Inspection processing method and system for heat supply pipe network based on unmanned aerial vehicle
By dividing the heating pipeline network into multiple areas, determining the leakage risk coefficient based on historical and real-time monitoring data analysis, and formulating drone inspection strategies, the problem of insufficient reliability of leakage detection and treatment of heating pipeline networks in the existing technology is solved, and more accurate and reliable inspection and treatment are achieved.
Patent Information
- Application Number
- CN202510093380.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-21
- Publication Date
- 2025-06-03
AI Technical Summary
In the leak detection processing of heating pipeline networks, the prior art neglects to determine the leak detection strategy of the drone in combination with the real-time monitoring data of the heating pipeline network, resulting in insufficient reliability of the leak detection processing.
By dividing the heating pipeline network into multiple areas, using historical monitoring data to analyze and determine the leakage risk coefficients in different locations, and combining real-time monitoring data and infrared data to determine the regional risk coefficients of the area, thereby formulating differentiated drone inspection strategies.
The regional risk coefficient is determined from the two perspectives of the distribution of leakage risk locations in the area and the leakage risk coefficients at different locations, which improves the reliability and accuracy of inspection and processing.
Smart Images
Figure CN120083922A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of heating pipe networks, and particularly relates to an inspection and processing method and system for heating pipe networks based on unmanned aerial vehicles (UAVs). Background Art
[0002] In order to solve the problem of leak detection and treatment of heating pipe networks, specifically in the invention patent application CN114355969A, "An intelligent heating pipe network leak detection method and system using UAV inspection", a UAV is equipped with a thermal camera to automatically identify abnormal ambient temperatures where pipe network leaks occur, and the leak points are identified through the ambient temperatures recorded by the thermal camera, making the leak detection inspection records informatized, numericalized, and data traceable. However, there are the following technical problems: When conducting leak detection and treatment of heating pipe networks, the existing technical solutions neglect to determine the leak detection strategy of UAVs by combining the real-time monitoring data of heating pipe networks. The real-time monitoring data of heating pipe networks can reflect the leakage situation in the heating pipe networks. Therefore, if the leak detection strategy of UAVs cannot be determined by combining the real-time monitoring data of heating pipe networks, the reliability of leak detection and treatment cannot be guaranteed.
[0003] In view of the above technical problems, the present invention provides an inspection and processing method and system for heating pipe networks based on UAVs. Summary of the Invention
[0004] An inspection and processing method for heating pipe networks based on UAVs specifically includes: Dividing the heating pipe network into multiple regions, and determining the leakage risk coefficients at different positions in the regions by using the analysis results of historical monitoring data of different regions; When it is determined that there are leakage risk positions in the region based on the leakage risk coefficients, obtaining the distribution data of the leakage risk positions in the region, and determining the regional risk coefficient of the region by combining the leakage risk coefficients of the leakage risk positions in the region; When the regional risk coefficient of the region does not meet the requirements, determining the inspection and processing strategy of the UAV in the region by using the regional risk coefficient of the region, and determining the real-time leakage risks at different inspection positions in the region by using the analysis results of the real-time monitoring data and real-time infrared data of the inspection positions; When the regional risk coefficient of the region meets the requirements, determining the suspected risk inspection positions among the inspection positions based on the analysis results of the real-time infrared data of different inspection positions, and determining the real-time leakage risks at different suspected risk inspection positions in the region by using the real-time infrared data and real-time monitoring data of the suspected risk inspection positions.
[0005] The beneficial effects of the present invention are as follows: Based on the distribution data of leakage risk positions in the area and the leakage risk coefficients of different leakage risk positions, the area risk coefficient of the area is determined, thus realizing the determination of the area risk coefficient from two perspectives: the distribution aggregation of leakage risk positions in the area and the leakage risk coefficients of different leakage risk positions, and also laying a foundation for further adopting differentiated inspection and treatment methods according to different areas.
[0006] Using the real-time infrared data and real-time monitoring data of suspected risk inspection positions to determine the real-time leakage risks of different suspected risk inspection positions in the area, avoiding the technical problem of inaccurate judgment results of the real-time leakage risks of suspected risk inspection positions caused by solely considering real-time monitoring data, and improving the accuracy of the judgment results of real-time leakage risks by further combining the real-time infrared data of suspected risk inspection positions.
