Data Fusion Method Based on Multi-Source Sensors

Abstract

The present invention provides a data fusion method based on multi-source sensors, which relates to the technical field of equipment status monitoring. The present invention obtains the thermal imaging map of the monitored equipment, and obtains the calorific value matrix, area anomaly index, and temperature anomaly index according to the thermal imaging map to form the anomaly index of each thermal imaging map. It obtains the sequence of change amounts of the temperature increase process, starts further monitoring according to the judgment and obtains the further monitoring time, obtains the vibration curve and voltage curve, generates the further monitoring score, and continuously obtains the thermal imaging map. According to the further monitoring score, the change amount of the anomaly index during the further monitoring process, and the change amount of the anomaly index during the temperature increase process, it forms the further monitoring index to judge the equipment. By means of data fusion of multi-source sensors, the present invention monitors the equipment in all directions and accurately realizes the judgment of the operating state of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment status monitoring, and specifically to a data fusion method based on multi-source sensors. Background Art

[0002] During the operation of modern industrial equipment, the stability and safety of the equipment are crucial. Traditional equipment monitoring methods mainly rely on a single sensor, such as a temperature sensor or a vibration sensor, for data collection and anomaly detection. However, these single-sensor methods have many deficiencies. In recent years, with the rapid development of sensor technology and data processing technology, multi-source sensor data fusion methods have gradually become a research hotspot. By integrating multiple sensors to obtain multi-dimensional data of the equipment, the operating state of the equipment can be reflected more comprehensively and accurately.

[0003] In the prior art, the publication number CN111754483A discloses a method and system for identifying abnormal equipment based on a thermal imager. The method includes: S1: Obtain the current location information and environmental information; SZ: Generate and collect an infrared thermal image according to the environmental information, and identify the temperatures of all pixel points in the infrared thermal image; S3: Determine whether the temperature of each pixel point exceeds a threshold. If there are pixel points whose temperature exceeds the threshold, jump to step S4. If not, skip the infrared thermal image; S4: Collect a visible light image corresponding to the infrared thermal image; S5: Extract the corresponding equipment according to the visible light image and the infrared thermal image; S6: Identify the type and model of the equipment and the common temperature anomaly fault types corresponding to the equipment according to the extracted equipment; S7: Transmit the type and model of the equipment, the common temperature anomaly fault types, the current location data, and the alarm information to the data monitoring center.

[0004] Although the disclosed technical document realizes the monitoring of equipment through a thermal imager, it does not achieve the collaborative monitoring of multiple sensors. Judging the state of the monitored equipment solely from the visual level is not comprehensive and accurate enough, and it is easy to have errors in judging the equipment state.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a data fusion method based on multi-source sensors to solve the problems raised in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] The data fusion method based on multi-source sensors specifically includes the following steps:

[0009] Obtain the thermal imaging map of the monitored device at a fixed frequency, and obtain the calorific value matrix of each thermal imaging map within the sliding time window. Set the calorific value threshold and obtain the abnormal pixel points in the calorific value matrix. Obtain the area abnormality index and temperature abnormality index respectively according to the abnormal pixel points, and form the abnormality index of each thermal imaging map.

[0010] Form the abnormality index sequence of the thermal imaging maps within the sliding time window and obtain the sequence of the change amount of the temperature increase process. Set the abnormality index threshold, and start further monitoring according to the relationship between the abnormality index threshold and the abnormality index, and obtain the temperature increase process.

[0011] Obtain the further monitoring time according to the temperature increase process. Obtain the vibration curve and voltage curve of the monitored device during the further monitoring time, and obtain the further monitoring score.

[0012] Continuously obtain the thermal imaging map of the monitored device during the further monitoring time, and obtain the change amount of the abnormality index during the further monitoring process. Form the further monitoring index according to the further monitoring score, the change amount of the abnormality index during the further monitoring process, and the change amount of the abnormality index during the temperature increase process. Set threshold Ⅰ and threshold Ⅱ respectively, and judge whether the device is abnormal according to the relationship between the further monitoring index and threshold Ⅰ and threshold Ⅱ.

[0013] Further, obtain the thermal imaging map of the monitored device at a fixed frequency. The thermal imaging is obtained by a thermal imager. Add a time stamp to each obtained thermal imaging map, and process the thermal imaging map by the median filtering method. Set the sliding window time to 1 hour, and obtain the calorific value matrix of each thermal imaging map within the sliding window time according to the pixel points in the thermal imaging map. The formula is as follows: ;

[0014] Among them, is the calorific value matrix of the th thermal imaging map, is the calorific value of the pixel point in the th row and the th column, , , is the total number of thermal imaging maps, , is the number of rows of pixel points in the thermal imaging map, is the number of columns of pixel points in the thermal imaging map;

[0015] Set the calorific value threshold, calibrate the pixel points whose calorific value exceeds the calorific value threshold, and obtain the abnormal pixel points. The logic is as follows:

[0016] The thermal imaging map is binarized to obtain a binary mask for each thermal imaging map. Pixel points with a thermal value not exceeding the thermal value threshold are converted to black, and pixel points with a thermal value exceeding the thermal value threshold are converted to white. The white pixel points are the abnormal pixel points.

[0017] Furthermore, area analysis is performed on the abnormal pixel points in the thermal imaging map to obtain the area anomaly index of the abnormal pixel points. The formula is as follows:

[0018] ;

[0019] Where, is the area anomaly index of the th thermal imaging map, represents the number of abnormal pixel points in the th thermal imaging map, is the total number of pixel points in the thermal imaging map, represents the spatial aggregation degree of the pixel points in the th thermal imaging map, represents the time difference, are all coefficients, , , is the retrieval variable of the thermal imaging map number, , ;

[0020] The logic for obtaining the spatial aggregation degree is as follows:

[0021] The positions of each abnormal pixel point in the thermal imaging map are obtained and numbered respectively. The position is the row and column data of the abnormal pixel point in the thermal imaging map. The adjacency relationship between any two abnormal pixel points is obtained. The formula is as follows:

[0022] ;

[0023] Where, represents the adjacency relationship value between the th and the th abnormal pixel points, and respectively represent the positions of the th and the th abnormal pixel points;

[0024] The spatial aggregation degree is obtained. The formula is as follows: ;

[0025] Where, is the spatial aggregation degree, represents the adjacency relationship value between the th and the th abnormal pixel points, is the number of abnormal pixel points;

[0026] The time difference represents the difference between the timestamps of two adjacent thermal images.

