Electromechanical equipment operation energy consumption data processing method based on big data

Abstract

The invention discloses an electromechanical equipment operation energy consumption data processing method based on big data, and belongs to the technical field of equipment analysis, and the method specifically comprises the following steps: collecting an infrared image of the whole electromechanical equipment in real time based on an infrared sensor; an infrared image sequence is generated according to the time, a temperature change area in the electromechanical equipment in unit time is recognized, whether the temperature of the temperature change area exceeds a preset threshold value or not is judged, and if yes, an alarm is given; an abnormal area in the temperature change areas is screened, a maintenance signal is sent out, the temperature change speed of the abnormal area is calculated, if the temperature change speed is larger than a preset threshold value, the power consumption of the current electromechanical equipment is obtained, and if the power consumption change speed is smaller than a preset power value, it is judged that temperature control of the current electromechanical equipment is abnormal; according to the invention, effective analysis of the electromechanical equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment analysis, and in particular to a method for processing electromechanical equipment operation energy consumption data based on big data. Background Art

[0002] With the continuous development of the times and the continuous improvement of the level of industrialization, the types of electromechanical equipment are increasing. Electromechanical equipment is widely used in various fields and is one of the important equipment that is indispensable in modern industrial production. With the continuous advancement of science and technology, electromechanical equipment is also developing continuously and has become an indispensable part of the modern industrialization process. Electromechanical equipment can be divided into transmission devices, control systems and other auxiliary equipment, such as hydraulic systems, pneumatic systems, etc. according to their functions; they can be divided into manufacturing, transportation, construction, municipal administration, etc. according to their application fields; they can be divided into traditional electromechanical equipment and intelligent electromechanical equipment according to their technical levels.

[0003] Electromechanical equipment plays an increasingly important role in the production process. However, the energy consumption of electromechanical equipment has always been one of the difficult problems in industrial production. Traditional energy consumption monitoring methods mainly rely on manual inspections and regular maintenance, which is not only inefficient but also prone to missed detections and false detections. Therefore, how to achieve real-time monitoring and early warning of the energy consumption of electromechanical equipment has become a problem that needs to be solved.

[0004] When electromechanical equipment is running for a long time, each component is powered on for a long time. When component failure or material aging occurs, it will affect the circuit, increase energy consumption, and generate excess heat, thus affecting the operation and energy consumption of the electromechanical equipment. Therefore, it is necessary to monitor the temperature of the electromechanical equipment and issue early warnings or alarms in time. Summary of the invention

[0005] The purpose of the present invention is to provide a method for processing electromechanical equipment operation energy consumption data based on big data to solve the following technical problems:

[0006] When electromechanical equipment is running for a long time, each component is powered on for a long time. When component failure or material aging occurs, it will affect the circuit, increase energy consumption, and generate excess heat, thus affecting the operation and energy consumption of the electromechanical equipment. Therefore, it is necessary to monitor the temperature of the electromechanical equipment and issue early warnings or alarms in time.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A method for processing electromechanical equipment operation energy consumption data based on big data comprises the following steps:

[0009] Collect infrared images of the entire electromechanical equipment in real time based on infrared sensors;

[0010] Generate an infrared image sequence according to the time sequence, identify the area of ​​temperature change in the electromechanical device within the unit time t according to the infrared image sequence, and determine whether the temperature of the temperature change area exceeds a preset threshold, if so, issue an alarm, if not, continue to identify;

[0011] Screen the abnormal area in the temperature change area, send out a maintenance signal, and calculate the temperature change rate v1 of the abnormal area within the unit time t. If the temperature change rate is greater than the preset threshold, obtain the power consumption of the current electromechanical equipment, and collect the change rate v2 of the power consumption of the electromechanical equipment. If the change rate v2 is less than the corresponding preset power value, it is determined that the temperature control of the current electromechanical equipment is abnormal:

[0012] The warning time of the electromechanical equipment is calculated according to the temperature change speed. When the warning time is less than a preset threshold, a warning is issued, otherwise the identification continues.

