Sensor-based UAV Temperature Measurement Method and System

Abstract

The present invention relates to the technical field of unmanned aerial vehicle temperature measurement, and specifically, to a method and system for unmanned aerial vehicle temperature measurement based on sensors. It includes monitoring the temperature measurement process of the unmanned aerial vehicle, obtaining the influencing associated items and associated values corresponding to different temperature data to be verified; calculating the influence weights of each influencing associated item in the lowest numerical deviation interval and the highest numerical deviation interval, and ranking the influencing associated items according to the influence weights; formulating an avoidance route according to the ranking. By dividing the influence weights of the influencing associated items in the temperature measurement process and formulating an avoidance route according to the division order, the present invention realizes the sequential avoidance of the influencing associated items, avoids the repeated influence of the influencing associated items caused by disorderly avoidance, reduces the temperature measurement efficiency, increases the degree of damage suffered by the unmanned aerial vehicle during the temperature measurement process, and affects its service life.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV temperature measurement, and specifically, to a method and system for UAV temperature measurement based on sensors. Background Art

[0002] UAV temperature measurement is based on the characteristic that the platinum thermal resistance changes with temperature. There is a linear relationship between the platinum thermal resistance and temperature, that is, as the temperature increases, the resistance value of platinum will also increase accordingly. This relationship is called the temperature coefficient of the platinum thermal resistance, and the temperature data of the monitored area is obtained through the change value of the resistance.

[0003] During the non-contact temperature measurement process by UAV, since the area to be measured is generally an area that cannot be directly reached by humans and has certain risks, for example, monitoring the high-temperature gas around a kiln, at this time, the UAV needs to fly to a specific area for temperature monitoring.

[0004] Since the UAV temperature measurement process will be affected by different external factors, such as distance, sensor angle, and mechanical stress, etc., all of which will affect the final temperature monitoring result. If these external environmental influences cannot be reasonably avoided, it will lead to errors in the final monitoring result.

[0005] The traditional processing method is to control the UAV to avoid in real time. For example, when it is found that the entire UAV is affected by mechanical jitter due to the UAV entering a heat flow area, at this time, it is necessary to control the UAV to leave the current area, and then control the UAV to the next monitoring point. However, in actual situations, different monitoring points may be affected by different external factors. If only a single external factor is adjusted in real time and the monitoring path cannot be reasonably planned, the monitoring efficiency will be greatly reduced.

[0006] In order to solve the above problems, there is an urgent need for a UAV temperature measurement method that can plan the UAV monitoring path according to the influence trends of different external factors. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for UAV temperature measurement based on sensors to solve the problems raised in the above background art.

[0008] To achieve the above purpose, one of the objects of the present invention is to provide a method for UAV temperature measurement based on sensors, including the following steps:

[0009] S1. Collect the monitored temperature data of the UAV at different locations in the same area, and mark it as the temperature data to be verified;

[0010] S2. Monitor the UAV temperature measurement process, and obtain the influence correlation items and correlation values corresponding to different temperature data to be verified;

[0011] S3. Cluster the temperature data to be verified, divide them into multiple temperature ranges, and mark them as the ranges to be verified;

[0012] S4. Obtain the temperature values in the actual monitoring area, compare them with the ranges to be verified, and divide the ranges with the lowest and highest value offsets;

[0013] S5. Combine with the industry standards for UAV temperature measurement to obtain the conventional values of various influencing related items;

[0014] S6. Collect the influencing related items and related values in the ranges with the lowest and highest value offsets, combine with the conventional values of various influencing related items, calculate the influence weights of the influencing related items in the ranges with the lowest and highest value offsets, and rank the influencing related items according to the influence weights;

[0015] S7. Judge the influence trends of the same influencing related items in the ranges with the lowest and highest value offsets;

[0016] When the same influencing related items are positively correlated in the ranges with the lowest and highest value offsets, maintain the current ranking;

[0017] When the same influencing related items are not positively correlated in the ranges with the lowest and highest value offsets, move down one ranking;

[0018] S8. Obtain the ranking of each influencing related item according to the judgment result, and formulate an avoidance route according to the ranking.

[0019] As a further improvement of this technical solution, the different locations in the same area in S1 are different measurement points in the area with the same actual temperature.

