An energy consumption management system and method for a monitoring device

Abstract

The present invention discloses an energy consumption management system and method for monitoring devices. The method includes: collecting the power consumption of the monitoring devices to obtain power consumption sequence data and a power consumption curve; according to the operating time t of the monitoring devices, extracting the data at the t-th moment from the power consumption curve and the power consumption test change curve to obtain the current power data and the current reference data, and obtaining the correction data according to the current reference data; marking the devices with a comprehensive similarity between the power consumption curve and the power consumption test change curve greater than a first threshold and the difference between the correction data and the current power data less than a second threshold as normal devices; otherwise, marking them as abnormal devices and sending a warning signal; analyzing the power change situation of the normal devices according to the power consumption sequence data to obtain severely aged devices; sending a device replacement signal according to the abnormal devices and the severely aged devices. The present invention relates to the field of energy consumption management and solves the technical problems of the deficiencies in refinement, intelligence, and accuracy of the existing methods.

Description

Technical Field

[0001] The present 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] Under the background of the 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 electricity consumed by a large number of monitoring equipment running continuously cannot be underestimated. Through reasonable energy consumption management, energy consumption peaks and valleys can be accurately identified, and the operation time and mode of equipment can be optimized, thereby reducing electricity bills. In terms of equipment maintenance and life extension, energy consumption management helps to detect abnormal energy consumption of equipment in a timely manner. When there are abnormal fluctuations in the energy consumption of equipment, it often means that there may be potential fault hazards inside the equipment. Through the monitoring and analysis of the energy consumption management system, equipment failures can be warned in advance, enabling maintenance personnel to conduct inspections and repairs in a timely manner, avoiding serious damage caused by small faults developing into large faults, thereby extending the service life of the equipment and reducing the equipment replacement cost. From the perspective of environmental protection, energy consumption management can reduce energy consumption, and thus reduce carbon emissions and the emissions of other pollutants.

[0003] However, most of the existing energy consumption management methods for monitoring equipment adopt simple electricity monitoring systems. Although they can collect electricity data in real time through electricity sensors and transmit it to the central control system for display and recording, it is difficult to detect potential laws and abnormal situations in the data. When the energy consumption of the equipment changes slowly, it is impossible to accurately judge whether it is normal. For the early warning system based on threshold setting, the threshold setting usually lacks scientificity and flexibility, and the early warning accuracy is insufficient, unable to accurately judge the actual operating state of the equipment, resulting in inaccurate energy consumption assessment of the equipment and unable to adjust the operating state and management strategy of the equipment 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; for this purpose, the present invention provides an energy consumption management system and method for monitoring equipment, which is used to solve the technical problems of the deficiencies in refinement, intelligence and accuracy of the existing energy consumption management methods.

[0005] To achieve the above object, the first aspect of the present invention provides an energy consumption management system for monitoring equipment, including:

[0006] An electricity collection module: used to collect the electricity consumption of the monitoring equipment according to a preset time interval, and obtain the electricity sequence data changing with time;

[0007] Data storage module: used to store the power sequence data of the monitoring device in the power storage repository, and store the power test change curve of the monitoring device in the power change template library;

[0008] Abnormal warning module: used to extract the power test change curve from the power change template library, obtain abnormal devices and normal devices based on the current power sequence data and the power test change curve of the monitoring device, and send a warning signal according to the abnormal devices;

[0009] Energy consumption analysis module: used to analyze the power change situation of normal devices based on the current power sequence data to obtain severely aged devices;

[0010] Device management module: used to obtain the device information of abnormal devices and severely aged devices, and send a device replacement signal.

[0011] Furthermore, the power test change curve is a curve graph of the power changing with time obtained by performing a full-life cycle test on a standard monitoring device in a standard environment; wherein, the standard environment includes constant temperature, humidity, voltage, and dust concentration, excluding the influence of external interference factors on power consumption.