[0007] A further technical solution is to divide the heat supply pipeline network into multiple areas, specifically including: Dividing the heat supply pipeline network into multiple areas according to a preset area.
[0008] A further technical solution is that the historical monitoring data includes the flow rate and temperature at different positions in the area.
[0009] A further technical solution is that the method for determining the leakage risk coefficient of the position is: Based on the analysis data of the position in the heat supply pipeline network, determine the similar heat supply position of the position; According to the deviation situation between the historical monitoring data of the similar heat supply position and the historical monitoring data of the position, determine the monitoring data deviation moment of the position; Determine the leakage risk coefficient of the position according to the monitoring data deviation moment of the position.
[0010] A further technical solution is that the similar heat supply position of the position is determined according to the distance between the position and the heat exchange station.
[0011] A further technical solution is that the monitoring data deviation moment is the moment when the deviation between the historical monitoring data and the historical monitoring data of the similar heat supply position is not within the preset range.
[0012] A further technical solution is that the leakage risk coefficient of the position is determined according to the preset risk coefficient corresponding to the monitoring data deviation moment of the position.
[0013] A further technical solution is that when the leakage risk coefficient of the position does not meet the requirements, then determine the position as a leakage risk position.
[0014] A further technical solution lies in that the method for determining the suspected risk inspection positions among the inspection positions is as follows: Based on the analysis result of the real-time infrared data of the inspection position, determine the infrared image features of the real-time infrared data of the inspection position; According to the analysis result of the infrared image features, determine the preset leakage risk coefficient corresponding to the infrared image features, and use the preset leakage risk coefficient corresponding to the infrared image features as the image risk coefficient of the inspection position; Based on the image risk coefficient, determine the suspected risk inspection positions among the inspection positions.
[0015] A further technical solution lies in that determining the suspected risk inspection positions among the inspection positions based on the image risk coefficient specifically includes: Regard the inspection positions whose image risk coefficients do not meet the requirements as the suspected risk inspection positions.
[0016] In a second aspect, the present application provides an inspection processing system for a heat supply pipeline network based on an unmanned aerial vehicle, adopting the above-mentioned inspection processing method for a heat supply pipeline network based on an unmanned aerial vehicle, specifically including: A risk coefficient evaluation module, a regional risk evaluation module, and an inspection processing module; Among them, the regional coefficient evaluation module is responsible for dividing the heat supply pipeline network into multiple regions, and using the analysis result of the historical monitoring data of different regions to determine the leakage risk coefficients of different positions in the regions; When the regional risk evaluation module is responsible for determining that there are leakage risk positions in the region based on the leakage risk coefficient, obtain the distribution data of the leakage risk positions in the region, and combine the leakage risk coefficients of the leakage risk positions in the region to determine the regional risk coefficient of the region; The inspection processing module is responsible for generating a differentiated real-time leakage risk and inspection strategy according to the regional risk coefficient of the region. Description of the Drawings
[0017] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.
[0018] Figure 1 It is a flowchart of an inspection processing method for a heat supply pipeline network based on an unmanned aerial vehicle.
[0019] Figure 2 It is a flowchart of a method for determining the leakage risk coefficient of a position; Figure 3 It is a flowchart of a method for determining the regional risk coefficient of a region. Detailed Embodiments
[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various ways and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar structures and thus their detailed description will be omitted.
[0021] The terms "a", "an", "the", and "said" are used to denote the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to mean an open inclusion and mean that there may be additional elements / components / etc. in addition to the listed elements / components / etc.
[0022] Example 1 To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, a patrol processing method for a heating pipe network based on an unmanned aerial vehicle is provided, specifically including: Dividing the heating pipe network into multiple regions, and using the analysis results of historical monitoring data of different regions to determine the leakage risk coefficients of different positions in the regions; When it is determined that there are leakage risk positions in the region based on the leakage risk coefficients, obtaining the distribution data of the leakage risk positions in the region, and combining the leakage risk coefficients of the leakage risk positions in the region to determine the regional risk coefficient of the region; When the regional risk coefficient of the region does not meet the requirements, using the regional risk coefficient of the region to determine the patrol processing strategy of the unmanned aerial vehicle in the region, and using the analysis results of the real-time monitoring data and real-time infrared data of the patrol positions to determine the real-time leakage risks of different patrol positions in the region; When the regional risk coefficient of the region meets the requirements, determining the suspected risk patrol positions among the patrol positions based on the analysis results of the real-time infrared data of different patrol positions, and using the real-time infrared data and real-time monitoring data of the suspected risk patrol positions to determine the real-time leakage risks of different suspected risk patrol positions in the region.