[0020] As a further improvement of this technical solution, the influencing related items in S2 include the temperature measurement distance of the sensor, the temperature measurement height, the mechanical stress, and the temperature measurement angle of the sensor;

[0021] Among them, the temperature measurement distance of the sensor is the distance between the sensor induction area and the heat source area;

[0022] The temperature measurement height is the height of the UAV from the ground;

[0023] The mechanical stress is the vibration frequency of the UAV;

[0024] The temperature measurement angle of the sensor is the angle formed between the sensor and the UAV hovering parallel to the ground during monitoring.

[0025] As a further improvement of this technical solution, the method for clustering the temperature data to be verified in S3 includes the following steps:

[0026] S3.1. Integrate the temperature data to be verified into a data set, and randomly select multiple temperature data to be verified in the current data set as the clustering centers of the first clustering data set;

[0027] S3.2. Calculate the distances between each temperature data to be verified and each clustering center, and assign the current temperature data to be verified to the first clustering data set with the closest distance;

[0028] S3.3. Calculate the mean values of the temperature data to be verified in the new data set as the clustering centers of the new round of clustering data set;

[0029] S3.4. Continue to calculate the distances between each temperature data to be verified and each clustering center, and perform a new round of partitioning of the temperature data to be verified;

[0030] S3.5. Until the temperature data to be verified in each data set does not change, otherwise repeat steps S3.3 and S3.4.

[0031] As a further improvement of this technical solution, the method for partitioning multiple temperature ranges in S3 includes the following steps:

[0032] S3.6. Sort the temperature data to be verified in each temperature range in ascending order;

[0033] S3.7. Obtain the maximum and minimum values in the temperature range as the range values of the current temperature range to be verified.

[0034] As a further improvement of this technical solution, the method for partitioning the lowest numerical deviation range and the highest numerical deviation range in S4 includes the following steps:

[0035] S4.1. Sort the range values of each interval in ascending order;

[0036] S4.2. Compare the temperature values in the actual monitoring area with the range values, and obtain the maximum difference and the minimum difference;

[0037] S4.3. Select the range value where the maximum difference is located as the highest numerical deviation range;

[0038] S4.4. Select the range value where the minimum difference is located as the lowest numerical deviation range.

[0039] As a further improvement of this technical solution, the value for comparing the temperature value in the actual monitoring area with the range value in S4.2 is the maximum or minimum value that makes up the range value.

[0040] As a further improvement of this technical solution, the method for ranking the impact-related items according to the impact weight in S6 includes the following steps:

[0041] S6.1. Obtain the associated values of each impact-related item corresponding to the lowest numerical deviation range and the highest numerical deviation range;

[0042] S6.2. Calculate the difference between each associated value and the corresponding normal value, and mark it as the weight impact difference;

[0043] S6.3. Define the unit difference and the corresponding unit weight, and calculate the final impact weight = weight impact difference / unit difference × unit weight;

[0044] S6.4. Obtain the final impact weights of each impact-related item in the lowest numerical deviation range and the highest numerical deviation range, and compare the impact weights;

[0045] S6.5. Take the impact-related item with the largest impact weight value as the direct impact-related item of the current highest numerical deviation range;

[0046] S6.6. Take the impact-related item with the smallest impact weight value as the direct impact-related item of the current lowest numerical deviation range, and sort the remaining impact-related items according to the impact weight.

[0047] Another object of the present invention is to provide a system for implementing a method for measuring the temperature of an unmanned aerial vehicle based on a sensor, including a data processing module and an avoidance route planning module. The data processing module cooperates with the sensor to obtain the temperature data of the current temperature measurement area;

[0048] Among them, the data processing module includes a data clustering processing unit, a data interval division unit, a project weight calculation unit, and an impact trend judgment unit;

[0049] The data clustering processing unit is used to perform clustering processing on each item of temperature data to be verified;

[0050] The data interval division unit is used to divide multiple temperature intervals, mark them as intervals to be verified, and obtain the temperature values of the actual monitoring area, compare them with the intervals to be verified, and divide the lowest numerical deviation range and the highest numerical deviation range;