[0012] Based on the above technical modules, the present invention deeply analyzes the power sequence data of the monitoring device by constructing a power storage repository and a power change template library, ensures that the monitoring device is always in an efficient operation state, avoids additional energy consumption caused by device abnormalities or aging, thereby reducing the overall energy consumption. And through the device management module, the device replacement information is sent in real time to optimize the operation state of the device, reduce the unnecessary energy consumption caused by device problems, and thus indirectly reduce the carbon emission.

[0013] Furthermore, obtaining abnormal devices and normal devices based on the current power sequence data and the power test change curve of the monitoring device includes:

[0014] A1, plotting the power sequence data of the monitoring device into a curve to obtain a power consumption curve;

[0015] A2, comparing whether the comprehensive similarity between the power consumption curve and the power test change curve is greater than a first preset threshold; if yes, jump to A3; if no, mark the monitoring device as an abnormal device;

[0016] A3, according to the operation time t of the monitoring device, respectively extract the data at the t moment from the power consumption curve and the power test change curve to obtain the current power data and the current reference data;

[0017] 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 consumption data and the corrected data is greater than the second preset threshold. If so, mark the monitoring device as an abnormal device; otherwise, mark the monitoring device as a normal device.

[0018] Further, the method for obtaining the comprehensive similarity includes:

[0019] A21. Take the derivatives of the power consumption curve and the power test change curve, record the starting point, ending point, and the points where the first derivative is zero and the second derivative is not zero of the curves, to obtain the set of characteristic points P1 = {p 11 , p 12 , …, p 1n} of the power consumption curve and the set of characteristic points P2 = {p 21 , p 22 , …, p 2m} of the power test change curve; n and m represent the number of elements in the set of characteristic points;

[0020] A22. Calculate the local curvature of each characteristic point in the set of characteristic points, to obtain the set of local curvatures K1 = {k 11 , k 12 , …, k 1n} of the set of characteristic points P1 and the set of local curvatures K2 = {k 21 , k 22 , …, k 2m} of the set of characteristic points P2;

[0021] A23. Use the dynamic time warping algorithm to align the set of characteristic points P1 and the set of characteristic points P2 on the time axis, to obtain the aligned characteristic point pairs (p 1i , p 2j );

[0022] A24. 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, to obtain the comprehensive similarity.

[0023] By taking the derivatives of the power consumption curve and the power test change curve and extracting the set of characteristic points, the key features of the curves can be captured, reflecting the change trends and turning points of the curves. And based on the local curvatures of the characteristic points, the shape changes of the curves can be further understood in depth. Finally, by comprehensively considering the position similarity and the curvature similarity to calculate the comprehensive similarity, the similarity degree of the two curves can be comprehensively measured, so as to more accurately judge whether the energy consumption situation of the monitoring device is normal and screen out abnormal devices.

[0024] Further, the calculation of 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:

[0025] According to the position similarity formula calculate the position similarity Sp ij ; where and respectively represent and coordinates, represents the maximum value of the distances between all aligned feature point pairs;

[0026] 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 in the local curvature sets K1 and K2;

[0027] According to the position similarity Sp ij and the curvature similarity Sc ij obtain the comprehensive similarity between the power consumption curve and the power consumption test change curve.

[0028] Furthermore, the obtaining of the comprehensive similarity between the power consumption curve and the power consumption test change curve according to the position similarity Sp ij and the curvature similarity Sc ij includes:

[0029] According to the formula S ij =αSp ij +(1 - α)Sc ij calculate the feature point pair similarity S ij ; where α represents the weight coefficient, used to adjust the relative importance of the position similarity and the curvature similarity, and is obtained through practical experience;

[0030] Average all the aligned feature point similarities S ij to obtain the comprehensive similarity S between the power consumption curve and the power consumption test change curve.