[0027] Furthermore, perform temperature analysis on the abnormal pixel points in the thermal image to obtain the temperature anomaly index of the abnormal pixel points. The formula is as follows: ;

[0028] Where, is the temperature anomaly index of the th thermal image, is the sum of the calorific values of all abnormal pixel points in the th thermal image, are all coefficients, , is the retrieval variable of the thermal image number, ;

[0029] Obtain the anomaly index of each thermal image. The formula is as follows: ; s

[0030] Where, The anomaly index of the th thermal image, is the area anomaly index of the th thermal image, is the temperature anomaly index of the th thermal image, is the retrieval variable of the thermal image number, .

[0031] Furthermore, form an anomaly index sequence of the thermal images within the time window in chronological order, and obtain the change amount of the anomaly index sequence. The formula is as follows: ;

[0032] Where, is the change amount of the anomaly index, The anomaly index of the th thermal image, The anomaly index of the th thermal image, is the retrieval variable of the thermal image number, ;

[0033] Set the anomaly index threshold. When the anomaly index exceeds the anomaly index threshold, start further monitoring, and at the same time trace the temperature increase process. The logic for obtaining the temperature increase process is:

[0034] When the anomaly index exceeds the anomaly index threshold, check the change amount of the anomaly index sequence from back to front, and obtain a subsequence in which the anomaly index continuously increases and contains the last anomaly index. This subsequence is the temperature increase process.

[0035] Form a sequence of change amounts of the anomaly index during the temperature increase process.

[0036] Furthermore, obtain the further monitoring time according to the sequence of change amounts of the anomaly index during the temperature increase process. The formula is as follows: ;

[0037] where, is the further monitoring time, is the average value of the sequence of change amounts of the anomaly index during the temperature increase process, is the standard deviation of the sequence of change amounts of the anomaly index during the temperature increase process, is the change rate of the sequence of change amounts of the anomaly index during the temperature increase process, is the average value threshold, is the standard deviation threshold, is the change rate threshold, is the time of the temperature increase process;

[0038] The formula for the time of the temperature increase process is as follows: ;

[0039] where, represents the timestamp of the th thermal imaging diagram during the temperature increase process, represents the timestamp of the first thermal imaging diagram during the temperature increase process;

[0040] The formula for the change rate of the anomaly index is as follows: ;

[0041] where, is the change rate, represents the anomaly index of the th thermal imaging diagram during the temperature increase process, represents the anomaly index of the first thermal imaging diagram during the temperature increase process.

[0042] Furthermore, during the further monitoring process, obtain the vibration curve and voltage curve of the device. The vibration curve is obtained by a vibration sensor, and the voltage curve is obtained by an oscilloscope. The horizontal axes of the vibration curve and voltage curve are time, the start time is the moment when the further monitoring is started, the time length is the further monitoring time, the vertical axis of the vibration curve is amplitude, and the vertical axis of the voltage curve is voltage. Obtain the further monitoring score:

[0043] ;

[0044] Among them, For further monitoring score, is the initial score value, is the vibration curve, is the initial amplitude, is the voltage curve, is the initial voltage, is the further monitoring time, and are coefficients, ;

[0045] The initial score value is a preset value, the initial amplitude is the amplitude of the equipment during normal operation, and the initial voltage is the voltage of the equipment during normal operation.

[0046] Furthermore, during the further monitoring process, continuously obtain the thermal imaging map of the monitored equipment and form the abnormal index change amount during the further monitoring process, and generate the further monitoring index. The basis formula is as follows:

[0047] ;

[0048] Among them, is the further monitoring index, is the th abnormal index change amount during the further monitoring process, is the number of abnormal index change amounts during the further monitoring process, is the th abnormal index change amount in the temperature increase process change amount sequence, is the number of abnormal index change amounts in the temperature increase process change amount sequence, is the further monitoring score, is the initial score value.

[0049] Furthermore, respectively set the threshold Ⅰ and threshold Ⅱ of the further monitoring index, and make a judgment according to the further monitoring index. The logic is as follows:

[0050] When the further monitoring index is less than or equal to the threshold Ⅰ, the equipment continues to work in the current state;

[0051] When the further monitoring index is greater than the threshold Ⅰ and less than the threshold Ⅱ, the equipment starts the heat dissipation system and continues to work;

[0052] When the further monitoring index is greater than or equal to the threshold Ⅱ, the equipment starts the heat dissipation system and alarms, and the maintenance personnel intervene in the management.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] The present invention obtains the anomaly index of the monitored equipment through a thermal image, conducts further detection based on the anomaly index and obtains the temperature increase process. During the further monitoring process, a vibration curve, a voltage curve, and a thermal image during the further monitoring process are obtained and analyzed to obtain a further monitoring index. The further monitoring index is combined with threshold I and threshold II to judge the monitored equipment. The present invention analyzes the data obtained by multiple sensors to form a further monitoring index, fuses the data of multi-source sensors, monitors the equipment in all directions, and accurately realizes the judgment of the operating state of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0057] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0058] Embodiment:

[0059] Please refer to Figure 1 , the present invention provides a technical solution:

[0060] Based on the data fusion method of multi-source sensors, the specific steps include:

[0061] Step 1: Obtain the thermal image of the monitored equipment at a fixed frequency, and obtain the calorific value matrix of each thermal image within a sliding time window. Set a calorific value threshold and obtain the abnormal pixel points in the calorific value matrix. Obtain the area anomaly index and the temperature anomaly index respectively according to the abnormal pixel points, and form the anomaly index of each thermal image;

[0062] The content of the said step 1 includes the following:

[0063] Step 101: Obtain the thermal imaging map of the monitored device at a fixed frequency. The thermal imaging is obtained by a thermal imager. Add a timestamp to each obtained thermal imaging map, and process the thermal imaging map by the median filtering method. Set the sliding window time to 1 hour, and obtain the heat value matrix of each thermal imaging map within the sliding window time according to the pixel points in the thermal imaging map. The basis formula is as follows: ;

[0064] Among them, is the heat value matrix of the th thermal imaging map, is the heat value of the pixel point in the th row and the th column, , is the total number of thermal imaging maps, , is the number of rows of pixel points in the thermal imaging map, is the number of columns of pixel points in the thermal imaging map;

[0065] Among them, organize the heat values of each pixel point in the thermal imaging map by row and column positions to form a two-dimensional heat value matrix, and completely retain the thermal distribution spatial information on the device surface.