[0051] The project weight calculation unit is used to collect each impact-related item and the associated value in the lowest numerical deviation range and the highest numerical deviation range, combine the normal values of each impact-related item, calculate the impact weights of each impact-related item in the lowest numerical deviation range and the highest numerical deviation range, and rank the impact-related items according to the impact weight;

[0052] The influence trend judgment unit is used to judge the influence trends of the same influence-related items in the lowest numerical deviation range and the highest numerical deviation range;

[0053] When the same influence-related item is positively correlated in the lowest numerical deviation range and the highest numerical deviation range, the current ranking order is maintained;

[0054] When the same influence-related item is not positively correlated in the lowest numerical deviation range and the highest numerical deviation range, it is moved down one ranking;

[0055] The influence trend judgment unit obtains the ranking order of each influence-related item according to the judgment result, and formulates an avoidance route according to the ranking order.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] In the sensor-based UAV temperature measurement method and system, by dividing the influence weights of the influence-related items in the temperature measurement process and formulating an avoidance route according to the division order, the sequential avoidance of the influence-related items is realized, avoiding the repeated influence of the influence-related items caused by disordered avoidance, resulting in a reduction in temperature measurement efficiency, making the damage degree suffered by the UAV during temperature measurement higher and affecting its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is the overall method flowchart of the present invention;

[0059] Figure 2 is the method flowchart for clustering each temperature data to be verified in the present invention;

[0060] Figure 3 is the method flowchart for dividing multiple temperature ranges in the present invention;

[0061] Figure 4 is the method flowchart for dividing the lowest numerical deviation range and the highest numerical deviation range in the present invention;

[0062] Figure 5 is the method flowchart for ranking the influence-related items according to the influence weights in the present invention;

[0063] Figure 6 is the overall system structure block diagram of the present invention;

[0064] Figure 7 is the schematic diagram of a common Howland constant current source circuit of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0066] Please refer to Figure 1 As shown, one of the purposes of the present invention is to provide an unmanned aerial vehicle temperature measurement method based on sensors, including the following steps:

[0067] S1. Collect the monitored temperature data of the unmanned aerial vehicle at different locations in the same area and mark it as the temperature data to be verified;

[0068] S2. Monitor the temperature measurement process of the unmanned aerial vehicle and obtain the influencing associated items and associated values corresponding to different temperature data to be verified;

[0069] S3. Perform clustering processing on each item of temperature data to be verified, divide multiple temperature ranges, and mark them as the ranges to be verified;

[0070] S4. Obtain the temperature value of the actual monitored area, compare it with the ranges to be verified, and divide the range with the lowest numerical deviation and the range with the highest numerical deviation;

[0071] S5. Combine the industry standards for unmanned aerial vehicle temperature measurement to obtain the conventional values of each influencing associated item;

[0072] S6. Collect the influencing associated items and associated values in the range with the lowest numerical deviation and the range with the highest numerical deviation, combine the conventional values of each influencing associated item, calculate the influence weights of each influencing associated item in the range with the lowest numerical deviation and the range with the highest numerical deviation, and perform a ranking of the influencing associated items according to the influence weights;

[0073] S7. Judge the influence trends of the same influencing associated items in the range with the lowest numerical deviation and the range with the highest numerical deviation;

[0074] When the same influencing associated item is positively correlated in the range with the lowest numerical deviation and the range with the highest numerical deviation, maintain the current ranking;

[0075] When the same influencing associated item is not positively correlated in the range with the lowest numerical deviation and the range with the highest numerical deviation, move down one ranking;

[0076] S8. Obtain the ranking of each influencing associated item according to the judgment result and formulate an avoidance route according to the ranking.

[0077] In specific use, the present invention provides a UAV temperature measurement method that can plan the monitoring path of the UAV according to the influence trends of different external factors. The specific content is as follows:

[0078] Before specific temperature measurement, in order to obtain the influence of various external factors on the UAV during the temperature measurement process, it is necessary to determine through specific temperature values. Therefore, it is first necessary to collect the monitoring temperature data of the UAV at different locations in the same area. Different locations in the same area in the present invention are defined as areas with the same actual temperature, that is, the change amount during the UAV temperature measurement at this time is not the actual temperature. The finally measured temperature difference is caused by other related external factors. That is, the actual temperature in the same area is consistent, but the external factors affecting the UAV at different locations are different, which is marked as the temperature data to be verified. That is, the current temperature data has not been verified and may be greatly affected by external factors. Therefore, the corresponding temperature data is very likely to be different from the actual value;