[0031] Furthermore, the calculation formula of the environmental adjustment coefficient is:

[0032] K=(1 + λ 1 [(T - T 0 ) / T 0 )×(1 + λ 2 [(H - H 0 ) / H 0 )×(1 + λ 3 [(D - D 0) / D 0 ) where T represents the current ambient temperature, T 0 represents the reference temperature of the standard environment, H represents the current ambient humidity, H 0 represents the reference humidity of the standard environment, D represents the current ambient dust concentration, D 0 represents the reference dust concentration of the standard environment, λ 1 、λ 2 、λ 3 are the proportion of each index, used to adjust the influence degree of each environmental factor on the power consumption, and the proportion of each index is obtained by regression analysis method.

[0033] By introducing the environmental adjustment coefficient, the influence of factors such as the current ambient temperature, humidity, and dust concentration on the power consumption of the equipment is comprehensively considered, making the judgment result more in line with the actual situation, reducing the misjudgment caused by environmental changes, and improving the reliability of the judgment.

[0034] Further, the analysis of the power consumption change of the normal equipment according to the power consumption sequence data includes:

[0035] B1. Obtain the power consumption change sequence according to the power consumption sequence data ; where Q represents the total amount of data Q in the power consumption change sequence, , and , represents the power consumption value of the qth data point;

[0036] B2. Count the number of zeros in the power consumption change sequence to obtain the zero value count, and sum several values in the power consumption change sequence to obtain the total change;

[0037] B3. Judge whether the normal equipment is a severely aged equipment according to the zero value count and the total change.

[0038] Further, the judgment of whether the normal equipment is a severely aged equipment according to the zero value count and the total change includes:

[0039] B31. Calculate the ratio of the zero value count to the total amount of data Q in the power consumption change sequence to obtain the zero value ratio;

[0040] B32. Judge whether the zero value ratio is greater than the third preset threshold; if yes, jump to B33; if no, mark the normal equipment as a severely aged equipment;

[0041] B33. When the total change is greater than the fourth preset threshold, mark the normal equipment as a severely aged equipment.

[0042] When the zero value is relatively high, it indicates that the power consumption of the device fluctuates very little during this period and the working state is relatively stable. When the zero value is relatively low, it means that the power consumption of the monitoring device fluctuates frequently, and there may be potential faults or serious device aging. The total change, on the other hand, comprehensively considers the increase and decrease of the power consumption of the device in each operation stage. Whether it is the power change caused by normal working mode switching or the abnormal power fluctuation caused by device aging and performance decline, it will be reflected in the total change.

[0043] The second aspect of the present invention provides an energy consumption management method for a monitoring device, including:

[0044] Collect the power consumption of the monitoring device according to a preset time interval to obtain power sequence data and a power consumption curve that change with time;

[0045] According to the operation time t of the monitoring device, extract the data at the t-th moment 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 corrected data; and,

[0046] Mark 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 the difference between the corrected data and the current power data is less than the second preset threshold as a normal device; otherwise, mark it as an abnormal device and send a warning signal according to the abnormal device;

[0047] Analyze the power change situation of the normal device according to the power sequence data to obtain seriously aged devices;

[0048] Obtain the device information of the abnormal device and the seriously aged device and send a device replacement signal.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] The present invention judges whether the operation state of the monitoring device is normal by comparing the power consumption curve and the power test change curve of the monitoring device. By analyzing the characteristic points and local curvatures of the curves, it can accurately capture the change trend and shape characteristics of the curves, and combine the difference between the actual power consumption and the ideal power consumption to make a secondary judgment on the state of the device, and can verify the device state again from the perspective of energy consumption quantification; at the same time, an environmental adjustment coefficient is introduced to comprehensively consider the influence of environmental temperature, humidity, dust concentration, etc. on the power consumption of the device, and correct the ideal power consumption according to environmental factors, making the judgment result more in line with the actual situation and improving the reliability of the judgment;

[0051] In the energy consumption analysis module, the power change sequence of the monitoring device is deeply analyzed. By counting the zero-value count and the total change, the judgment result of severely aged devices is obtained. Combining with the information of abnormal devices, a device replacement signal is sent to the device management module, realizing real-time monitoring of the energy consumption status of the device, avoiding additional energy consumption caused by device abnormalities or aging, and reducing the overall energy consumption.