[0013] As a further solution of the present invention: the specific process of identifying the temperature change area is:

[0014] The infrared image sequence is gray-processed to obtain a gray-scale image sequence, a rectangular coordinate system is established with the central pixel of the gray-scale image as the origin, the coordinates (x, y) of all pixels in the gray-scale image are obtained, and the gray-scale values ​​G of all pixels in the gray-scale image sequence are obtained respectively. i (x, y), G represents the grayscale value, i represents the order of the grayscale image corresponding to the current pixel in the grayscale image sequence, i=1,…,n, calculate the grayscale difference D between the pixels with the same coordinates in adjacent grayscale images, if the grayscale difference D>d, d is the natural temperature change threshold, then the corresponding pixel is the temperature change area, if the grayscale difference D≤d, then the corresponding pixel is the no-change area.

[0015] As a further solution of the present invention: the setting process of the natural temperature change threshold d is:

[0016] Obtain the generation date and time of the current infrared image, estimate the current temperature change rate v, calculate the temperature change value ΔT per unit time t based on the temperature change rate, calculate the corresponding background radiation intensity change based on the temperature change value ΔT and the temperature starting point, calculate the corresponding grayscale change value based on the background radiation intensity change, and mark the grayscale change value as the natural temperature change threshold.

[0017] As a further solution of the present invention: the temperature change areas are numbered and sorted, the temperature values ​​T1, T2, ..., Tn of each temperature change area are obtained in turn, and the proportions P1, P2, ..., Pn of each temperature change area to the overall electromechanical equipment are calculated in turn, and the temperature warning value Y is calculated according to the formula Y = (T1*P1+T2*P2+...+Tn*Pn) / n. When the temperature warning value exceeds the preset threshold, a warning is issued.

[0018] As a further solution of the present invention: the process of screening abnormal areas is:

[0019] Select the temperature of one of the temperature change areas as the value to be tested, and mark the temperatures of the remaining temperature change areas as detection values. Calculate the difference between the value to be tested and all the detection values, and calculate the mean of the differences. If the mean of the differences is greater than the preset threshold and the variance of all the detection values ​​is less than the preset threshold, the temperature change area corresponding to the value to be tested is determined to be an abnormal area, otherwise continue to identify.

[0020] As a further solution of the present invention: the process of screening abnormal areas is:

[0021] If the mean of the difference between the measured value and the detection value is less than the preset threshold, and the variance of all detection values ​​is greater than the preset threshold, the temperature mean of the detection values ​​is calculated, and the detection value with the largest difference from the temperature mean is selected, and the temperature change area corresponding to the detection value is determined as an abnormal area, otherwise continue to identify.

[0022] As a further solution of the present invention: if the mean of the difference between the measured value and the detection value is less than a preset threshold, and the variance of all detection values ​​is less than the preset threshold, the mean of the temperature change values ​​of all temperature change areas is calculated, and if the mean is greater than the preset threshold, an alarm is issued.

[0023] As a further solution of the present invention: for electromechanical equipment that is determined to have temperature control abnormalities, a fault report is generated, including the precise location of the abnormal area, temperature change data, and power consumption change status, and the fault report is uploaded to the central monitoring system.

[0024] Beneficial effects of the present invention:

[0025] The present invention collects infrared images of the entire electromechanical equipment through infrared sensors, thereby facilitating monitoring of the overall temperature status of the electromechanical equipment, facilitating real-time control of abnormal conditions that may occur in various parts of the electromechanical equipment, and conducting all-round management and control; and by identifying the temperature change area in the infrared image through a frame difference sequence, the area where the temperature in the electromechanical equipment changes is quickly located, an alarm is issued in time, and the temperature change area is further screened to screen out abnormal areas with temperatures that are significantly different from those in most areas, and timely maintenance alarms are issued for the abnormal areas, thereby eliminating safety hazards in advance and achieving comprehensive safety protection for the electromechanical equipment. At the same time, by monitoring the changes in power consumption, the temperature control performance of the equipment can be further evaluated, providing data support for energy-saving optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below in conjunction with the accompanying drawings.

[0027] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 As shown, the present invention is a method for processing electromechanical equipment operation energy consumption data based on big data, comprising the following steps:

[0030] 1. Use highly sensitive infrared sensors to monitor electromechanical equipment in real time and capture the temperature distribution on the surface of the equipment. These infrared images can reflect the temperature changes of various parts of the equipment during operation, providing basic data for subsequent analysis and judgment. Through infrared images, it is possible to visually observe whether there are areas of overheating or abnormal temperature in the equipment.