[0066] Set the heat value threshold, calibrate the pixel points whose heat values exceed the heat value threshold, and obtain the abnormal pixel points. The logic is as follows:

[0067] Perform binarization processing on the thermal imaging map to obtain the binary mask of each thermal imaging map. The pixel points whose heat values do not exceed the heat value threshold turn black, and the pixel points whose heat values exceed the heat value threshold turn white. The white pixel points are the abnormal pixel points.

[0068] In this step, obtaining the thermal imaging map at a fixed frequency ensures the continuity and timeliness of data collection. The median filtering method is adopted, and the neighborhood window size is set to 3X3. Arrange the heat values within the neighborhood window in descending order, and take the 5th largest value to replace the heat value of the pixel at the center of the window. The median filtering method can effectively suppress random noise and avoid the interference of single-point heat value fluctuations on anomaly detection, providing a high-quality data basis for subsequent analysis. The design of the sliding time window takes into account both short-term anomaly capture and long-term trend analysis, making the formation of the anomaly index sequence have time coherence and facilitating subsequent dynamic tracking. The binarization processing clearly calibrates the abnormal pixel points as white areas through the heat value threshold, directly providing structured input for the area anomaly index calculation in Step 102 and the temperature anomaly index in Step 103, and avoiding redundant processing of the original thermal imaging data.

[0069] Step 102: Analyze the area of the abnormal pixel points in the thermal imaging map to obtain the area anomaly index of the abnormal pixel points. The basis formula is as follows:

[0070] ;

[0071] in, For the The area anomaly index of the thermal image, Indicates the The number of abnormal pixels in a thermal image, is the total number of pixels in the thermal image, Indicates the The spatial aggregation of the pixels of a thermal image, Indicates the time difference, are coefficients, , , The search variable for the thermal image number, ;

[0072] The area anomaly index (AAI) of a thermal image specifically reflects thermal anomalies in the monitoring equipment. A comprehensive indicator, the larger the AAI, the more severe the thermal anomaly. This may increase the risk of heat accumulation, reduce equipment safety, and potentially lead to equipment failure. Therefore, this indicator is of great value in equipment monitoring and maintenance.

[0073] It is the ratio of the number of abnormal pixels to the total number of pixels. By calculating the ratio of the number of abnormal pixels to the total number of pixels, a standardized indicator can be obtained, which facilitates the comparison between different thermal images. Thermal images of different devices or different resolutions may have different total numbers of pixels. Using the ratio can eliminate these effects and make the indicators comparable.

[0074] Directly introduce spatial aggregation. Spatial aggregation is an important indicator for judging the degree of abnormal heat concentration. The unevenness of heat distribution directly affects the risk level of the equipment. Therefore, it is introduced as an independent term in the formula. Using the original spatial aggregation value can directly reflect the current status without additional transformation. This index calculates the rate of change in the number of abnormal pixels between the current moment and the previous moment. By calculating this change, we can capture the changing trends in the device's thermal state. This is crucial for promptly identifying potential equipment issues. The faster the rate of increase in abnormal pixels, the higher the risk of equipment failure, so this dynamic change needs to be reflected in the index.

[0075] Temporal rate of change of spatial aggregation Used to calculate the change rate of spatial aggregation degree between the current moment and the previous moment. In addition to the change in the number of abnormal pixels, the change in spatial aggregation degree is also important. This can help judge the severity and urgency of thermal anomalies. By observing the change in aggregation degree, it can be determined whether the device is experiencing continuous thermal anomalies, which helps formulate corresponding maintenance strategies.

[0076] The area anomaly index of the thermal image is related to multiple internal variables. The number of abnormal pixel points directly reflects the degree of thermal anomaly existing on the device surface. Each abnormal pixel represents a possible heat source or potential fault area. When the device overheats, the increase in surface temperature will cause more pixel points to show abnormal thermal states. Therefore, the larger the number, the larger the scale of the thermal anomaly, which directly affects the value of the area anomaly index. The total number of pixel points is the base number used to standardize the number of abnormal pixel points, providing a framework for relative comparison.

[0077] Spatial aggregation degree describes the distribution of abnormal pixels in the image, reflecting the concentration degree of thermal anomalies. If the abnormal pixels are highly concentrated, it usually indicates that the heat in a specific area is too high, which may cause device damage or pose safety problems. Therefore, the change in aggregation degree directly affects the area anomaly index, indicating the thermal anomaly risk of the device.

[0078] The change rate of the number of abnormal pixels reflects the change rate of the number of abnormal pixels over time, and the change rate of spatial aggregation degree reflects the change of spatial aggregation degree over time. If the thermal anomaly increases rapidly in a short period of time, it may mean that the device is encountering serious thermal problems and has a high risk. This dynamic characteristic of the change is the key to evaluating the device state.

[0079] The number of abnormal pixels is the most direct indicator to measure the thermal anomaly of the device. If there are a large number of abnormal pixels on the surface of the device, it indicates that the device may have a serious overheating problem. Therefore, the influence of this variable should be the largest, so the coefficient is the largest. Spatial aggregation degree reflects the concentration degree of abnormal heat. A dense abnormal area is more likely to cause device damage than a scattered one. Therefore, the coefficient of spatial aggregation degree is second only to the number of abnormal pixels. The time change rate reflects the change trend of abnormal conditions. Although the change speed can provide important early warning information, its direct influence is usually lower than that of the number of abnormal pixels and spatial aggregation degree.

[0080] The term of the change rate of spatial aggregation degree reflects the change of aggregation degree. Although it also has significance in the analysis of fault trends, its direct influence is the smallest compared with other factors. Ensure that the sum of all weights is 1, so that the influence of different terms can be compared on the same basis and maintain a relative proportional relationship.