[0079] After completing the temperature collection work, it is necessary to obtain the external factors that affect the temperature measurement in the monitoring area. The present invention collectively refers to the external factors as influence-related items, and different influence-related items correspond to specific correlation values. The influence-related items involved in the present invention include the sensor temperature measurement distance, the temperature measurement height, the mechanical stress, and the sensor temperature measurement angle;

[0080] Among them, the sensor temperature measurement distance is the distance between the sensor sensing area and the heat source area;

[0081] The temperature measurement height is the height of the UAV from the ground;

[0082] The mechanical stress is the UAV jitter frequency;

[0083] The sensor temperature measurement angle is the angle formed between the sensor during monitoring and the UAV hovering parallel to the ground;

[0084] After completing the determination work of each influence-related item, in order to obtain the relationship between the measured temperature and the influence-related items, it is necessary to perform clustering processing on each temperature data to be verified, as Figure 2 shown. The specific clustering method is as follows:

[0085] Integrate the temperature data to be verified into a data set. Randomly select multiple temperature data to be verified in the current data set as the clustering centers of the first clustering data set. Calculate the distances between each temperature data to be verified and each clustering center, that is, the temperature difference between the two. The lower the difference, the closer the distance; conversely, the farther the distance. Assign the current temperature data to be verified to the first clustering data set with the closest distance. Calculate the mean value of each temperature data to be verified in the new data set as the clustering center of the second clustering data set. Continue to calculate the distances between each temperature data to be verified and each clustering center, and perform the second division of the temperature data to be verified until the temperature data to be verified in each data set does not change, that is, the closest to the clustering center in the current data set. At this time, different data sets store temperature data in different data ranges, and divide the temperature intervals according to the data ranges in the data sets, and mark them as intervals to be verified. For example Figure 3 as shown below, the specific division steps are as follows:

[0086] First, since the temperature data to be verified in each data set is distributed according to the clustering center, the numerical sizes are different. It is necessary to perform sorting according to the different numerical sizes and obtain the maximum and minimum values among them as the interval range values of the current interval to be verified. For example, in a certain data set {300; 305; 306; 296; 298; 308; 307; 295; 299}, the corresponding interval range value is [295, 308] at this time.

[0087] After determining the interval range values, since there are differences between the interval range values and the actually measured temperature values, in order to obtain the relationship between the temperature values and the influencing associated items, it is necessary to obtain the temperature values of the actual monitoring area. For example, through the direct contact measurement method at multiple points, obtain the average value as the temperature value of the actual monitoring area, and compare the temperature value of the actual monitoring area with the previously obtained interval range values, and divide the interval with the lowest numerical offset and the interval with the highest numerical offset. As Figure 4 shown below, the specific content is as follows:

[0088] First, sort each interval range value. For example, the divided interval range values are respectively and , where the temperature value measured in the actual monitoring area is . Sort the interval range values in terms of size. Among them , and compare it with the temperature value of the actual monitoring area, that is, calculate and and to calculate the differences, obtain the maximum difference and the minimum difference, and select the interval range value where the maximum difference is located as the interval with the highest numerical offset, and select the interval range value where the minimum difference is located as the interval with the lowest numerical offset. For example The difference with is the largest, and the difference with is the smallest. Then the highest range of numerical deviation is , and the lowest range of numerical deviation is .

[0089] Since the acquisition of range values in different ranges is affected by multiple associated influencing items, but the weights of the main influences are different. In order to obtain the influence weights of each associated influencing item and formulate subsequent avoidance routes based on the influence weights, it is necessary to combine the industry standards for UAV temperature measurement to obtain the normal values of each associated influencing item, that is, the temperature measurement values will not be affected under normal value conditions, and calculate the influence weights of each associated influencing item in the lowest range and the highest range of numerical deviation based on the normal values. As Figure 5 shown, the specific steps are as follows:

[0090] First, obtain the associated values of each associated influencing item corresponding to the lowest range and the highest range of numerical deviation, calculate the difference between each associated value and the corresponding normal value, mark it as the weight influence difference, and formulate the unit difference and the corresponding unit weight. Calculate the final influence weight = weight influence difference / unit difference × unit weight, obtain the final influence weights of each associated influencing item in the lowest range and the highest range of numerical deviation, and conduct an influence weight comparison. The associated influencing item with the largest influence weight value is used as the direct associated influencing item for the current highest range of numerical deviation, and the associated influencing item with the smallest influence weight value is used as the direct associated influencing item for the current lowest range of numerical deviation. The remaining associated influencing items are sorted according to the influence weights.