[0031] 2. Arrange the continuously collected infrared images in chronological order to form a series of image sequences. Use image processing algorithms to analyze these sequences and identify areas where the temperature changes significantly within a unit time. Monitor the temperature of these areas. If the temperature exceeds the preset safety threshold, the system will immediately issue an alarm to remind the operator to take appropriate measures.

[0032] 3. In the identified temperature change areas, further screen out areas that may have faults or abnormalities. For these abnormal areas, the system will send out a maintenance signal to notify maintenance personnel to conduct inspections and repairs. At the same time, the temperature change rate v1 of these areas per unit time is calculated to assess the severity and urgency of the problem.

[0033] 4. If the temperature change rate v1 of the abnormal area exceeds the preset threshold, it indicates that there may be a serious fault or performance degradation. At this time, the system will obtain the power consumption of the current electromechanical equipment and collect the change rate of power consumption v2. By analyzing the power consumption, we can further understand the operating status and energy efficiency of the equipment.

[0034] 5. Compare the collected power consumption change rate v2 with the preset power value. If v2 is less than the preset value, it means that there may be a problem with the temperature control system of the electromechanical equipment and the temperature of the equipment cannot be effectively controlled. In this case, the temperature control system needs to be checked and repaired to ensure the safe and stable operation of the equipment.

[0035] 6. Using the temperature change rate v1, combined with the thermodynamic characteristics of the equipment and historical operating data, calculate the possible warning time. This warning time refers to the time point when the equipment may fail or its performance may deteriorate under the current operating state. By giving early warning, more time can be gained for equipment maintenance and care, avoiding the impact of sudden failures on production.

[0036] 7. If the calculated warning time is less than the set safety threshold, it means that the equipment is at a high risk and may fail in a short time. At this time, the system will immediately send out a warning signal to remind the operator to pay attention and take necessary measures to prevent the failure. This early warning mechanism helps to improve the reliability and stability of the equipment and ensure the smooth progress of production.

[0037] The present invention collects infrared images of the entire electromechanical equipment through infrared sensors, thereby facilitating monitoring of the overall temperature status of the electromechanical equipment, facilitating real-time control of abnormal conditions that may occur in various parts of the electromechanical equipment, and conducting all-round management and control; and by identifying the temperature change area in the infrared image through a frame difference sequence, the area where the temperature in the electromechanical equipment changes is quickly located, an alarm is issued in time, and the temperature change area is further screened to screen out abnormal areas with temperatures that are significantly different from those in most areas, and timely maintenance alarms are issued for the abnormal areas, thereby eliminating safety hazards in advance and achieving comprehensive safety protection for electromechanical equipment.

[0038] In a preferred embodiment of the present invention, the specific process of identifying the temperature change area is:

[0039] 1. Grayscale processing: grayscale processing is performed on the collected infrared image sequence to obtain a grayscale image sequence. This step is to convert complex color images into simple black and white images to facilitate subsequent image processing and analysis.

[0040] 2. Establish a coordinate system: Take the central pixel of the grayscale image as the origin and establish a rectangular coordinate system. Through this coordinate system, the position of each pixel can be accurately located.

[0041] Get pixel coordinates and grayscale values: Get the coordinates (x, y) of all pixels in the grayscale image, and record the grayscale value Gi (x, y) of each pixel in the grayscale image sequence. Here, G represents the grayscale value, and i represents the order of the grayscale image corresponding to the current pixel in the grayscale image sequence, i = 1, ..., n.

[0042] 3. Calculate the grayscale difference: Calculate the grayscale difference D between the pixels with the same coordinates in adjacent grayscale images. This grayscale difference reflects the brightness change of the pixel between two consecutive frames of images.

[0043] 4. Determine the temperature change area: If the grayscale difference D is greater than the preset natural temperature change threshold d, the pixel is considered to belong to the temperature change area. This means that the temperature of the area has changed significantly during this time interval. On the contrary, if the grayscale difference D is less than or equal to the threshold d, the pixel is considered to belong to the no-change area.

[0044] In a preferred case of this embodiment, the setting process of the natural temperature change threshold d is:

[0045] 1. The system first records the date and time when the current infrared image was generated. This information is critical for subsequent temperature change rate estimates because it provides a reference point for environmental conditions to change over time. For example, if the image was acquired at noon in the summer, the expected temperature may be higher than in the early morning or evening. By accurately recording these timestamps, the accuracy and relevance of subsequent analysis can be ensured.

