Sensor-based unmanned aerial vehicle temperature measurement method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle temperature measurement, in particular to an unmanned aerial vehicle temperature measurement method and system based on a sensor. The method comprises the steps of monitoring a temperature measurement process of an unmanned aerial vehicle, and obtaining influence association items and association values corresponding to different to-be-verified temperature data; calculating the influence weight of each influence association item in the interval with the lowest value offset and the interval with the highest value offset, and sequencing the influence association items according to the influence weight; and formulating an evasion route according to the syn-order sequence. According to the method, influence weight division is carried out on the influence association items in the temperature measurement process, and the avoidance route is formulated according to the division sequence, so that the sequential avoidance of the influence association items is realized, and the repeated influence of the influence association items caused by disordered avoidance and the reduction of temperature measurement efficiency are avoided, so that the damage degree of the unmanned aerial vehicle in the temperature measurement process is increased; and the service life is influenced.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature measurement of unmanned aerial vehicles, and in particular to a temperature measurement method and system of unmanned aerial vehicles based on sensors. Background Art

[0002] Drone temperature measurement is based on the characteristics of platinum thermal resistors that change with temperature. There is a linear relationship between platinum thermal resistors and temperature, that is, as the temperature rises, the resistance value of platinum will also increase accordingly. This relationship is called the temperature coefficient of the platinum thermal resistor, and the temperature data of the monitored area is obtained through the change in resistance.

[0003] In the process of non-contact temperature measurement by drone, since the area to be measured is generally an area that cannot be directly reached by humans and has certain risks, such as temperature monitoring of high-temperature gases around a kiln, drones are required to fly to specific areas for temperature monitoring.

[0004] Since the temperature measurement process of the drone will be affected by various external factors, such as distance, sensor angle and mechanical stress, etc., which will affect the final temperature monitoring results, if the influence of these external environments cannot be reasonably avoided, errors will occur in the final monitoring results.

[0005] The traditional approach is to control the drone for real-time avoidance. For example, when a heat flow area is found on the drone, causing the entire drone to be mechanically shaken, the drone needs to be controlled to leave the current area and then control the drone to the next monitoring point. However, in actual situations, different monitoring points may be affected by different external factors. If 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 address 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 sensor-based UAV temperature measurement method and system to solve the problems raised in the above background technology.

[0008] To achieve the above object, one of the objects of the present invention is to provide a temperature measurement method of a drone based on a sensor, comprising the following steps: S1. Collect temperature data monitored by drones at different locations in the same area and mark them as temperature data to be verified; S2. Monitor the temperature measurement process of the drone and obtain the impact-related items and related values ​​corresponding to different temperature data to be verified; S3, clustering the temperature data to be verified, dividing them into multiple temperature intervals, and marking them as intervals to be verified; S4, obtaining the temperature value of the actual monitoring area, comparing it with the interval to be verified, and dividing it into the interval with the lowest value deviation and the interval with the highest value deviation; S5. Combined with the industry standard of drone temperature measurement, obtain the conventional values ​​of various impact-related items; S6. Collect various impact-related items and associated values ​​in the lowest numerical deviation interval and the highest numerical deviation interval, calculate the impact weights of various impact-related items in the lowest numerical deviation interval and the highest numerical deviation interval in combination with the conventional values ​​of various impact-related items, and rank the impact-related items according to the impact weights; S7, determining the impact trend of the same impact-related items in the lowest numerical deviation interval and the highest numerical deviation interval; When the same impact-related items are positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking is maintained; When the same impact-related items are not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, they are moved down one rank; S8. Obtain the ranking of each impact-related project based on the judgment result, and formulate an avoidance route according to the ranking.

[0009] As a further improvement of the technical solution, the different locations in the same area in S1 are different measurement points in the same area of ​​actual temperature.

[0010] As a further improvement of the 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; The sensor temperature measurement distance is the distance between the sensor sensing area and the heat source area; The temperature measurement height is the height of the drone from the ground; Mechanical stress is the jitter frequency of the drone; The temperature measurement angle of the sensor is the angle formed by the sensor and the drone hovering parallel to the ground during monitoring.