[0091] In order to further accurately determine the relationship between each associated influencing item and the temperature value, multiple ranges need to be verified. At this time, judge the influence trend of the same associated influencing item in the lowest range and the highest range of numerical deviation, that is, if the direct associated influencing item with the lowest influence weight in the current lowest range of numerical deviation has the highest influence weight in the highest range of numerical deviation, it indicates that the current direct associated influencing item is positively correlated in the lowest range and the highest range of numerical deviation. At this time, the ranking of the current direct associated influencing item remains unchanged, and continue to judge the influence trend of the next associated influencing item. If the influence trend is positively correlated, that is, the ranking remains unchanged, otherwise it is not positively correlated, then it drops one ranking. It should be noted that when the current ranking and the next associated influencing item are not positively correlated, the current ranking of the associated influencing item does not change the ranking.

[0092] After completing the ranking determination work for each influencing related item, according to the ranking of each influencing related item, formulate a ranking sorting to develop an avoidance route. For example, based on the ranking determination result, the influencing weight ranking of the sensor temperature measurement distance, temperature measurement height, mechanical stress, and sensor temperature measurement angle is mechanical stress, sensor temperature measurement distance, temperature measurement height, and sensor temperature measurement angle. Then, the influencing related item to be avoided first is mechanical stress. That is, when adjusting the UAV flight route, it is necessary to avoid areas with high mechanical stress, and avoid the influencing related item of the next ranking within the controllable range of mechanical stress until each influencing related item is within the controllable range, so as to achieve the sequential avoidance of influencing related items, avoid repeated influence of influencing related items caused by unordered avoidance, resulting in a decrease in temperature measurement efficiency, an increase in the damage degree suffered by the UAV during temperature measurement, and an impact on its service life.

[0093] The second object of the present invention is to provide a system for implementing a UAV temperature measurement method based on sensors, including a data processing module and an avoidance route planning module. The data processing module cooperates with the sensor to obtain temperature data of the current temperature measurement area.

[0094] Among them, the data processing module includes a data clustering processing unit, a data interval division unit, a project weight calculation unit, and an influence trend judgment unit.

[0095] The data clustering processing unit is used to perform clustering processing on each item of temperature data to be verified.

[0096] The data interval division unit is used to divide multiple temperature intervals, mark them as intervals to be verified, and obtain the temperature values of the actual monitoring area, compare them with the intervals to be verified, and divide the interval with the lowest numerical deviation and the interval with the highest numerical deviation.

[0097] The project weight calculation unit is used to collect each influencing related item and related value in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, combine the conventional values of each influencing related item, calculate the influence weight of each influencing related item in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, and perform ranking sorting on the influencing related items according to the influence weight.

[0098] The influence trend judgment unit is used to judge the influence trend of the same influencing related item in the interval with the lowest numerical deviation and the interval with the highest numerical deviation.

[0099] When the same influencing related item is positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking sorting is maintained.

[0100] When the same influencing related item is not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, it is moved down one ranking.

[0101] The influence trend judgment unit obtains the ranking of each influence-related item according to the judgment result, and formulates an avoidance route according to the ranking.

[0102] It should be noted that during the temperature monitoring in cooperation with the sensor, the existing processing method is as Figure 7 shown. A common Howland constant current source circuit is adopted, which is composed of R1, R2, R3, R4 and an operational amplifier. The output current changes with the change of the input voltage and does not change with the change of the load, achieving the purpose of constant current.

