temperature, humidity, voltage and dust concentration; Energy consumption analysis module: used to analyze the power changes of normal equipment according to the power sequence data and obtain seriously aged equipment; Equipment management module: used to obtain equipment information of abnormal equipment and seriously aged equipment, and send equipment replacement signals.

2. The energy consumption management system for monitoring equipment according to claim 1, characterized in that: It also includes a data storage module for storing the power sequence data of the monitoring device in a power storage library, and storing the power test change curve of the monitoring device in a power change template library.

3. The energy consumption management system for monitoring equipment according to claim 2, characterized in that: The method for obtaining the comprehensive similarity includes: A21, derive the power consumption curve and the power test change curve, record the starting point, end point and the point where the first-order derivative is zero and the second-order derivative is not zero, and obtain the characteristic point set P1={p 11 ,p 12 ,…,p 1n } and the characteristic point set P2={p 21 ,p 22 ,…,p 2m }; n and m represent the number of elements in the feature point set; A22, calculate the local curvature of each feature point in the feature point set, and obtain the local curvature set K1={k 11 ,k 12 ,…,k 1n } and the local curvature set K2={k 21 ,k 22 ,…,k 2m }; A23, using the dynamic time warping algorithm to align the feature point set P1 and the feature point set P2 on the time axis, to obtain the aligned feature point pair (p 1i ,p 2j ); A24, calculates the comprehensive similarity of the power consumption curve and the power test change curve according to the position similarity formula and the curvature similarity formula to obtain the comprehensive similarity.

4. The energy consumption management system for monitoring equipment according to claim 3, characterized in that: The method of calculating the comprehensive similarity of the power consumption curve and the power test change curve according to the position similarity formula and the curvature similarity formula includes: According to the location similarity formula Calculate the position similarity Sp ij ;in, and Respectively and The coordinates of Represents the maximum distance between all aligned feature point pairs; According to the curvature similarity formula Sc ij =1- k 1i -k 2j / k max Calculate the curvature similarity Sc ij ; where k max represents the maximum value among the local curvature sets K1 and K2; According to the position similarity Sp ij and curvature similarity Sc ij The comprehensive similarity between the power consumption curve and the power test change curve is obtained.

5. The energy consumption management system for monitoring equipment according to claim 4, characterized in that: According to the position similarity Sp ij and curvature similarity Sc ij The comprehensive similarity between the power consumption curve and the power test change curve is obtained, including: According to the formula S ij =αSp ij +(1-α)Sc ij Calculate the similarity S of the feature point pair ij ; Among them, α represents the weight coefficient; The similarity S of all aligned feature points ij The average is calculated to obtain the comprehensive similarity S between the power consumption curve and the power test change curve.

6. The energy consumption management system for monitoring equipment according to claim 1, characterized in that: The calculation formula of the environmental adjustment coefficient is: K=(1+λ1[(T-T0) / T0])×(1+λ2[(H-H0) / H0])×(1+λ3[(D-D0) / D0]); where T represents the current ambient temperature, T0 represents the reference temperature of the standard environment, H represents the current ambient humidity, H0 represents the reference humidity of the standard environment, D represents the dust concentration of the current environment, D0 represents the reference dust concentration of the standard environment, and λ1, λ2, and λ3 are proportional coefficients of each indicator, which are used to adjust the influence of each environmental factor on power consumption.

7. The energy consumption management system for monitoring equipment according to claim 1, characterized in that: Analyzing the power change of normal equipment according to the power sequence data includes: B1, obtain the power change sequence based on the power sequence data ; Where Q represents the total amount of data Q in the power change sequence, ,and , Indicates the power value of the qth data point; B2, counting the number of zero values ​​in the power change sequence to obtain the zero value count, and summing up several values ​​in the power change sequence to obtain the total change; B3, judging whether a normal device is a severely aged device based on the zero value count and the total amount of change.

8. The energy consumption management system for monitoring equipment according to claim 7, characterized in that: The step of judging whether a normal device is a severely aged device based on the zero value count and the total amount of changes includes: B31, calculate the ratio of the zero value count to the total amount of data Q in the power change sequence to obtain the zero value ratio; B32, judging whether the zero value ratio is greater than a third preset threshold; if yes, jumping to B33; if no, marking the normal device as a severely aged device; B33: When the total amount of change is greater than a fourth preset threshold, the normal device is marked as a severely aged device.

9. A method for managing energy consumption of a monitoring device, applied to an energy consumption management system for a monitoring device according to any one of claims 1 to 8, characterized in that: include: The power consumption of the monitoring equipment is collected according to the preset time interval to obtain the power sequence data and power consumption curve that change with time; According to the operation time t of the monitoring equipment, the data at time t is extracted from the power consumption curve and the power test change curve respectively to obtain the current power data and the current reference data, and the current reference data is multiplied by the environmental adjustment coefficient to obtain the correction data; as well as, The monitoring device whose comprehensive similarity between the power consumption curve and the power test change curve is greater than the first preset threshold value and whose difference between the corrected data and the current power data is less than the second preset threshold value is marked as a normal device; Otherwise, it is marked as an abnormal device and a warning signal is sent according to the abnormal device; Analyze the power changes of normal equipment based on power sequence data and identify seriously aged equipment; Obtain device information of abnormal devices and severely aged devices, and send device replacement signals.

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