[0052] Compared with the prior art, the present invention can more timely and effectively detect and handle device problems, preventing excessive energy consumption of the device. Moreover, by optimizing the operating state of the device, unnecessary energy consumption caused by device problems is reduced, thereby indirectly reducing carbon emissions and contributing to the achievement of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 It is a flowchart of a method for managing the energy consumption of a monitoring device provided by the present invention;

[0055] Figure 2 It is a framework diagram of a system for managing the energy consumption of a monitoring device provided by the present invention;

[0056] Figure 3 It is a flowchart of the working process of the abnormal warning module in a system for managing the energy consumption of a monitoring device provided by the present invention;

[0057] Figure 4 It is a flowchart of the working process of the energy consumption analysis module in a system for managing the energy consumption of a monitoring device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0059] Please refer to Figures 1-4 , the first aspect embodiment of the present invention provides a system for managing the energy consumption of a monitoring device, including:

[0060] A power acquisition module: used to acquire the power consumption of the monitoring device at preset time intervals to obtain power sequence data that changes with time;

[0061] Data storage module: used to store the power sequence data of the monitoring device in the power storage repository, and store the power test change curve of the monitoring device in the power change template library;

[0062] Abnormal warning module: used to extract the power test change curve from the power change template library, obtain abnormal devices and normal devices based on the current power sequence data and power test change curve of the monitoring device, and send a warning signal according to the abnormal devices;

[0063] Energy consumption analysis module: used to analyze the power change of normal devices based on the power sequence data to obtain severely aged devices;

[0064] Device management module: used to obtain the device information of abnormal devices and severely aged devices, and send a device replacement signal.

[0065] Power is the most direct manifestation of device energy consumption. By analyzing the power, the energy consumption level of the device at different times and in different working modes can be accurately understood. Therefore, in the present invention, by collecting the power sequence data of the monitoring device and analyzing it, abnormal conditions and signs of severe aging during the operation of the device are found, so as to realize the effective management of device energy consumption, ensure the device runs in a normal state, avoid additional energy consumption caused by device abnormalities or aging, ultimately achieve the purpose of energy conservation and emission reduction, and at the same time ensure the reliability and service life of the device, and improve the overall performance and management efficiency of the device.

[0066] An energy consumption management system for a monitoring device of the present invention includes a power acquisition module, a data storage module, an abnormal warning module, an energy consumption analysis module, and a device management module. It should be noted that the power acquisition module, the data storage module, the abnormal warning module, the energy consumption analysis module, and the device management module are in communication connection.

[0067] The power acquisition 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 at preset time intervals to obtain the power sequence data that changes with time. This preset time interval can be flexibly set according to the characteristics and actual requirements of the monitoring device, usually in hours or days as the time unit;

[0068] The power acquisition module can record the power consumption information of the monitoring device from the start of work 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 device changing with time can be obtained to show the energy consumption characteristics of the device at different life cycle stages;

[0069] At the same time, it can also extract the power consumption of the device within the most recent time interval at any time, which helps in short-term monitoring and real-time analysis of the device's immediate performance.

[0070] The data storage module of this embodiment is used to store the power sequence data of the monitoring device in the power storage repository and store the power test change curve of the monitoring device in the power change template library.

[0071] In one implementation, the data storage module can classify and store the power sequence data and its corresponding power test change curve according to the category and model of the monitoring device. At the same time, store the power test change curve corresponding to the model in the corresponding area in the power change template library to achieve classified storage, improve the management efficiency and query speed of the data, and facilitate the subsequent module to quickly and accurately locate the required information when calling the data.