[0046] 2. Using historical meteorological data and known environmental parameters such as season, weather conditions, equipment operating load, etc., the system estimates the current temperature change rate v. This estimate is based on a statistical analysis of past temperature changes under similar conditions. For example, if historical data shows that the surface temperature of the equipment usually rises rapidly on a hot summer afternoon, the system will predict a higher temperature change rate. This estimate helps set a reasonable natural temperature change threshold to distinguish between normal temperature fluctuations and possible abnormal conditions.

[0047] 3. Based on the estimated temperature change rate v, the system calculates the expected temperature change ΔT within a given time interval t. This calculation takes into account the thermal inertia of the device and the influence of environmental factors, thereby providing a dynamic range of temperature changes. For example, if the device usually takes some time to respond to changes in ambient temperature, then even in the case of rapid temperature rise, the actual temperature change will have a certain delay. In this way, the temperature change trend in a short period of time can be predicted more accurately.

[0048] 4. Next, the system determines the change in background radiation intensity based on the calculated temperature change value ΔT and the initial temperature reading. Since infrared images are generated by capturing infrared radiation from the surface of an object, any change in temperature will result in a change in radiation intensity. By understanding this relationship, subtle differences in infrared images can be more accurately interpreted and related to actual temperature changes.

[0049] 5. Once the change in background radiation intensity is known, the system can convert it into grayscale change values ​​on the image. This is because the infrared camera captures the energy distribution of infrared radiation, and radiation with different energy levels will appear as different grayscale levels on the image. In this way, abstract temperature data can be converted into intuitive visual information, allowing operators to more easily identify areas of temperature change.

[0050] 6. The final step is to set the grayscale change value calculated earlier as the natural temperature change threshold d. This threshold represents the maximum expected temperature change due to natural environmental factors in the absence of external interference. Any temperature change exceeding this threshold will be considered abnormal and may require further inspection or intervention. In this way, the system is able to automatically adjust its sensitivity to adapt to changing external environmental conditions while reducing the possibility of false alarms.

[0051] In another preferred embodiment of the present invention, the temperature change areas are numbered and sorted, the temperature values ​​T1, T2, ..., Tn of each temperature change area are obtained in turn, and the proportions P1, P2, ..., Pn of each temperature change area to the overall electromechanical equipment are calculated in turn, and the temperature warning value Y is calculated according to the formula Y = (T1*P1+T2*P2+...+Tn*Pn) / n. When the temperature warning value exceeds a preset threshold, a warning is issued.

[0052] In another preferred embodiment of the present invention, the process of screening abnormal areas is:

[0053] The temperature of one of the temperature change areas is selected as the value to be tested, and the temperatures of the remaining temperature change areas are marked as the test values.

[0054] Calculate the difference between the value to be tested and all the test values ​​respectively, and calculate the mean of these differences.

[0055] If the mean of the difference is greater than the preset threshold, and the variance of all the detected values ​​is less than the preset threshold, the temperature change region corresponding to the value to be detected is determined as an abnormal region. Otherwise, continue to identify other possible abnormal regions.

[0056] In a preferred case of this embodiment, the process of screening abnormal areas further includes:

[0057] If the mean of the difference between the measured value and the detection value is less than the preset threshold, but the variance of all the detection values ​​is greater than the preset threshold, the temperature mean of the detection values ​​is calculated.

[0058] The detection value with the largest difference from the temperature average is selected from all the detection values, and the temperature change area corresponding to the detection value is determined as the abnormal area. Otherwise, continue to identify other possible abnormal areas.

[0059] In another preferred case of this embodiment, if the mean of the difference between the measured value and the detection value is less than the preset threshold, and the variance of all detection values ​​is also less than the preset threshold, the mean of the temperature change values ​​of all temperature change areas is calculated. If the mean is greater than the preset threshold, an alarm is issued, indicating that the electromechanical equipment may have an overall temperature control problem or abnormal operation.

[0060] In another preferred situation of this embodiment, for the electromechanical equipment that is determined to have temperature control abnormalities, a fault report is generated, including the precise location of the abnormal area, temperature change data, and power consumption change status, and the fault report is uploaded to the central monitoring system.