[0081] The logic for obtaining the spatial aggregation degree is as follows:

[0082] Obtain the positions of each abnormal pixel in the thermal image and number them. The positions are the row and column data of the abnormal pixels in the thermal image. Obtain the adjacency relationship between any two abnormal pixels. The formula is as follows:

[0083] ;

[0084] Among them, represents the adjacency relationship value between the th and the th abnormal pixels. and respectively represent the positions of the th and the th abnormal pixels.

[0085] The adjacency relationship value reflects the adjacent relationship between two abnormal pixels. When the adjacency relationship value is 1, it means that the two abnormal pixels are adjacent and border on each other. When the adjacency relationship value is 0, it means that the two abnormal pixels do not border on each other, that is, they are not adjacent. By respectively judging the magnitude relationship between the differences in the horizontal and vertical coordinates of two abnormal pixels and 1, when the absolute values of the differences in the horizontal coordinates and the absolute values of the differences in the vertical coordinates are both less than or equal to 1, it indicates that the abnormal pixel with the coordinate of must be in the nine-square grid centered on , then it means that is adjacent to . When at least one of the absolute values of the differences in the horizontal and vertical coordinates of two abnormal pixels is greater than 1, it indicates that the abnormal pixel with the coordinate of is not in the nine-square grid centered on , then is not adjacent to .

[0086] Obtain the spatial aggregation degree. The formula is as follows: ;

[0087] Among them, is the spatial aggregation degree, represents the adjacency relationship value between the ]>th and the th abnormal pixels, is the number of abnormal pixels;

[0088] The spatial aggregation degree specifically reflects the aggregation situation of all abnormal pixel points in the thermal imaging map, and is an important indicator reflecting the concentration degree of abnormal pixel points. The larger the spatial aggregation degree, the more concentrated the abnormal pixel points in the thermal imaging map, and then the more obvious and uncontrollable the abnormal temperature state is, increasing the risk and reducing the equipment safety. Therefore, this indicator has important reference value. The molecule calculates the adjacency relationship value between each abnormal pixel point and other abnormal pixel points respectively. If there is an adjacency relationship, it is 1 and added to the molecule. If there is no adjacency relationship, it is 0 and not added to the molecule. The value of the molecule represents twice the number of abnormal pixel points with adjacency relationships. The denominator is the non-linear increase of abnormal pixel points, reflecting the non-linear influence of the number of abnormal pixel points on the spatial aggregation degree. The number of pixel points in the thermal imaging map is fixed. The increase in the number of abnormal pixel points will inevitably lead to a non-linear increase in the number of adjacency relationships. If the molecule remains unchanged, the increased abnormal pixel points do not aggregate with other abnormal pixel points, and the value of the spatial aggregation degree decreases non-linearly. Constructing the spatial aggregation degree can unify the dimensions of different thermal imaging degrees, facilitating subsequent processing.

[0089] The time difference represents the difference between the timestamps of two adjacent thermal imaging maps.

[0090] In this step, through the area anomaly index formula, integrating the number of abnormal pixels, the spatial aggregation degree and its time change rate, the thermal distribution anomaly is quantified from multiple dimensions. The spatial aggregation degree is calculated by judging the adjacency relationship between abnormal pixel points to identify whether the abnormal area is scattered noise or concentrated hot spot. The aggregated abnormal pixels may represent local overheating of the equipment, while the scattered points may be interference, enhancing the sensitivity of the algorithm to real faults. The weight allocation highlights the dominant position of the area ratio, ensuring that the algorithm responds to large-area anomalies first, and at the same time taking into account the spatial form and time change, such as small-range but rapidly spreading anomalies. This index is complementary to the temperature anomaly index in step 103. The area reflects the anomaly range, and the temperature reflects the energy intensity, jointly constituting the total anomaly index, providing multi-dimensional data support for the threshold determination and trend analysis in step 2. In addition, the introduction of the time difference enables the dynamic change of the anomaly index to be quantified, preparing for the determination of the subsequent temperature increase process.

[0091] Step 103: Perform temperature analysis on the abnormal pixel points in the thermal imaging map to obtain the temperature anomaly index of the abnormal pixel points. The formula is as follows: ;

[0092] Among them, is the temperature anomaly index of the th thermal imaging map, is the sum of the calorific values of all abnormal pixel points in the th thermal imaging map, are all coefficients, , , The search variable for the thermal image number, ;

[0093] The temperature anomaly index specifically reflects the temperature anomaly status of the current thermal image and also reflects the temperature change process. The higher the temperature anomaly status, the higher the overall temperature faced by the thermal image, and the greater the overall temperature change between two adjacent thermal images. The accelerated heat accumulation in the abnormal area of the equipment may indicate an impending serious failure. By integrating the current heat and dynamic changes, the index can more sensitively capture the potential failure trend of the equipment. Directly introduce the sum of the thermal values of all abnormal pixels in the thermal image. The overall sum of the thermal values directly reflects the temperature condition of the monitored equipment. Therefore, this formula takes the sum of the thermal values of all abnormal pixels into consideration. It is necessary to pay attention not only to the static temperature condition, but also to the change of the overall thermal value. The rapid change of the sum of the thermal values of all abnormal pixels means that the temperature change of the monitored equipment is also accelerating, indicating that the temperature of the equipment may rise sharply due to a fault, which indicates that the equipment may be about to fail. Therefore, it is necessary to reflect the difference between the sum of the thermal values of all abnormal pixels between different thermal images. When the sum of the thermal values of all abnormal pixels is large, it means that the temperature of the currently monitored equipment is large, and the temperature anomaly index is also large. When two adjacent When the difference between the sum of the thermal values of all abnormal pixels in a thermal image increases, it means that the temperature change of the monitored equipment is more drastic, the temperature anomaly of the equipment is more obvious, and the temperature anomaly index is larger. The sum of the thermal values of all abnormal pixels only represents the overall thermal value status of the thermal image at that time, and cannot reflect whether the equipment is running stably or rapidly deteriorating. Obviously, the danger of rapid deterioration of temperature conditions is more threatening than stable high temperature. Therefore, the difference between the sum of the thermal values of all abnormal pixels between the two thermal images is set to have a higher weight to reflect the significant risks brought about by the rapid deterioration of temperature conditions, and to ensure that the sum of all weights is 1. In this way, the influence of different items can be compared on the same basis and a relative proportional relationship can be maintained.