[0072] In one implementation, the power test change curve in the power change template library is obtained by conducting full-life cycle tests on various standard monitoring devices in a standard environment. The standard environment covers conditions such as constant temperature, humidity, voltage, and dust concentration, excluding the influence of external interference factors on power consumption to the greatest extent, so as to obtain the purest curve of power change over time of the device in an ideal state. For example, when testing a certain model of sensor, set the ambient temperature constantly at 25 °C, control the humidity at 50%, keep the voltage stable at 220 V, and maintain the dust concentration at a very low level (such as about 0.03 - 0.05 mg / m³), let the sensor complete the whole process from start to end of its service life in such an environment, record the power data at each time point, and finally generate the power test change curve;

[0073] The power test change curve provides a scientific basis for the overall energy consumption management decision-making. Managers can understand the energy consumption level of the device in an ideal state based on this curve. Taking this as the goal, by optimizing the device operation environment, adjusting the working mode, etc., make the energy consumption of the actual device close to or reach the ideal state, so as to achieve the purpose of energy conservation, emission reduction, and reduction of operating costs.

[0074] 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 based on the current power sequence data of the monitoring device and the power test change curve, and alarm the abnormal devices.

[0075] In one implementation, obtaining abnormal devices and normal devices based on the current power sequence data of the monitoring device and the power test change curve includes:

[0076] A1, plotting the power sequence data of the monitoring device into a curve to obtain the power consumption curve;

[0077] A2. Calculate the comprehensive similarity between the power consumption curve and the power consumption test change curve, and determine whether the first similarity is greater than the first preset threshold;

[0078] And the calculation process of the comprehensive similarity is as follows:

[0079] A21. Feature point extraction: Take the derivatives of the power consumption curve and the power consumption test change curve, record the starting point, ending point of the curve, and the points where the first derivative is zero and the second derivative is not zero, and obtain the set of feature points P1 = {p 11 , p 12 , …, p 1n} of the power consumption curve and the set of feature points P2 = {p 21 , p 22 , …, p 2m} of the power consumption test change curve; n and m represent the number of elements in the set of feature points;

[0080] A22. Local curvature calculation: A22. Calculate the local curvature of each feature point in the set of feature points, and obtain the set of local curvatures K1 = {k 11 , k 12 , …, k 1n} of the set of feature points P1 and the set of local curvatures K2 = {k 21 , k 22 , …, k 2m} of the set of feature points P2;

[0081] A23. Time axis alignment: Use the dynamic time warping algorithm to align the set of feature points P1 and the set of feature points P2 on the time axis, and obtain the aligned feature point pairs (p 1i , p 2j );

[0082] A24. Comprehensive similarity calculation: Calculate the comprehensive similarity between the power consumption curve and the power consumption test change curve according to the position similarity formula and the curvature similarity formula;

[0083] Among them, the position similarity formula is: , and respectively represent and coordinates, represents the maximum value of the distances between all aligned feature point pairs;

[0084] The curvature similarity formula is: Sc ij = 1 - k 1i - k 2j / k max , kmax represents the maximum value of all values in the local curvature sets K1 and K2;

[0085] Next, according to the formula S ij = αSp ij + (1 - α)Sc ij the similarity S of the feature point pairs is calculated; ij α represents the weight coefficient, which is obtained based on historical experience;

[0086] Finally, the similarities S of all aligned feature points ij are averaged to obtain the comprehensive similarity S between the power consumption curve and the power test change curve;

[0087] If the first similarity is greater than the first preset threshold, the monitoring device is marked as a normal device; otherwise, the monitoring device is marked as an abnormal device;

[0088] A3. When the comprehensive similarity is greater than the first preset threshold, according to the running time t of the monitoring device, the data at the t-th moment are respectively extracted from the power consumption curve and the power test change curve to obtain the current power data and the current reference data;

[0089] For example, assume there is an intelligent monitoring camera as the monitoring device. The power acquisition module collects power data at intervals of 15 minutes. After a full 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 running time t of the camera since it was put into use is 1000 hours, that is, 60,000 minutes. Then, the last power data in the power sequence data for the day, which represents the data at the t-th moment in the power consumption curve, is extracted as the current power data;

[0090] Similarly, through the full life cycle test of the same model camera in the standard environment before, the power test change curve is obtained. Then, the power data at the 60,000 - minute moment is extracted from this power test change curve to obtain the current reference data;