[0061] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for processing electromechanical equipment operation energy consumption data based on big data, characterized in that: The following steps are involved: Collect infrared images of the entire electromechanical equipment in real time based on infrared sensors; Generate an infrared image sequence according to the time sequence, identify the area of ​​temperature change in the electromechanical device within the unit time t according to the infrared image sequence, and determine whether the temperature of the temperature change area exceeds a preset threshold, if so, issue an alarm, if not, continue to identify; Screen the abnormal area in the temperature change area, send out a maintenance signal, and calculate the temperature change rate v1 of the abnormal area within the unit time t. If the temperature change rate is greater than the preset threshold, obtain the power consumption of the current electromechanical equipment, and collect the change rate v2 of the power consumption of the electromechanical equipment. If the change rate v2 is less than the corresponding preset power value, it is determined that the temperature control of the current electromechanical equipment is abnormal: The warning time of the electromechanical equipment is calculated according to the temperature change speed. When the warning time is less than a preset threshold, a warning is issued, otherwise the identification continues.

2. According to the method for processing electromechanical equipment operation energy consumption data based on big data according to claim 1, it is characterized in that: The specific process of identifying the temperature change area is: The infrared image sequence is gray-processed to obtain a gray-scale image sequence, a rectangular coordinate system is established with the central pixel of the gray-scale image as the origin, the coordinates (x, y) of all pixels in the gray-scale image are obtained, and the gray-scale values ​​G of all pixels in the gray-scale image sequence are obtained respectively. i (x, y), G represents the grayscale value, i represents the order of the grayscale image corresponding to the current pixel in the grayscale image sequence, i=1,…,n, calculate the grayscale difference D between the pixels with the same coordinates in adjacent grayscale images, if the grayscale difference D>d, d is the natural temperature change threshold, then the corresponding pixel is the temperature change area, if the grayscale difference D≤d, then the corresponding pixel is the no-change area.

3. According to the method for processing electromechanical equipment operation energy consumption data based on big data in claim 2, it is characterized in that: The setting process of the natural temperature change threshold d is: Obtain the generation date and time of the current infrared image, estimate the current temperature change rate v, calculate the temperature change value ΔT per unit time t based on the temperature change rate, calculate the corresponding background radiation intensity change based on the temperature change value ΔT and the temperature starting point, calculate the corresponding grayscale change value based on the background radiation intensity change, and mark the grayscale change value as the natural temperature change threshold.

4. According to the method for processing electromechanical equipment operation energy consumption data based on big data in claim 1, it is characterized in that: The temperature change areas are numbered and sorted, and the temperature values ​​T1, T2, ..., Tn of each temperature change area are obtained in turn, and the proportions P1, P2, ..., Pn of each temperature change area to the whole electromechanical equipment are calculated in turn. The temperature warning value Y is calculated according to the formula Y = (T1*P1+T2*P2+...+Tn*Pn) / n. When the temperature warning value exceeds the preset threshold, a warning is issued.

5. The method for processing electromechanical equipment operation energy consumption data based on big data according to claim 1 is characterized in that: The process of screening abnormal areas is: Select the temperature of one of the temperature change areas as the value to be tested, and mark the temperatures of the remaining temperature change areas as detection values. Calculate the difference between the value to be tested and all the detection values, and calculate the mean of the differences. If the mean of the differences is greater than the preset threshold and the variance of all the detection values ​​is less than the preset threshold, the temperature change area corresponding to the value to be tested is determined to be an abnormal area, otherwise continue to identify.

6. A method for processing electromechanical equipment operation energy consumption data based on big data according to claim 5, characterized in that: The process of screening abnormal areas also includes: If the mean of the difference between the measured value and the detection value is less than the preset threshold, and the variance of all detection values ​​is greater than the preset threshold, the temperature mean of the detection values ​​is calculated, and the detection value with the largest difference from the temperature mean is selected, and the temperature change area corresponding to the detection value is determined as an abnormal area, otherwise continue to identify.

7. A method for processing electromechanical equipment operation energy consumption data based on big data according to claim 6, characterized in that: If the mean of the difference between the measured value and the detection value is less than the preset threshold, and the variance of all detection values ​​is less than the preset threshold, the mean of the temperature change values ​​of all temperature change areas is calculated, and if the mean is greater than the preset threshold, an alarm is issued.

8. The method for processing electromechanical equipment operation energy consumption data based on big data according to claim 1 is characterized in that: For electromechanical equipment that is judged to have temperature control abnormalities, a fault report is generated, including the precise location of the abnormal area, temperature change data, and power consumption changes, and the fault report is uploaded to the central monitoring system.

Images