[0094] The anomaly index of each thermal image is obtained according to the following formula: ;

[0095] in, No. The abnormal index of the thermal image, For the The area anomaly index of the thermal image, For the The temperature anomaly index of the thermal image, The search variable for the thermal image number, .

[0096] The anomaly index represents the overall anomaly condition of each thermal image. Numerically, it directly reflects the anomaly state of each thermal image. The larger the value, the greater the risk of the thermal image in terms of abnormal temperature distribution or abnormal temperature values. Summing up the area anomaly index and temperature anomaly index of the thermal image reflects the anomaly state of the thermal image from two dimensions and provides a direct numerical basis for subsequent calculations.

[0097] This step focuses on capturing the energy accumulation and change trend in the abnormal area through the temperature anomaly index formula. The intention of designing a higher weight for the dynamic temperature increase term in the coefficient is to emphasize that "the rate of temperature rise" is more valuable for early warning than "the absolute heat value". For example, the temperature in a certain area rising from 50°C to 70°C may be more dangerous than remaining stable at 80°C. The construction of the anomaly index realizes the integration of the spatial range and energy intensity, enabling the anomaly index sequence in step 2 to comprehensively reflect the equipment state. The key role of this step is to organically combine the static area analysis in step 102 with the dynamic temperature analysis in this step to form a more discriminative comprehensive index. At the same time, through the anomaly index increment, it provides data reference for tracing the temperature increase process in step 2. For example, if the anomaly index rises three times in a row, it is determined as a potential fault, providing an accurate start signal for further monitoring in step 3.

[0098] Step 2: Form an anomaly index sequence from the anomaly indices of thermal images within a sliding time window and obtain the change amount sequence of the temperature increase process. Set the anomaly index threshold, and start further monitoring according to the relationship between the anomaly index threshold and the anomaly index to obtain the temperature increase process;

[0099] The said step 2 includes the following contents:

[0100] Form an anomaly index sequence of the thermal images within the time window in chronological order, and obtain the change amount of the anomaly index sequence. The formula is as follows: ;

[0101] Among them, is the change amount of the anomaly index, the anomaly index of the th thermal image, is the retrieval variable of the thermal image number, , ;

[0102] The change amount of the anomaly index reflects the change condition of each thermal image from the perspective of the overall condition. The larger the change amount of the anomaly index, the more drastic the temperature change condition of the monitored equipment and the more obvious the rapid deterioration condition. Therefore, it is necessary to identify it and conduct the next step of research and judgment.

[0103] Set the abnormal index threshold. When the abnormal index exceeds the abnormal index threshold, start further monitoring. At the same time, trace the temperature increase process. The logic for obtaining the temperature increase process is as follows:

[0104] When the abnormal index exceeds the abnormal index threshold, check the change amount of the abnormal index sequence from the back to the front, and obtain the subsequence of continuously increasing abnormal indexes that contains the last abnormal index. This subsequence is the temperature increase process;

[0105] Form a sequence of change amounts of abnormal indexes during the temperature increase process.

[0106] This step converts static detection into dynamic trend analysis through the change amount of the abnormal index, providing a time-series basis for subsequent decision-making. For the temperature increase process traceability, reverse search for the continuously rising subsequence from the last abnormal index can capture the "heating persistence". Short-term fluctuations do not trigger an alarm, but consecutive rises are determined to be a serious abnormality, avoiding interference from accidental noise. The sequence of change amounts of abnormal indexes generated in this step provides a direct input for the statistical parameter calculation in step 301, thereby dynamically determining the further monitoring time. The higher the change rate, the shorter the monitoring time. This connection ensures the coherence between step 1 and step 3, enabling the system to adaptively adjust the monitoring strategy according to the severity of the abnormality.

[0107] Step 3: Obtain the further monitoring time based on the temperature increase process. During the further monitoring time, obtain the vibration curve and voltage curve of the monitored device, and obtain the further monitoring score;

[0108] The said step 3 includes the following contents:

[0109] Step 301: Obtain the further monitoring time according to the sequence of change amounts of the temperature increase process. The formula is as follows: ;

[0110] Where, is the further monitoring time, is the average value of the sequence of change amounts of the temperature increase process, is the standard deviation of the sequence of change amounts of the temperature increase process, is the change rate of the sequence of change amounts of the temperature increase process, is the average value threshold, is the standard deviation threshold, is the change rate threshold, is the time of the temperature increase process;

[0111] The further monitoring time reflects the time that requires further focused monitoring. The longer the further monitoring time is, it indicates that a longer time is needed to conduct more detailed monitoring and analysis of the equipment, and it also shows that the current state of the equipment is not an emergency state, allowing sufficient time for detailed monitoring and analysis. Therefore, this indicator has important reference value in equipment monitoring and maintenance. The average value of the sequence of the change amount during the temperature increase process reflects the overall temperature condition during the current temperature increase process. The larger the average value is, it means that the overall abnormal state performance during the temperature increase process is more obvious, indicating that the condition of the monitored equipment deteriorates more significantly, and the further monitoring time is smaller. The larger the standard deviation of the sequence of the change amount during the temperature increase process is, it shows that the step phenomenon of the abnormal state during the temperature increase process is more obvious. From another aspect, the deterioration condition of the monitored equipment, the further monitoring time is smaller. The larger the change rate of the sequence of the change amount during the temperature increase process is, it indicates that the abnormal state during the temperature increase process deteriorates more rapidly, and the further monitoring time is smaller. Since the faster the deterioration speed is, the more urgently a more detailed judgment and analysis are needed, and the further monitoring time needs to be smaller to avoid missing the opportunity to handle the abnormal situation due to long-term monitoring. Therefore, by taking the form of a negative sign, the ratios of the average value to the average threshold, the standard deviation to the standard deviation threshold, and the change rate to the change rate threshold are respectively constructed to reflect the relationship between the changes during the temperature increase process and the condition that the equipment can withstand. Taking the average value of the three ratios and the coefficient 0.5 can prevent calculation problems caused by excessive values. Referencing the time of the temperature increase process reflects the principle based on the actual temperature increase condition. The time of the temperature increase process has occupied a part of the time, and the further monitoring time must be controlled. The time of the temperature increase process already reflects the time characteristics of the temperature increase process. The larger the time of the temperature increase process is, it indicates that the temperature increase in the early stage is relatively slow, providing a time margin for further monitoring in the later stage. Therefore, the further monitoring time is larger.