[0091] A4. Multiply the current reference data by the environment adjustment coefficient to obtain the corrected data; where the environment adjustment coefficient K = (1 + λ 1 [(T - T 0 ) / T 0 ) × (1 + λ 2 [(H - H 0 ) / H 0 ) × (1 + λ 3 [(D - D 0 ) / D 0 ) takes into account the current environmental temperature T, humidity H, dust concentration D and the reference temperature T of the standard environment0 , humidity H 0 , dust concentration D 0 differences, where λ 1 , λ 2 , λ 3 are the proportions of each index, obtained through regression analysis, reflecting the influence degree 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;

[0092] Next, compare whether the difference between the current power consumption data and the corrected data is greater than the second preset threshold. If it is greater, mark the monitoring device as an abnormal device; otherwise, mark it as a normal device.

[0093] In one implementation, obtaining the proportions of each index through regression analysis may include the following operating steps:

[0094] By the method of controlling variables, first fix all other environmental factors and equipment operating conditions except one environmental factor. For example, when studying the influence of temperature on the power consumption of the equipment, keep other factors such as humidity, dust concentration, and the working load of the equipment at a constant level;

[0095] On this basis, change the selected environmental factor (such as temperature), and record the corresponding power consumption of the equipment at different temperature values. Collect multiple sets of such data points to form a data set about temperature and power consumption. It can be set that the temperature gradually increases from a lower value to a higher value, such as increasing from 10°C at intervals of 2°C to 30°C, and record the power consumption of the equipment each time;

[0096] Then, for this data set, use simple linear regression analysis to fit the data. Assume that the power consumption is the dependent variable and the temperature is the independent variable. The fitted linear equation is y = a + λ 1 x + c, where a is the intercept, λ 1 is the temperature index proportion, and c is the error term. Solve this linear regression equation by methods such as the least squares method to obtain the value of λ 1 , which represents the influence degree of temperature change on the power consumption of the equipment under the condition that other conditions remain unchanged;

[0097] Similarly, conduct experiments and analyses on humidity and dust concentration respectively. Finally, obtain three different linear regression equations and the corresponding coefficients λ 1 (temperature), λ 2 (humidity), and λ 3 (dust concentration).

[0098] In the abnormal warning module, by analyzing the key feature points and local curvatures of the power consumption curve and the power test change curve, the similarity information between the two is obtained. Among them, the key feature points are used to represent the turning points of the power change trend of the device in different operating stages, and the local curvature describes the degree of bending of the curve and is used to represent the stability of the energy consumption change. Based on the comprehensive consideration of the key feature points and local curvatures, the similarity information of the two curves can be obtained more accurately, providing a basis for judging whether the device is abnormal.

[0099] In this embodiment, the abnormal warning module accurately identifies abnormal devices by comprehensively considering information such as curve shape and environmental factors, provides timely and accurate information for the maintenance and management of the devices, helps prevent energy waste and device damage caused by the continued operation of the devices in an abnormal state, and improves the reliability of the devices and the efficiency of energy consumption management.

[0100] The energy consumption analysis module of this embodiment is used to analyze the power change of normal devices according to the current power sequence data to obtain severely aged devices.

[0101] Specifically, first obtain the power change sequence according to the current power sequence data ; where Q represents the total amount of data Q in the power change sequence, , and , represents the power value of the q-th data point;

[0102] Count the number of zeros in the power change sequence to obtain the zero value count, and sum several values in the power change sequence to obtain the total change;

[0103] 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;

[0104] Judge whether the zero value ratio is greater than the third preset threshold; if yes, jump to B33; if no, mark the normal device as a severely aged device;

[0105] Judge whether the total change is greater than the fourth preset threshold; if yes, mark the normal device as a severely aged device; if no, do not process.

[0106] The zero value ratio reflects the stable operation of the device within a certain period of time, and the total change reflects the total power consumption change of the device within a certain period of time. By combining these two judgment indicators of the zero value ratio and the total change, both the short-term stable operation of the device and the long-term overall energy consumption fluctuation are considered, which helps to quickly detect the aging signs of the device, avoid the continuous abnormal increase in energy consumption and performance decline caused by device aging, and at the same time reduce the additional energy costs and potential failure risks caused by device aging for the enterprise.