[0011] As a further improvement of the technical solution, the method of clustering the various temperature data to be verified in S3 includes the following steps: S3.1. Integrate various 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 center of the first clustering data set; S3.2, calculate the distance between each temperature data to be verified and each cluster center, and assign the current temperature data to be verified to the first cluster data set with the closest distance; S3.3, calculate the mean of each temperature data to be verified in the new data set as the clustering center of the new round of clustering data set; S3.4, continue to calculate the distance between each temperature data to be verified and each cluster center, and divide a new round of temperature data to be verified; S3.5. Repeat steps S3.3 and S3.4 until the temperature data to be verified in each data set does not change. Otherwise, repeat steps S3.3 and S3.4.

[0012] As a further improvement of the technical solution, the method of dividing multiple temperature intervals in S3 includes the following steps: S3.6. Sort the temperature data to be verified in each temperature range by size; S3.7. Obtain the maximum and minimum values ​​in the temperature interval as the interval range value of the current interval to be verified.

[0013] As a further improvement of the technical solution, the method of dividing the minimum interval of numerical deviation and the maximum interval of numerical deviation in S4 includes the following steps: S4.1. Sort the values ​​of each interval range by size; S4.2, and compare the temperature value of the actual monitoring area with the interval range value to obtain the maximum difference and the minimum difference; S4.3, select the interval value where the maximum difference is located as the interval with the highest numerical deviation; S4.4. Select the interval value where the minimum difference exists as the minimum interval of numerical deviation.

[0014] As a further improvement of the present technical solution, the value obtained by comparing the temperature value of the actual monitoring area with the interval range value in S4.2 is the maximum value or the minimum value constituting the interval range value.

[0015] As a further improvement of the technical solution, the method of ranking the impact-related projects according to the impact weights in S6 includes the following steps: S6.1. Obtain the associated values ​​of various items affecting the associated items corresponding to the lowest interval of the value deviation and the highest interval of the value deviation; S6.2. Calculate the difference between each associated value and the corresponding regular value, and mark it as the weight impact difference; S6.3. Establish the unit difference and the corresponding unit weight, and calculate the final impact weight = weight impact difference / unit difference × unit weight; S6.4. Obtain the final impact weights of each impact-related item in the lowest numerical deviation interval and the highest numerical deviation interval, and compare the impact weights; S6.5. The impact-related item with the largest impact weight value is used as the direct impact-related item in the highest range of current value deviation; S6.6. The impact-related item with the smallest impact weight value is used as the direct impact-related item in the lowest range of the current value deviation, and the remaining impact-related items are sorted according to the impact weight.

[0016] The second object of the present invention is to provide a system for implementing a sensor-based drone temperature measurement method, including a data processing module and an avoidance route planning module, wherein the data processing module cooperates with the sensor to obtain temperature data of the current temperature measurement area; 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; The data clustering processing unit is used to perform clustering processing on each item of temperature data to be verified; The data interval division unit is used to divide multiple temperature intervals, mark them as intervals to be verified, and obtain the temperature value of the actual monitoring area, compare it with the interval to be verified, and divide it into an interval with the lowest value deviation and an interval with the highest value deviation; The item weight calculation unit is used to collect various impact-related items and associated values ​​in the lowest interval of numerical deviation and the highest interval of numerical deviation, calculate the impact weights of various impact-related items in the lowest interval of numerical deviation and the highest interval of numerical deviation in combination with the conventional values ​​of various impact-related items, and rank the impact-related items according to the impact weights; The influence trend determination unit is used to determine the influence trend of the same influence-related items in the lowest interval of numerical deviation and the highest interval of numerical deviation; When the same impact-related items are positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking is maintained; When the same impact-related items are not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, they are moved down one rank; The impact trend judgment unit obtains the ranking of various impact-related items according to the judgment result, and formulates an avoidance route according to the ranking.