[0112] The formula for the time of the temperature increase process is as follows: ;

[0113] Wherein, represents the timestamp of the th thermal imaging diagram during the temperature increase process, represents the timestamp of the first thermal imaging diagram during the temperature increase process;

[0114] The time of the temperature increase process is the total time of the judged temperature increase process. Subtracting the time of the first thermal imaging diagram of the temperature increase process from the time of the last thermal imaging diagram gives the time of the temperature increase process.

[0115] The formula for the change rate of the abnormal index is as follows: ;

[0116] Wherein, is the change rate, represents the anomaly index of the nth thermal image during the temperature increase process, represents the anomaly index of the 1st thermal image during the temperature increase process.

[0117] The rate of change represents the speed of the temperature increase process. The faster the temperature increases, the more urgent the state of the monitored device is. Therefore, the rate of change needs to be considered when obtaining the further monitoring time.

[0118] In this step, the further monitoring time is calculated through a dynamic formula to achieve the matching of the monitoring duration and the anomaly risk. For example: if the change amount during the temperature increase process fluctuates greatly, the further monitoring time is shortened to avoid missing the valuable processing time for the abnormal condition of the device in order to monitor more data; if the rate of change is extremely high, it is quickly shortened to respond quickly. The time alignment design ensures the synchronization of thermal, mechanical, and electrical data, laying a foundation for multi-source fusion. The weight coefficient and denominator in the formula normalize the three parameters, balancing the influence of different statistics on the monitoring time and preventing a single parameter from dominating the decision-making. The core value of this step is to convert the time-series data in step 2 into an operable monitoring strategy, which not only avoids over-monitoring and missing the opportunity to handle abnormal conditions but also ensures full coverage of high-risk events.

[0119] Step 302: During the further monitoring process, obtain the vibration curve and voltage curve of the device. The vibration curve is obtained through a vibration sensor, and the voltage curve is obtained through an oscilloscope. The horizontal axis of the vibration curve and voltage curve is time, the starting moment is the moment when the further monitoring is started, the time length is the further monitoring time, the vertical axis of the vibration curve is the amplitude, and the vertical axis of the voltage curve is the voltage. Obtain the further monitoring score:

[0120] ;

[0121] : where, is the further monitoring score, is the initial score value, is the vibration curve, is the initial amplitude, is the voltage curve, is the initial voltage, is the further monitoring time, and are coefficients, ;

[0122] The further monitoring score specifically reflects the device status of the monitored device during the further monitoring process. It is a comprehensive indicator. The larger the further monitoring score, the farther the device status in the monitoring process deviates from the initial state, which means the more serious the abnormal state and the greater the potential fault threat. Therefore, this indicator has important reference value in device monitoring and maintenance summary.

[0123] It reflects the degree to which the vibration state of the monitored device deviates from the initial vibration state during the further monitoring process. It converts the two-dimensional data of the vibration curve into a one-dimensional value reflecting the abnormal state, which is convenient for participating in calculations and has an intuitive manifestation. It reflects the degree to which the voltage state of the monitored device deviates from the initial voltage state during the further monitoring process. It converts the two-dimensional data of the voltage curve into a one-dimensional value reflecting the abnormal state, which is convenient for participating in calculations and has an intuitive manifestation. The square term enhances the manifestation of the deviation of vibration and voltage from the initial state during the further monitoring process. Integrate the deviation degrees of vibration and voltage during the further monitoring time to present the deviation degree from an overall perspective and form a specific value for the next calculation. When the vibration and voltage during the further monitoring period deviate from the initial vibration and voltage states, it indicates that the device has potential faults in mechanical and electrical aspects respectively. An excessive deviation degree means an increased fault risk. Using the initial value as a parameter term increases the value range of the further monitoring score, which is convenient for subsequent numerical processing and facilitates judging the degree of deviation of the further monitoring process from the initial state. Since the hard damage caused by vibration changes is difficult to recover and more dangerous, and voltage changes within a certain range will not cause significant damage, it reflects the importance of preventing mechanical damage.

[0124] The initial value of the score is a preset value. The initial amplitude is the amplitude of the device during normal operation, and the initial voltage is the voltage of the device during normal operation.

[0125] In this step, through the synchronous acquisition of vibration and voltage curves, vibration reflects the mechanical structure stability and voltage reflects the power supply state, constructing an all-round state of the device operation status. The integral form can quantify the cumulative effect of the deviation of vibration and voltage from the normal values. The magnitude relationship between the coefficients reflects that mechanical faults are usually more destructive than voltage fluctuations. For example, bearing wear may directly lead to shutdown, while voltage sags can be mitigated by redundant power supplies. The calculation of the further monitoring score not only quantifies the real-time abnormal degree but also standardizes the score through step 401, which is convenient for comparison with historical data. The key role of this step is to expand the single thermal anomaly detection to multi-physical field joint analysis, providing data input in the vibration and voltage dimensions for the further monitoring index in step 401, enhancing the comprehensiveness and reliability of fault determination.

[0126] Step 4: Continuously obtain the thermal imaging map of the monitored device within the further monitoring period, and obtain the change amount of the anomaly index during the further monitoring process. Form a further monitoring index based on the further monitoring score, the change amount of the anomaly index during the further monitoring process, and the change amount of the anomaly index during the temperature increase process. Set threshold I and threshold II respectively, and determine whether the device is abnormal according to the relationship between the further monitoring index and threshold I and threshold II.

[0127] The said Step 4 includes the following content:

[0128] Step 401: During the further monitoring process, continuously obtain the thermal imaging map of the monitored device and form the change amount of the anomaly index during the further monitoring process, and generate a further monitoring index. The formula is as follows:

[0129] ;

[0130] Where, is the further monitoring index, is the th change amount of the anomaly index during the further monitoring process, is the number of change amounts of the anomaly index during the further monitoring process, is the th change amount of the anomaly index in the change amount sequence during the temperature increase process, is the number of change amounts of the anomaly index in the change amount sequence during the temperature increase process, is the further monitoring score, is the initial value of the score.