[0023] Specifically, dividing the heating pipe network into multiple regions specifically includes: Dividing the heating pipe network into multiple regions according to a preset region.
[0024] It can be understood that the historical monitoring data includes the flow rate and temperature of different positions in the region.
[0025] It should be noted that, as Figure 2 shown, the method for determining the leakage risk coefficient of the position is: Determine the similar heating positions of the location based on the analysis data of the heating pipe network at the location; Determine the monitoring data deviation moments of the location according to the deviation between the historical monitoring data of the similar heating positions and the historical monitoring data of the location; Determine the leakage risk coefficient of the location according to the monitoring data deviation moments of the location.
[0026] Furthermore, the similar heating positions of the location are determined according to the distance between the location and the heat exchange station.
[0027] It can be understood that the monitoring data deviation moments are the moments when the deviation between the historical monitoring data and the historical monitoring data of the similar heating positions is not within the preset range.
[0028] Specifically, the leakage risk coefficient of the location is determined according to the preset risk coefficient corresponding to the monitoring data deviation moments of the location.
[0029] Furthermore, when the leakage risk coefficient of the location does not meet the requirements, it is determined that the location is a leakage risk location.
[0030] Optionally, the method for determining the leakage risk coefficient of the location is as follows: S11 Determine the similar heating positions of the location based on the analysis data of the heating pipe network at the location, and determine the reference monitoring data of the location according to the historical monitoring data of the similar heating positions; S12 Determine the monitoring data deviation moments of the location according to the deviation between the reference monitoring data and the historical monitoring data of the location, and determine the monitoring data deviation coefficients of different dates by using the monitoring data deviation moments of different dates and the deviation amounts of the monitoring data deviation moments; S13 Determine the leakage risk coefficient of the location according to the monitoring data deviation coefficients of different dates.
[0031] Optionally, the following content is included in step S12 above: S121 Determine that the location does not belong to the leakage risk location when it is determined that there are no monitoring data deviation moments of the location according to the deviation between the reference monitoring data and the historical monitoring data of the location. When there are monitoring data deviation moments of the location, go to step S122; S122 Obtain the number of monitoring data deviation moments of the location. When the number of monitoring data deviation moments of the location meets the requirements, go to step S123. When the number of monitoring data deviation moments of the location does not meet the requirements, go to step S124; S123 Take the date of the moment when there is a deviation in the monitoring data at the said location as the deviation date. When the quantity of the said deviation dates meets the requirement, it is determined that the said location does not belong to the leakage risk location. When the quantity of the said deviation dates does not meet the requirement, proceed to step S124; S124 Use the moments of deviation in the monitoring data on different dates and the deviation amounts at the moments of deviation in the monitoring data to determine the monitoring data deviation coefficients for different dates. When there is a date for which the monitoring data deviation coefficient does not meet the requirement, proceed to step S13. When there is no date for which the monitoring data deviation coefficient does not meet the requirement, it is determined that the said location does not belong to the leakage risk location.
[0032] Optionally, the following content is included in the above step S13: S131 Take the dates for which the number of times of deviation in the monitoring data does not meet the requirement as the data deviation dates. When the quantity of the data deviation dates meets the requirement, proceed to step S132. When the quantity of the said data deviation dates does not meet the requirement, proceed to step S133; S132 Determine the total number of times of deviation in the monitoring data for the data deviation dates based on the number of times of deviation in the monitoring data for different data deviation dates. When the total number of times of deviation in the monitoring data for the said data deviation dates meets the requirement, it is determined that the said location does not belong to the leakage risk location. When the total number of times of deviation in the monitoring data for the said data deviation dates does not meet the requirement, proceed to step S133; S133 Determine the leakage risk coefficient of the said location based on the monitoring data deviation coefficients for different dates.