[0017] Compared with the prior art, the present invention has the following beneficial effects: In the sensor-based UAV temperature measurement method and system, the impact weights of the related items affecting the temperature measurement process are divided, and avoidance routes are formulated according to the division order, so as to achieve sequential avoidance of the related items affecting the temperature measurement process, avoid disorderly avoidance and cause repeated impact on the related items affecting the temperature measurement process, resulting in reduced temperature measurement efficiency, increased damage to the UAV during the temperature measurement process, and affecting its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the overall method of the present invention; Figure 2 A flow chart of a method for clustering various temperature data to be verified according to the present invention; Figure 3 A flow chart of a method for dividing multiple temperature intervals according to the present invention; Figure 4 A flow chart of a method for dividing a minimum interval of numerical deviation and a maximum interval of numerical deviation according to the present invention; Figure 5 A flow chart of a method for ranking impact-related items according to impact weights of the present invention; Figure 6 It is the overall system structure block diagram of the present invention; Figure 7 It is a schematic diagram of a common Howland constant current source circuit of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, one of the purposes of the present invention is to provide a sensor-based drone temperature measurement method, comprising the following steps: S1. Collect temperature data monitored by drones at different locations in the same area and mark them as temperature data to be verified; S2. Monitor the temperature measurement process of the drone and obtain the impact-related items and related values ​​corresponding to different temperature data to be verified; S3, clustering the temperature data to be verified, dividing them into multiple temperature intervals, and marking them as intervals to be verified; S4, obtaining the temperature value of the actual monitoring area, comparing it with the interval to be verified, and dividing it into the interval with the lowest value deviation and the interval with the highest value deviation; S5. Combined with the industry standard of drone temperature measurement, obtain the conventional values ​​of various impact-related items; S6. Collect various impact-related items and associated values ​​in the lowest numerical deviation interval and the highest numerical deviation interval, calculate the impact weights of various impact-related items in the lowest numerical deviation interval and the highest numerical deviation interval in combination with the conventional values ​​of various impact-related items, and rank the impact-related items according to the impact weights; S7, determining the impact trend of the same impact-related items in the lowest numerical deviation interval and the highest numerical deviation interval; When the same impact-related items are positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking is maintained; When the same impact-related items are not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, they are moved down one rank; S8. Obtain the ranking of each impact-related project based on the judgment result, and formulate an avoidance route according to the ranking.

[0021] In specific use, the present invention provides a UAV temperature measurement method that can plan the UAV monitoring path according to the influence trend of different external factors, and the specific contents are as follows: Before the specific temperature measurement, in order to obtain the influence of various external factors on the drone during the temperature measurement process, it is necessary to determine it through specific temperature values. Therefore, it is necessary to first collect the monitoring temperature data of the drone at different locations in the same area, where the same area and different locations are defined in the present invention as areas with the same actual temperature, that is, the change in the drone temperature measurement at this time is not the actual temperature, and the final 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 caused to the drone at different locations are different, which are marked as temperature data to be verified, that is, the current temperature data has not been verified and may be greatly affected by external factors, so the corresponding temperature data is easy to differ from the actual value; 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 impact-related items, and different impact-related items correspond to specific associated values. The impact-related items involved in the present invention include the sensor temperature measurement distance, temperature measurement height, mechanical stress, and sensor temperature measurement angle; The sensor temperature measurement distance is the distance between the sensor sensing area and the heat source area; The temperature measurement height is the height of the drone from the ground; Mechanical stress is the jitter frequency of the drone; The temperature measurement angle of the sensor is the angle formed by the sensor and the drone hovering parallel to the ground during monitoring; After completing the determination of various impact-related items, in order to obtain the relationship between the measured temperature and the impact-related items, it is necessary to cluster the temperature data to be verified, such as Figure 2 As shown, the specific clustering method is as follows: Integrate the various temperature data to be verified into a data set, randomly select multiple temperature data to be verified in the current data set as the cluster center of the first clustering data set, calculate the distance between each temperature data to be verified and each cluster center, that is, the temperature difference between the two, the lower the difference, the closer the distance, and vice versa, the farther the distance, assign the current temperature data to be verified to the first clustering data set with the nearest distance, calculate the mean of each temperature data to be verified in the new data set, as the cluster center of the second clustering data set, continue to calculate the distance between each temperature data to be verified and each cluster center, and perform secondary 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 distance to the cluster center in the current data set is the closest. At this time, different data sets store temperature data with different data ranges, and divide the temperature interval according to the data range in the data set, marked as intervals to be verified, such as Figure 3 As shown, the specific division steps are as follows: First, since the temperature data to be verified in each data set are distributed according to the cluster center, the values ​​are different. They need to be sorted according to the values ​​and the maximum and minimum values ​​are obtained as the interval range value of the current interval to be verified. For example, in a data set, it is {300; 305; 306; 296; 298; 308; 307; 295; 299}, and the corresponding interval range value is [295,308].