[0131] The further monitoring index reflects the change situation of the anomaly index in the further monitoring process compared with the temperature increase process. By the change situation of the anomaly index, it is judged whether the device continues to deteriorate continuously at the speed of the temperature increase process after entering the further monitoring process. Introduce the ratio of the further monitoring score to the initial score to highlight the deviation degree of the abnormal state in the further monitoring process compared with the initial state. Directly judge the deviation degree of the device in the further monitoring process compared with the initial state and the change state compared with the temperature increase process through the further monitoring index. The larger the further monitoring index, the more serious the abnormal state of the device is, especially the abnormal state in the further monitoring process. Therefore, this index has important reference value in device monitoring and maintenance.

[0132] represents the mean value of the change rate of the change amount of the anomaly index during the further monitoring process, reflecting the speed of the increase of the anomaly index during the further monitoring process. The faster the mean value of the change rate is, the faster the anomaly index increases, the faster the abnormal state of the device deteriorates, and the higher the further monitoring index is. It represents the mean value of the change rate of the change amount of the anomaly index during the temperature increase process, reflecting the speed at which the anomaly index increases during the temperature increase process. When the change rate of the change amount of the anomaly index during the temperature increase process is greater than the change rate of the change amount of the anomaly index during the further monitoring process, it indicates that the deterioration speed of the equipment state slows down during the further monitoring process, and the further monitoring index will decrease. The ratio of the further monitoring score to the initial score reflects the degree to which the abnormal state deviates from the initial state during the further monitoring process. The higher the ratio of the further monitoring score to the initial score, the higher the degree to which the equipment state deviates from the initial state during the further monitoring process, and the higher the further monitoring index. Multiply the ratio of the mean values of the change rates of the two processes directly by the ratio of the two scores to intuitively show the deterioration degree of the abnormal state of the equipment.

[0133] This step realizes the deep fusion of multi-source data through the further monitoring index formula. The numerator is the mean value of the adjacent change rates during the monitoring process, reflecting the dynamic acceleration trend of real-time anomalies. If the anomaly index increment of each thermal imaging diagram during the monitoring period is greater than the anomaly index increment of the previous thermal imaging diagram, it indicates that the deterioration speed accelerates; at the same time, by comparing with the change rate of the anomaly index during the temperature increase process, the change rate of the equipment operation status after the further monitoring link is judged. Scoring correction factor Incorporating the influence of vibration / voltage anomalies into the index can break the limitation of single thermal data - for example: even if the thermal anomaly change rates are similar, if the further monitoring score increases significantly, the further monitoring index will increase synchronously, triggering a higher-level alarm. This index, as the only input for the threshold determination in step 402, realizes the unified quantification of thermal, mechanical, and electrical data, ensuring the simplicity and objectivity of the decision-making logic.

[0134] Step 402: Set the threshold Ⅰ and threshold Ⅱ of the further monitoring index respectively, and make a judgment according to the further monitoring index. The logic is as follows:

[0135] When the further monitoring index is less than or equal to the threshold Ⅰ, the equipment continues to work in the current state;

[0136] When the further monitoring index is greater than the threshold Ⅰ and less than the threshold Ⅱ, the equipment starts the heat dissipation system and continues to work;

[0137] When the further monitoring index is greater than or equal to the threshold Ⅱ, the equipment starts the heat dissipation system and alarms, and the maintenance personnel intervene in the management.

[0138] In this step, through hierarchical threshold determination, complex multi-source data metrics are converted into clear operation and maintenance instructions. The intention of setting Threshold Ⅰ is to allow the device to continue operating within a controllable range, such as short-term temperature fluctuations, while triggering the cooling system to inhibit the development of abnormalities; the setting of Threshold Ⅱ targets irreversible risks, such as thermal runaway accompanied by severe vibrations, and requires immediate alarm and manual intervention. The key to this logic lies in the dynamic response. This step and the further monitoring index calculation in Step 401 form a closed-loop control, ultimately achieving the self-adaptability and hierarchical processing efficiency of device health management, avoiding production losses caused by excessive downtime and ensuring the safety of the device to the greatest extent.

[0139] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0140] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0141] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A data fusion method based on multi-source sensors, characterized in that The specific steps include: Obtain the thermal imaging map of the monitored device at a fixed frequency, and obtain the calorific value matrix of each thermal imaging map within the sliding time window. Set the calorific value threshold and obtain the abnormal pixel points in the calorific value matrix. Obtain the area abnormality index and the temperature abnormality index respectively according to the abnormal pixel points, and form the abnormality index of each thermal imaging map. Form the abnormality index sequence of the thermal imaging maps within the sliding time window and obtain the sequence of the change amount of the temperature increase process. Set the abnormality index threshold, and start further monitoring according to the relationship between the abnormality index threshold and the abnormality index, and obtain the temperature increase process. Obtain the further monitoring time according to the temperature increase process. Obtain the vibration curve and voltage curve of the monitored device within the further monitoring time, and obtain the further monitoring score. Continuously obtain the thermal imaging map of the monitored device within the further monitoring time, and obtain the change amount of the abnormality index during the further monitoring process. Form the further monitoring index according to the further monitoring score, the change amount of the abnormality index during the further monitoring process, and the change amount of the abnormality index during the temperature increase process. Set threshold Ⅰ and threshold Ⅱ respectively, and judge whether the device is abnormal according to the relationship between the further monitoring index and threshold Ⅰ and threshold Ⅱ.