[0103] Although this circuit is simple and has good constant current accuracy, it also has the following disadvantages:

[0104] 1. It has extremely high requirements for the accuracy of resistor matching to obtain a high output impedance;

[0105] 2. The input source impedance will increase the R1 resistor, so its value must be very low to minimize the matching error;

[0106] 3. The power supply voltage must be much higher than the maximum output voltage;

[0107] 4. The CMRR (common mode rejection ratio) performance of the operational amplifier must be relatively good;

[0108] According to the experimental results, for every 0.02% increase in the error of the four resistors R1~R4, the CMRR (common mode rejection ratio) decreases by 20 dB.

[0109] The output accuracy is reduced by about 10%. However, in actual engineering, the human and material resources required for precision matching of the four resistors are too high compared to the return of the obtained accuracy, so the requirements for the overall index accuracy are reduced and compromised for cost control.

[0110] Therefore, this solution discloses an improved Howland constant current source, as Figure 6 shown. A high-precision Howland constant current source is composed of an existing instrumentation operational amplifier and a general-purpose precision operational amplifier. There are 4 ultra-high-precision resistors integrated inside the instrumentation operational amplifier (these resistors have been laser-trimmed by the chip manufacturer to achieve high-precision matching, and the error of the 4 resistors is less than 0.01%).

[0111] As Figure 6 shown in the circuit example for calculation (R2 is a PT1000 temperature sensor):

[0112] Figure 6The constant current output is 500 μA. According to theoretical calculation, the voltage generated across R2 is 1.0000000 V. The actually generated voltage is 1.0001003 V. The error is 0.0001003 V, that is, 100.3 μV. The main contributor to this error is U1 (OP07, a general-purpose precision operational amplifier).

[0113] For a PT1000 temperature sensor, a resistance value of 2 KΩ corresponds to 266.35 °C.

[0114] 1.0001003 V corresponds to a resistance value of 2000.2006 Ω and a temperature of 266.40 °C.

[0115] The absolute error is 0.05 °C, ≈0.0188%.

[0116] Combined with the above method for determining the avoidance route, the temperature monitoring accuracy is further improved, the generation of errors is reduced, and at the same time, resistor pairing is not required, improving the temperature measurement efficiency.

[0117] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A sensor-based method for measuring the temperature of an unmanned aerial vehicle, characterized in that, It includes the following steps: S1. Collect the monitored temperature data of the drone at different locations in the same area, and mark it as the temperature data to be verified; S2. Monitor the temperature measurement process of the drone, and obtain the influencing associated items and associated values corresponding to different temperature data to be verified; S3. Perform clustering processing on each temperature data to be verified, divide multiple temperature ranges, and mark them as the ranges to be verified; S4. Obtain the temperature value of the actual monitored area, compare it with the range to be verified, and divide the range with the lowest numerical deviation and the range with the highest numerical deviation; S5. Combine the industry standards for drone temperature measurement to obtain the conventional values of each influencing associated item; S6. Collect the influencing associated items and associated values in the range with the lowest numerical deviation and the range with the highest numerical deviation, combine the conventional values of each influencing associated item, calculate the influence weights of each influencing associated item in the range with the lowest numerical deviation and the range with the highest numerical deviation, and perform a ranking of the influencing associated items according to the influence weights; S7. Judge the influence trends of the same influencing associated items in the range with the lowest numerical deviation and the range with the highest numerical deviation; When the same influencing associated item is positively correlated in the range with the lowest numerical deviation and the range with the highest numerical deviation, maintain the current ranking; When the same influencing associated item is not positively correlated in the range with the lowest numerical deviation and the range with the highest numerical deviation, move down one ranking; S8. Obtain the ranking of each influencing associated item according to the judgment result, and formulate an avoidance route according to the ranking; 2. The method for measuring the temperature of an unmanned aerial vehicle based on a sensor according to claim 1, wherein: In S1, different locations in the same area are different measurement points in the area with the same actual temperature; 3. The method for measuring the temperature of an unmanned aerial vehicle based on sensors according to claim 1, wherein: The influencing associated items in S2 include the temperature measurement distance of the sensor, the temperature measurement height, the mechanical stress, and the temperature measurement angle of the sensor; Among them, the temperature measurement distance of the sensor is the distance between the sensor induction area and the heat source area; The temperature measurement height is the height of the drone from the ground; The mechanical stress is the vibration frequency of the drone; The temperature measurement angle of the sensor is the included angle formed between the sensor and the drone hovering parallel to the ground during monitoring; 4. The sensor-based UAV temperature measurement method according to claim 1, characterized in that: The method for performing clustering processing on each temperature data to be verified in S3 includes the following steps: S3.