[0022] After the interval range values ​​are determined, since there are differences between the interval range values ​​and the actually measured temperature values, in order to obtain the relationship between the temperature value and the impact-related items, it is necessary to obtain the temperature value of the actual monitoring area. For example, through the point-by-point direct contact measurement method, the average value is obtained as the temperature value of the actual monitoring area, and the temperature value of the actual monitoring area is compared with the interval range values ​​obtained previously, and the interval with the lowest value deviation and the interval with the highest value deviation are divided, such as Figure 4 The specific contents are as follows: First, sort the range values ​​of each interval. For example, the range values ​​after division are as well as , where the temperature value of the actual monitoring area is , sort the values ​​of each interval range by size, where , and compare the temperature value of the actual monitoring area with it, that is, calculate and as well as Perform difference calculation to obtain the maximum difference and the minimum difference, and select the interval value where the maximum difference is located as the highest interval of numerical deviation, and select the interval value where the minimum difference is located as the lowest interval of numerical deviation, for example and The maximum difference between and The difference between is the smallest, then the highest range of numerical deviation is , the minimum range of numerical deviation is .

[0023] Since the acquisition of values ​​in different interval ranges is affected by multiple impact-related items, but the weights of the main impacts are different, in order to obtain the impact weights of each impact-related item and formulate subsequent avoidance routes based on the impact weights, it is necessary to combine the industry standards for drone temperature measurement to obtain the conventional values ​​of each impact-related item, that is, the conventional values ​​will not affect the temperature measurement value, and calculate the impact weights of each impact-related item in the lowest value deviation interval and the highest value deviation interval based on the conventional values, such as Figure 5 As shown, the specific steps are as follows: First, obtain the associated values ​​of each impact-related item corresponding to the lowest interval of numerical offset and the highest interval of numerical offset, calculate the difference between each associated value and the corresponding regular value, mark it as the weighted impact difference, formulate the unit difference and the corresponding unit weight, calculate the final impact weight = weighted impact difference / unit difference × unit weight, obtain the final impact weight of each impact-related item in the lowest interval of numerical offset and the highest interval of numerical offset, compare the impact weights, take the impact-related item with the largest impact weight value as the direct impact-related item of the current highest interval of numerical offset, take the impact-related item with the smallest impact weight value as the direct impact-related item of the current lowest interval of numerical offset, and sort the remaining impact-related items according to their impact weights.

[0024] In order to further refine the relationship between the impact of each influencing associated item on the temperature value, it is necessary to verify through multiple intervals. At this time, the impact trend of the same influencing associated item in the lowest numerical offset interval and the highest numerical offset interval is judged. That is, if the direct influencing associated item with the lowest impact weight in the current lowest numerical offset interval has the highest impact weight in the highest numerical offset interval, it means that the current direct influencing associated item is positively correlated in the lowest numerical offset interval and the highest numerical offset interval. At this time, the ranking of the current direct influencing associated item remains unchanged, and the impact trend judgment of the next-ranked influencing associated item continues. If the impact trend is positively correlated, the rank is maintained unchanged. Otherwise, it is downgraded by one rank. It is worth noting that when the current rank and the next-ranked influencing associated item are not positively correlated, the current rank influencing associated item does not change rank.

[0025] After completing the priority determination work of various impact-related items, formulate a priority ranking and formulate an avoidance route according to the priority of each impact-related item. For example, according to the priority judgment result, the impact weights of sensor temperature measurement distance, temperature measurement height, mechanical stress and sensor temperature measurement angle are ranked as mechanical stress, sensor temperature measurement distance, temperature measurement height and sensor temperature measurement angle. At this time, the first impact-related item to be avoided is mechanical stress. That is, when adjusting the flight route of the drone, it is necessary to avoid areas with large mechanical stress, and avoid the next priority impact-related item within the controllable range of mechanical stress, until all impact-related items are within the controllable range, thereby realizing the sequential avoidance of impact-related items and avoiding the repeated impact of impact-related items caused by disorderly avoidance, resulting in reduced temperature measurement efficiency, and increasing the degree of damage suffered by the drone during the temperature measurement process, affecting its service life.