[0107] 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.

[0108] The device management module of this embodiment is used to obtain the device information of abnormal devices and severely aging devices, and send a device replacement signal. The operation and maintenance personnel can replace the abnormal devices and severely aging devices in a timely manner according to the device replacement signal to ensure the normal operation and stability of the overall system.

[0109] A second aspect embodiment of the present invention provides an energy consumption management method for monitoring devices, including:

[0110] S1, Collect the power consumption of the monitoring device at preset time intervals to obtain the power sequence data and power consumption curve that change with time;

[0111] S2, 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;

[0112] S3, Extract the power test change curve from the power change template library, and respectively 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 device 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 corrected data; and,

[0113] Mark 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 the difference between the corrected data and the current power data is less than the second preset threshold as a normal device; otherwise, mark it as an abnormal device and send a warning signal according to the abnormal device;

[0114] S4, Analyze the power change situation of the normal device according to the current power sequence data to obtain severely aging devices;

[0115] S5, Replace the devices of abnormal devices and severely aging devices.

[0116] Some data in the above formula are calculated by removing the dimension and taking their numerical values. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the real situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained by simulating a large amount of data.

[0117] The working principle of the present invention:

[0118] Through the power consumption acquisition module, the power consumption of the monitoring device is acquired at preset time intervals, and the power consumption sequence data varying with time is obtained, providing a basic data source for the analysis of energy consumption management; through the data storage module, the power consumption sequence data of the monitoring device is stored in the power consumption repository, and the power consumption test change curve is stored in the power consumption change template library, effectively organizing and storing the data, facilitating subsequent modules to call at any time and being convenient for comparative analysis;

[0119] Then, through the abnormal warning module, the power consumption test change curve is extracted from the power consumption change template library, combined with the current power consumption sequence data, to judge the operating state of the device, distinguish normal and abnormal devices, accurately discover abnormal situations in the device operation and send warning signals, realizing the effective monitoring of the abnormal state of the device and avoiding the increase in energy consumption caused by device abnormalities; through the energy consumption analysis module, the power consumption change of normal devices is analyzed, the power consumption change sequence is calculated, the zero value count and the total change are statistically calculated, and it is judged whether the device is severely aged according to the set threshold, discovering in advance the energy consumption change caused by the decline in device performance, providing a basis for device maintenance and avoiding long-term high energy consumption caused by device aging;

[0120] Finally, through the device management module, the information of abnormal devices and severely aged devices is obtained and a device replacement signal is sent, coordinating the update and maintenance process of the device, and realizing the effective management of energy consumption.

[0121] 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 of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced 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: The power collection module is used to collect the power consumption of the monitoring device according to the preset time interval to obtain the power sequence data and power consumption curve that change with time; wherein the power sequence data includes the power consumption of the monitoring device collected at multiple time points; 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; 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 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, and the standard environment includes constant temperature, humidity, voltage and dust concentration, and the calculation process of 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 ) 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 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 of the power consumption curve and the power test change curve; Energy consumption analysis module: used to analyze the power changes of normal devices according to the power sequence data to obtain severely aged devices; wherein the determination rule of the severely aged devices is: 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; 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, marking the normal device as a severely aged device; 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 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.

4. 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 3, characterized in that: include: The power consumption of the monitoring device is collected according to a preset time interval to obtain power sequence data and a power consumption curve that change with time; wherein the power sequence data includes the power consumption of the monitoring device collected at multiple time points; 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 value and whose difference between the correction 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 an early warning signal is sent according to the abnormal device; wherein the calculation process of 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 ) 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 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 of the power consumption curve and the power test change curve; According to the power sequence data, the power changes of normal devices are analyzed to obtain severely aged devices. The determination rule of the severely aged devices is as follows: 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; 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, marking the normal device as a severely aged device; Obtain device information of abnormal devices and severely aged devices, and send device replacement signals.

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