1. Integrate each temperature data to be verified into a data set, and randomly select multiple temperature data to be verified in the current data set as the clustering centers of the first clustering data set; S3.

2. Calculate the distances between each temperature data to be verified and each clustering center, and assign the current temperature data to be verified to the first clustering data set with the closest distance; S3.

3. Calculate the mean values of each temperature data to be verified in the new data set as the clustering centers of the new round of clustering data set; S3.

4. Continue to calculate the distances between each temperature data to be verified and each clustering center, and perform a new round of division of the temperature data to be verified; S3.

5. Until the temperature data to be verified in each data set does not change, otherwise repeat steps S3.3 and S3.4; 5. The method for measuring the temperature of an unmanned aerial vehicle based on sensors according to claim 4, wherein: The method for dividing multiple temperature ranges in S3 includes the following steps: S3.

6. Sort the temperature data to be verified in each temperature range in ascending order; S3.

7. Obtain the maximum value and the minimum value in the temperature range as the range value of the current range to be verified.

6. The sensor-based UAV temperature measurement method according to claim 1, wherein: The method for dividing the lowest value deviation range and the highest value deviation range in S4 includes the following steps: S4.

1. Sort the range values of each item in ascending order; S4.

2. Compare the temperature values in the actual monitoring area with the range values to obtain the maximum difference and the minimum difference; S4.

3. Select the range value where the maximum difference is located as the highest value deviation range; S4.

4. Select the range value where the minimum difference is located as the lowest value deviation range.

7. The sensor-based UAV temperature measurement method according to claim 6, characterized in that: In S4.2, the value for comparing the temperature values in the actual monitoring area with the range values is the maximum or minimum value that makes up the range value.

8. The sensor-based UAV temperature measurement method according to claim 1, wherein: The method for ranking the influence-related items according to the influence weight in S6 includes the following steps: S6.

1. Obtain the correlation values of each influence-related item corresponding to the lowest value deviation range and the highest value deviation range; S6.

2. Calculate the difference between each correlation value and the corresponding normal value, and mark it as the weight influence difference; S6.

3. Define the unit difference and the corresponding unit weight, and calculate the final influence weight = weight influence difference / unit difference × unit weight; S6.

4. Obtain the final influence weights of each influence-related item in the lowest value deviation range and the highest value deviation range, and compare the influence weights; S6.

5. Take the influence-related item with the largest influence weight value as the direct influence-related item of the current highest value deviation range; S6.

6. Take the influence-related item with the smallest influence weight value as the direct influence-related item of the current lowest value deviation range, and rank the remaining influence-related items according to the influence weight.

9. A system for implementing the sensor-based UAV temperature measurement method according to claim 1, characterized in that: It includes a data processing module and an avoidance route planning module. The data processing module cooperates with the sensor to obtain the temperature data of the current temperature measurement area; Among them, the data processing module includes a data clustering processing unit, a data interval division unit, a project weight calculation unit, and an influence trend judgment unit; The data clustering processing unit is used to cluster each temperature data to be verified; The data interval division unit is used to divide multiple temperature intervals, mark them as intervals to be verified, obtain the temperature values in the actual monitoring area, compare them with the intervals to be verified, and divide the lowest value deviation range and the highest value deviation range; The project weight calculation unit is used to collect each influence-related item and its correlation value in the lowest value deviation range and the highest value deviation range, combine the normal values of each influence-related item, calculate the influence weights of each influence-related item in the lowest value deviation range and the highest value deviation range, and rank the influence-related items according to the influence weight; The influence trend judgment unit is used to judge the influence trend of the same influence-related item in the lowest value deviation range and the highest value deviation range; When the same influence-related item is positively correlated in the lowest value deviation range and the highest value deviation range, maintain the current ranking; When the same influence-related item is not positively correlated in the lowest value deviation range and the highest value deviation range, move down one ranking. The influence trend judgment unit obtains the ranking of each influence-related item according to the judgment result, and formulates an avoidance route according to the ranking.

Images