2. The data fusion method based on multi-source sensors according to claim 1, wherein: Obtain the thermal imaging map of the monitored device at a fixed frequency. The thermal imaging is obtained by a thermal imager. Add a time stamp to each obtained thermal imaging map, and process the thermal imaging map by the median filtering method. Set the sliding window time to 1 hour, and obtain the calorific value matrix of each thermal imaging map within the sliding window time according to the pixel points in the thermal imaging map. The formula is as follows: ； Among them, is the heat value matrix of the th thermal image, is the heat value of the pixel at the th row and the th column, , is the total number of thermal images, , is the number of rows of pixels in the thermal image, is the number of columns of pixels in the thermal image; Set the calorific value threshold, calibrate the pixel points whose calorific value exceeds the calorific value threshold, and obtain the abnormal pixel points. The logic is as follows: Perform binary processing on the thermal imaging map to obtain the binary mask of each thermal imaging map. The pixel points whose calorific value does not exceed the calorific value threshold turn black, and the pixel points whose calorific value exceeds the calorific value threshold turn white. The white pixel points are the abnormal pixel points.

3. The data fusion method based on multi-source sensors according to claim 2, wherein: Perform area analysis on the abnormal pixel points in the thermal imaging map to obtain the area abnormality index of the abnormal pixel points. The formula is as follows: ； Among them, is the area anomaly index of the th thermal imaging map, represents the number of abnormal pixel points in the th thermal imaging map, is the total number of pixel points of the thermal imaging map, represents the spatial aggregation degree of the pixel points of the th thermal imaging map, represents the time difference, are all coefficients, , is the retrieval variable of the thermal imaging map number, ; The logic for obtaining the spatial aggregation degree is as follows: Obtain the positions of each abnormal pixel point in the thermal imaging map and number them respectively. The position is the row and column data of the abnormal pixel point in the thermal imaging map. Obtain the adjacency relationship between any two abnormal pixel points. The formula is as follows: ； Among them, represents the th adjacency relationship value between the th and the and respectively represent the positions of the th and the th abnormal pixel points; To obtain the spatial aggregation degree, the formula is as follows: ; Among them, is the spatial aggregation degree, represents the th adjacency relationship value between the th and the th abnormal pixel points; is the number of abnormal pixel points; The time difference represents the time stamp difference between two adjacent thermal imaging maps.

4. The data fusion method based on multi-source sensors according to claim 3, characterized in that: Perform temperature analysis on the abnormal pixel points in the thermal imaging map to obtain the temperature abnormality index of the abnormal pixel points. The formula is as follows: ； Among them, is the temperature anomaly index of the th thermal imaging map, is the sum of the calorific values of all abnormal pixel points of the th thermal imaging map, are all coefficients, , is the retrieval variable of the thermal imaging map number, , ; Obtain the anomaly index of each thermal image according to the following formula: ; Among them, The abnormality index of the nth thermal image is the area abnormality index of the nth thermal image, and is the retrieval variable of the thermal image number, .

5. The data fusion method based on multi-source sensors according to claim 4, wherein: Form the abnormality index sequence of the thermal imaging maps within the time window in chronological order, and obtain the change amount of the abnormality index sequence. The formula is as follows: ； Among them, is the change amount of the anomaly index, The anomaly index of the first thermal image, is the retrieval variable of the thermal image number, ； Set the abnormality index threshold. When the abnormality index exceeds the abnormality index threshold, start further monitoring, and at the same time trace back the temperature increase process. The logic for obtaining the temperature increase process is: When the abnormality index exceeds the abnormality index threshold, check the change amount of the abnormality index sequence from the back to the front, and obtain the subsequence in which the abnormality index continuously increases and contains the last abnormality index. The subsequence is the temperature increase process. Form a sequence of change amounts during the temperature increase process with the abnormal change amount of the temperature during the temperature increase process.

6. The data fusion method based on multi-source sensors according to claim 5, characterized in that: Obtain the further monitoring time according to the sequence of change amounts during the temperature increase process, and the basis formula is as follows: ； Among them, To further monitor time, is the average value of the sequence of variation amounts during the temperature increase process, is the standard deviation of the sequence of variation amounts during the temperature increase process, is the change rate of the sequence of variation amounts during the temperature increase process, is the average value threshold, is the standard deviation threshold, is the change rate threshold, is the time of the temperature increase process; The time for the temperature increase process is based on the following formula: ; Among them, represents the timestamp of the th thermal image during the temperature increase process, represents the timestamp of the 1st thermal image during the temperature increase process; The formula based on the abnormal index change rate is as follows: ; Among them, is the rate of change, represents the anomaly index of the th thermal image during the temperature increase process, represents the anomaly index of the 1st thermal image during the temperature increase process.

7. The data fusion method based on multi-source sensors according to claim 6, characterized in that: During the further monitoring process, obtain the vibration curve and voltage curve of the device. The vibration curve is obtained by a vibration sensor, and the voltage curve is obtained by an oscilloscope. The horizontal axes of the vibration curve and voltage curve are time, the start time is the moment when the further monitoring is started, the time length is the further monitoring time, the vertical axis of the vibration curve is the amplitude, and the vertical axis of the voltage curve is the voltage. Obtain the further monitoring score: ； Among them, For further monitoring of the score, is the initial value of the score, is the vibration curve, is the initial amplitude, is the voltage curve, is the initial voltage, is the further monitoring time, and are coefficients, ; The initial value of the score is a preset value, the initial amplitude is the amplitude of the device during normal operation, and the initial voltage is the voltage of the device during normal operation.

8. The data fusion method based on multi-source sensors according to claim 7, characterized in that: During the further monitoring process, continuously obtain the thermal imaging map of the monitored device and form the change amount of the abnormal index during the further monitoring process, and generate a further monitoring index. The basis formula is as follows: ; Among them, For further monitoring the index, For the th abnormal index change amount during the further monitoring process, For the number of abnormal index change amounts during the further monitoring process, For the th abnormal index change amount in the sequence of temperature increase process change amounts, For the number of abnormal index change amounts in the sequence of temperature increase process change amounts, For the further monitoring score, For the initial value of the score.

9. The data fusion method based on multi-source sensors according to claim 8, wherein: Respectively set the threshold I and threshold II of the further monitoring index, and make a judgment according to the further monitoring index. The logic is as follows: When the further monitoring index is less than or equal to the threshold I, the device continues to work in the current state; When the further monitoring index is greater than the threshold I and less than the threshold II, the device starts the heat dissipation system and continues to work; When the further monitoring index is greater than or equal to the threshold II, the device starts the heat dissipation system and alarms, and the maintenance personnel intervene in the management.

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