[0033] Specifically, as Figure 3 shown, the method for determining the regional risk coefficient of the said region is as follows: Based on the distribution data of the leakage risk locations in the said region, determine the quantity of the leakage risk locations in the said region, and use the ratio of the quantity of the leakage risk locations to the preset quantity to determine the basic risk coefficient of the said region; Based on the leakage risk coefficients of the leakage risk locations in the said region, determine the average value of the leakage risk coefficients of the leakage risk locations in the said region, and take it as the average risk coefficient; Based on the average risk coefficient and the basic risk coefficient, determine the regional risk coefficient of the said region.
[0034] Furthermore, the regional risk coefficient of the said region is determined based on the product of the average risk coefficient and the basic risk coefficient.
[0035] In addition, it should be noted that the value range of the regional risk coefficient of the said region is between 0 and 1. Among them, when the regional risk coefficient of the said region is greater than the preset risk coefficient, it is determined that the regional risk coefficient of the said region does not meet the requirement.
[0036] It is understandable that the method for determining the regional risk coefficient of the said region is as follows: Based on the distribution data of the leakage risk positions in the said region, determine the number of leakage risk positions in the said region. When the number of leakage risk positions in the said region is greater than the preset number of risk positions, it is determined that the regional risk coefficient of the said region does not meet the requirements; When the number of leakage risk positions in the said region is not greater than the preset number of risk positions: When it is determined according to the leakage risk coefficients of different leakage risk positions that there is no leakage risk position in the said region with a leakage risk coefficient greater than the preset risk coefficient threshold: Determine the average value of the leakage risk coefficients of the leakage risk positions in the said region and use it as the average risk coefficient. When the average risk coefficient is less than the preset coefficient threshold, it is determined that the regional risk coefficient of the said region meets the requirements; When the average value of the said regional coefficient is not less than the preset coefficient threshold or there is a leakage risk position with a leakage risk coefficient greater than the preset risk coefficient threshold: Take the leakage risk positions with leakage risk coefficients greater than the preset risk coefficient threshold as the screened risk positions. When the number of the screened risk positions does not meet the requirements, it is determined that the regional risk coefficient of the said region does not meet the requirements; When the number of the screened risk positions meets the requirements: Determine the risk position aggregation coefficient in the said region based on the distances between different leakage risk positions in the said region. When the regional position aggregation coefficient in the said region does not meet the requirements, it is determined that the regional risk coefficient of the said region does not meet the requirements; When the regional position aggregation coefficient in the said region meets the requirements: Determine the average value of the leakage risk coefficients of the leakage risk positions in the said region based on the leakage risk coefficients of the leakage risk positions in the said region and use it as the average risk coefficient. Determine the regional risk coefficient of the said region based on the average risk coefficient and the regional position aggregation coefficient.
[0037] Furthermore, the method for determining the inspection and patrol processing strategy of the UAV in the said region is as follows: Based on the regional risk coefficient of the said region, determine the preset inspection and patrol strategy corresponding to the regional risk coefficient of the said region; Take the preset inspection and patrol strategy as the inspection and patrol processing strategy of the UAV in the said region.
[0038] Specifically, the inspection and patrol processing strategy includes the inspection and patrol processing time per unit length of the heat supply pipeline network.
[0039] It should be noted that the method for determining the real-time leakage risk of the inspection location is as follows: Based on the analysis result of the real-time infrared data of the inspection location, determine the infrared image features corresponding to the inspection location, and determine the reliability coefficient of the real-time monitoring data of the inspection location according to the matching situation between the infrared image features and the real-time monitoring data of the inspection location; According to the real-time monitoring data of the inspection location, determine the deviation situation of the monitoring data of the similar heating location of the inspection location, and use the deviation situation of the monitoring data of the similar heating location of the inspection location to determine the monitoring data deviation coefficient of the inspection location; Based on the monitoring data deviation coefficient and the reliability coefficient, determine the location risk coefficient of the inspection location, and use the location risk coefficient to determine the real-time leakage risk of the inspection location.