[0026] The second object of the present invention is to provide a system for realizing a sensor-based UAV temperature measurement method, including a data processing module and an avoidance route planning module, wherein the data processing module cooperates with the sensor to obtain 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 impact trend judgment unit; The data clustering processing unit is used to perform clustering processing on each item of temperature data to be verified; The data interval division unit is used to divide multiple temperature intervals, mark them as intervals to be verified, and obtain the temperature value of the actual monitoring area, compare it with the interval to be verified, and divide it into the interval with the lowest value deviation and the interval with the highest value deviation; The project weight calculation unit is used to collect various impact-related projects and associated values ​​in the lowest value deviation interval and the highest value deviation interval, calculate the impact weights of various impact-related projects in the lowest value deviation interval and the highest value deviation interval in combination with the conventional values ​​of various impact-related projects, and rank the impact-related projects according to the impact weights; The influence trend judgment unit is used to judge the influence trend of the same influence-related items in the lowest interval of numerical deviation and the highest interval of numerical deviation; When the same impact-related items are positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking is maintained; When the same impact-related items are not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, they are moved down one rank; The impact trend judgment unit obtains the ranking of various impact-related projects according to the judgment result, and formulates an avoidance route according to the ranking.

[0027] It is worth noting that in the process of temperature monitoring with sensors, the existing processing methods are as follows Figure 7As shown, a common Howland constant current source circuit is used, which is composed of R1, R2, R3, R4 and an operational amplifier. The output current changes with the change of input voltage and does not change with the change of load, thus achieving the purpose of constant current.

[0028] Although this circuit is simple and has good constant current accuracy, it also has the following disadvantages: 1. The accuracy of resistor matching is extremely high to obtain high output impedance; 2. The input source impedance will increase the resistance of R1, so its value must be very low to minimize matching errors; 3. The power supply voltage must be much higher than the maximum output voltage; 4. The CMRR (common mode rejection ratio) performance of the operational amplifier must be relatively good; According to experimental results, for every 0.02% increase in the error of the four resistors R1~R4, the CMRR (common mode rejection ratio) decreases by 20dB.

[0029] The output accuracy is reduced by about 10%. However, in real projects, the manpower and material resources spent on precision matching of four resistors are too high compared to the return of precision, so the requirements for overall indicator accuracy are reduced and compromised for cost control.

[0030] Therefore, this solution discloses an improved Howland constant current source, such as Figure 6 As shown, a high-precision Howland constant current source is formed by combining an existing instrumentation operational amplifier and a general precision operational amplifier. Four ultra-high precision resistors are integrated inside the instrumentation operational amplifier (these resistors are laser trimmed by the chip manufacturer to achieve high-precision matching, and the error of the four resistors is less than 0.01%).

[0031] like Figure 6 Calculate the circuit example shown (R2 is a PT1000 temperature sensor): Figure 6 The constant current output is 500uA. According to theoretical calculation, the voltage generated across R2 is 1.0000000V. The actual voltage generated is 1.0001003V. The error is 0.0001003V, or 100.3uV. The main contributor to this error is U1 (OP07, a general-purpose precision operational amplifier).

[0032] For the PT1000 temperature sensor, the resistance value of 2KΩ corresponds to 266.35℃.

[0033] 1.0001003V corresponds to a resistance of 2000.2006Ω and a temperature of 266.40℃.

[0034] The absolute error is 0.05℃, ≈0.0188%.

[0035] The above-mentioned avoidance route determination method is used to further improve the temperature monitoring accuracy and reduce the generation of errors. At the same time, there is no need to pair resistors, thereby improving the temperature measurement efficiency.

[0036] 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 descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A sensor-based UAV temperature measurement method, characterized in that: The steps include: S1. Collect temperature data monitored by drones at different locations in the same area and mark them as temperature data to be verified; S2. Monitor the temperature measurement process of the drone and obtain the impact-related items and related values ​​corresponding to different temperature data to be verified; S3, clustering the temperature data to be verified, dividing them into multiple temperature intervals, and marking them as intervals to be verified; S4, obtaining the temperature value of the actual monitoring area, comparing it with the interval to be verified, and dividing it into the interval with the lowest value deviation and the interval with the highest value deviation; S5. Combined with the industry standard of drone temperature measurement, obtain the conventional values ​​of various impact-related items; S6. Collect various impact-related items and associated values ​​in the lowest numerical deviation interval and the highest numerical deviation interval, calculate the impact weights of various impact-related items in the lowest numerical deviation interval and the highest numerical deviation interval in combination with the conventional values ​​of various impact-related items, and rank the impact-related items according to the impact weights; S7, determining the impact trend of the same impact-related items in the lowest numerical deviation interval and the highest numerical deviation interval; When the same impact-related items are positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking is maintained; When the same impact-related items are not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, they are moved down one rank; S8. Obtain the ranking of each impact-related project based on the judgment result, and formulate an avoidance route according to the ranking.