[0040] Furthermore, the monitoring data deviation coefficient of the inspection location is determined according to the average value of the deviation situation of the monitoring data of the similar heating location of the inspection location.
[0041] In addition, it should be noted that the location risk coefficient of the inspection location is determined according to the average value of the monitoring data deviation coefficient and the reliability coefficient.
[0042] It can be understood that using the location risk coefficient to determine the real-time leakage risk of the inspection location specifically includes: Obtain the risk coefficient interval corresponding to the location risk coefficient, and use the risk coefficient interval to determine the real-time leakage risk of the inspection location.
[0043] Furthermore, the real-time leakage risk includes the existence of leakage risk and the non-existence of leakage risk.
[0044] Specifically, the method for determining the suspected risk inspection location in the inspection location is as follows: Based on the analysis result of the real-time infrared data of the inspection location, determine the infrared image features of the real-time infrared data of the inspection location; According to the analysis result of the infrared image features, determine the preset leakage risk coefficient corresponding to the infrared image features, and use the preset leakage risk coefficient corresponding to the infrared image features as the image risk coefficient of the inspection location; Based on the image risk coefficient, determine the suspected risk inspection location in the inspection location.
[0045] Furthermore, based on the image risk coefficient, determining the suspected risk inspection location in the inspection location specifically includes: Take the inspection location where the image risk coefficient does not meet the requirements as the suspected risk inspection location.
[0046] Example 2 Second, this application provides an inspection and processing system for a heat supply pipeline network based on an unmanned aerial vehicle, which adopts the above-mentioned inspection and processing method for a heat supply pipeline network based on an unmanned aerial vehicle, and specifically includes: A risk coefficient evaluation module, a regional risk assessment module, and an inspection and processing module; Among them, the regional coefficient evaluation module is responsible for dividing the heat supply pipeline network into multiple regions, and using the analysis results of historical monitoring data of different regions to determine the leakage risk coefficients of different positions in the regions; When the regional risk assessment module is responsible for determining that there is a leakage risk position in the region based on the leakage risk coefficient, it obtains the distribution data of the leakage risk positions in the region, and combines the leakage risk coefficients of the leakage risk positions in the region to determine the regional risk coefficient of the region; The inspection and processing module is responsible for generating differentiated real-time leakage risks and inspection strategies according to the regional risk coefficients of the regions.
[0047] In the embodiments of the present invention, the term "multiple" refers to two or more, unless otherwise clearly defined. Terms such as "installation", "connection", and "fixation" should all be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific situations.
[0048] In the description of the embodiments of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, should not be construed as a limitation to the embodiments of the present invention.
[0049] In the description of this specification, the description of terms such as "one embodiment" and "one preferred embodiment" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.
[0050] The above are only the preferred embodiments of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention shall be included within the protection scope of the embodiments of the present invention.
Claims
1. A heating pipe network inspection method based on drones, characterized in that: Specifically include: Divide the heating network into multiple areas, and use the analysis results of historical monitoring data of different areas to determine the leakage risk coefficients of different locations in the areas; When it is determined based on the leakage risk coefficient that there is a leakage risk position in the area, obtaining distribution data of the leakage risk position in the area, and determining the regional risk coefficient of the area in combination with the leakage risk coefficient of the leakage risk position in the area; When the regional risk coefficient of an area does not meet the requirements, the regional risk coefficient of the area is used to determine the inspection and processing strategy of the drone in the area, and the real-time monitoring data of the inspection position and the analysis results of the real-time infrared data are used to determine the real-time leakage risks of different inspection positions in the area; When the regional risk coefficient of the area meets the requirements, the suspected risk inspection positions among the inspection positions are determined based on the analysis results of the real-time infrared data of different inspection positions, and the real-time infrared data of the suspected risk inspection positions and the real-time monitoring data are used to determine the real-time leakage risks of different suspected risk inspection positions in the area.
2. The inspection and processing method for a heating pipe network based on a drone according to claim 1, characterized in that: Divide the heating network into multiple areas, including: The heating network is divided into multiple areas according to preset areas.
3. The inspection and processing method for a heating pipe network based on a drone according to claim 1, characterized in that: The historical monitoring data includes flow and temperature at different locations in the area.