2. The sensor-based temperature measurement method for unmanned aerial vehicles according to claim 1, characterized in that: The different locations in the same area in S1 are different measurement points in the same area of ​​actual temperature.

3. The sensor-based temperature measurement method for drones according to claim 1, characterized in that: 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; The sensor temperature measurement distance is the distance between the sensor sensing area and the heat source area; The temperature measurement height is the height of the drone from the ground; Mechanical stress is the UAV vibration frequency; The temperature measurement angle of the sensor is the angle formed by the sensor and the drone hovering parallel to the ground during monitoring.

4. The sensor-based temperature measurement method for unmanned aerial vehicles according to claim 1, characterized in that: The method for clustering the various temperature data to be verified in S3 comprises the following steps: S3.

1. Integrate various 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 center of the first clustering data set; S3.2, calculate the distance between each temperature data to be verified and each cluster center, and assign the current temperature data to be verified to the first cluster data set with the closest distance; S3.3, calculate the mean of each temperature data to be verified in the new data set as the clustering center of the new round of clustering data set; S3.4, continue to calculate the distance between each temperature data to be verified and each cluster center, and divide a new round of temperature data to be verified; S3.

5. Repeat steps S3.3 and S3.4 until the temperature data to be verified in each data set does not change. Otherwise, repeat steps S3.3 and S3.

4.

5. The sensor-based temperature measurement method for unmanned aerial vehicles according to claim 4, characterized in that: The method for dividing multiple temperature intervals in S3 comprises the following steps: S3.

6. Sort the temperature data to be verified in each temperature range by size; S3.

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

6. The sensor-based temperature measurement method for unmanned aerial vehicles according to claim 1, characterized in that: The method of dividing the minimum interval of numerical deviation and the maximum interval of numerical deviation in S4 comprises the following steps: S4.

1. Sort the values ​​of each interval range by size; S4.2, compare the temperature value of the actual monitoring area with the interval range value to obtain the maximum difference and the minimum difference; S4.3, select the interval value where the maximum difference is located as the interval with the highest numerical deviation; S4.

4. Select the interval value where the minimum difference exists as the minimum interval of numerical deviation.

7. The sensor-based temperature measurement method for unmanned aerial vehicles according to claim 6, characterized in that: The value obtained by comparing the temperature value of the actual monitoring area with the interval range value in S4.2 is the maximum value or the minimum value of the interval range value.

8. The sensor-based temperature measurement method for unmanned aerial vehicles according to claim 1, characterized in that: The method for ranking the impact-related projects according to the impact weights in S6 comprises the following steps: S6.

1. Obtain the associated values ​​of various items affecting the associated items corresponding to the lowest interval of the value deviation and the highest interval of the value deviation; S6.

2. Calculate the difference between each associated value and the corresponding regular value, and mark it as the weight impact difference; S6.

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

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

5. The impact-related item with the largest impact weight value is used as the direct impact-related item in the highest range of current value deviation; S6.

6. The impact-related item with the smallest impact weight value is used as the direct impact-related item in the lowest range of the current value deviation, and the remaining impact-related items are sorted according to the impact weight.

9. A system for implementing the sensor-based temperature measurement method for unmanned aerial vehicles according to claim 1, characterized in that: It includes a data processing module and an avoidance route planning module, wherein the data processing module cooperates with the sensor to obtain the temperature data of the current temperature measurement area; 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; The data clustering processing unit is used to perform clustering processing on each item of temperature data to be verified; The data interval division unit is used to divide multiple temperature intervals, mark them as intervals to be verified, and obtain the temperature value of the actual monitoring area, compare it with the interval to be verified, and divide it into an interval with the lowest value deviation and an interval with the highest value deviation; The item weight calculation unit is used to collect various impact-related items and associated values ​​in the lowest interval of numerical deviation and the highest interval of numerical deviation, calculate the impact weights of various impact-related items in the lowest interval of numerical deviation and the highest interval of numerical deviation in combination with the conventional values ​​of various impact-related items, and rank the impact-related items according to the impact weights; The influence trend determination unit is used to determine the influence trend of the same influence-related items in the lowest interval of numerical deviation and the highest interval of numerical deviation; When the same impact-related items are positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, the current ranking is maintained; When the same impact-related items are not positively correlated in the interval with the lowest numerical deviation and the interval with the highest numerical deviation, they are moved down one rank; The impact trend judgment unit obtains the ranking of various impact-related items according to the judgment result, and formulates an avoidance route according to the ranking.

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