4. The inspection and processing method for a heating pipe network based on a drone according to claim 1, characterized in that: The method for determining the leakage risk coefficient of the position is: Determine similar heating locations of the location based on analysis data of the location in the heating network; Determining the deviation time of the monitoring data of the location according to the deviation between the historical monitoring data of the similar heating location and the historical monitoring data of the location; The leakage risk coefficient of the location is determined according to the monitoring data deviation time of the location.
5. The inspection and processing method for a heating pipe network based on a drone according to claim 4, characterized in that: A similar heating location of the location is determined based on the distance between the location and the heat exchange station.
6. The inspection and processing method for a heating pipe network based on a drone according to claim 1, characterized in that: The monitoring data deviation moment is the moment when the deviation between the historical monitoring data and the historical monitoring data of the similar heating location is not within a preset range.
7. The inspection and processing method for a heating pipe network based on a drone according to claim 1, characterized in that: The method for determining the regional risk coefficient of the region is: Determining the number of leakage risk locations in the area based on the distribution data of the leakage risk locations in the area, and determining the basic risk coefficient of the area based on the ratio of the number of leakage risk locations to a preset number; Determining an average value of the leakage risk coefficients of the leakage risk positions in the area according to the leakage risk coefficients of the leakage risk positions in the area, and using the average value of the leakage risk coefficients as the risk coefficient average value; A regional risk coefficient for the region is determined based on the risk coefficient average value and the basic risk coefficient.
8. The inspection and processing method for a heating pipe network based on a drone according to claim 7, characterized in that: The regional risk coefficient of the region is determined based on the product of the risk coefficient average value and the basic risk coefficient.
9. The inspection and processing method for a heating pipe network based on a drone according to claim 1, characterized in that: The method for determining the regional risk coefficient of the region is: Determining the number of leakage risk positions in the area based on the distribution data of the leakage risk positions in the area, and when the number of leakage risk positions in the area is greater than the preset number of risk positions, determining that the regional risk coefficient of the area does not meet the requirement; When the number of leakage risk locations in the area is not greater than the preset number of risk locations: When it is determined, based on the leakage risk coefficients of different leakage risk locations, that there is no leakage risk location in the area whose leakage risk coefficient is greater than a preset risk coefficient threshold: Determine an average value of leakage risk coefficients of leakage risk positions in the area, and use it as an average value of risk coefficients; when the average value of risk coefficients is less than a preset coefficient threshold, determine that the regional risk coefficient of the area meets the requirement; When the regional coefficient average value is not less than the preset coefficient threshold or there is a leakage risk location with a leakage risk coefficient greater than the preset risk coefficient threshold: The leakage risk positions with leakage risk coefficients greater than a preset risk coefficient threshold are used as screening risk positions, and when the number of the screening risk positions does not meet the requirement, it is determined that the regional risk coefficient of the area does not meet the requirement; When the number of risk screening locations meets the requirement: Determining the risk position clustering coefficient in the region according to the spacing between different leakage risk positions in the region, and when the regional position clustering coefficient in the region does not meet the requirement, determining that the regional risk coefficient of the region does not meet the requirement; When the regional location clustering coefficient in the region meets the requirements: According to the leakage risk coefficients of the leakage risk positions in the area, an average value of the leakage risk coefficients of the leakage risk positions in the area is determined and used as the risk coefficient average value, and the regional risk coefficient of the area is determined based on the risk coefficient average value and the regional position clustering coefficient.
10. A heating pipe network inspection and processing system based on drones, using a heating pipe network inspection and processing method based on drones according to any one of claims 1 to 9, characterized in that: Specifically include: Risk factor assessment module, regional risk assessment module, inspection processing module; The regional coefficient evaluation module is responsible for dividing the heating network into multiple regions and determining the leakage risk coefficients of different locations in the region using the analysis results of historical monitoring data of different regions; The regional risk assessment module is responsible for obtaining the distribution data of the leakage risk positions in the region when determining that there are leakage risk positions in the region based on the leakage risk coefficient, and determining the regional risk coefficient of the region in combination with the leakage risk coefficients of the leakage risk positions in the region; The inspection processing module is responsible for generating differentiated real-time leakage risks and inspection strategies according to the regional risk coefficient of the area.