Bee colony unmanned aerial vehicle urban pipeline inspection system

Abstract

The invention relates to the technical field of urban pipeline inspection, and particularly discloses a swarm unmanned aerial vehicle urban pipeline inspection system, which comprises a gas pipeline breakage degree analysis module, an unmanned aerial vehicle inspection task distribution module, a gas pipeline abnormity feedback module, a database and a pipeline maintenance effect judgment module, according to the method, the specific inspection task is allocated to the unmanned aerial vehicle according to the worn-out condition of each gas pipeline, the problems existing in the gas pipelines are evaluated in combination with the pipeline appearance abnormal condition, the pipeline leakage abnormal condition and the pipeline overheating abnormal condition of the gas pipelines, and after manual maintenance of the gas pipelines with the problems is completed, the unmanned aerial vehicle is subjected to inspection according to the inspection task. And the gas pipeline after maintenance is inspected again, so that personalized inspection requirements of the gas pipeline are met, potential safety hazards are helped to be found in time, the inspection efficiency of the unmanned aerial vehicle is improved, the comprehensiveness of safety assessment of the gas pipeline is improved, and a reliable basis is provided for long-term pipeline operation trend analysis and service life prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of urban pipeline inspection, and in particular to a swarm drone urban pipeline inspection system. Background Art

[0002] As a key infrastructure for urban energy supply, the safe operation of urban gas pipelines is of vital importance. Traditional gas pipeline inspection methods mainly rely on manual inspections, and workers need to walk or use vehicles to inspect the pipeline section by section. This method is extremely inefficient and labor-intensive. With the development of drone technology, although there have been some application attempts, most of the existing drone inspection systems have problems such as single function and poor coordination, which cannot meet the complex and large-scale inspection needs of urban gas pipelines. A more advanced and efficient inspection solution is urgently needed.

[0003] The following problems still exist in the existing technology: 1. Specific inspection tasks are not assigned to drones based on the dilapidated conditions of each gas pipeline in the urban area, which cannot meet the personalized inspection needs of gas pipelines and cannot timely discover potential safety hazards, resulting in low inspection efficiency and a sharp increase in safety risks, thereby shortening the life of the pipeline system and increasing maintenance costs, seriously affecting the safety and stability of urban gas supply.

[0004] 2. Failure to combine abnormal pipeline appearance, pipeline leakage and pipeline overheating to assess existing problems in the gas pipeline reduces the comprehensiveness of the gas pipeline safety assessment. Lack of comprehensive and accurate data support leads to inaccurate pipeline life prediction and risk assessment, which increases the danger of urban gas supply.

[0005] 3. After the manual inspection of the problematic gas pipeline is completed, the gas pipeline is not inspected again after the inspection. It is impossible to ensure the inspection effect of the gas pipeline, and it is impossible to confirm whether the repair has completely solved the problem. It is difficult to accurately evaluate the quality of the repair, and it is impossible to provide a reliable basis for long-term pipeline operation trend analysis and life prediction. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a swarm drone urban pipeline inspection system is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a swarm drone urban pipeline inspection system, including: a gas pipeline dilapidation analysis module, which is used to extract the pipeline location, service life, each repair report time point and historical repair number of each gas pipeline in each urban area in the target city, and analyze the corresponding dilapidation degree of each gas pipeline in each urban area.

[0008] The drone inspection task allocation module is used to confirm the drone inspection type corresponding to each gas pipeline in each urban area, and allocate inspection tasks to the drones corresponding to each gas pipeline in each urban area. When the drones corresponding to each gas pipeline in each urban area receive the corresponding inspection task instructions and pipeline locations, they automatically calibrate the GPS positioning and prepare for takeoff.

[0009] The gas pipeline abnormality feedback module is used to collect pipeline inspection information corresponding to each inspection of each gas pipeline in each urban area during the current cycle, confirm the abnormal problems existing in each gas pipeline in each urban area, and locate and feedback the gas pipelines with abnormal problems.

[0010] The database is used to store the cruising speed, cruising altitude and inspection frequency within a period corresponding to the key inspection type and the normal inspection type, store the surface temperature threshold of the gas pipeline under safe conditions, and store the gas safety concentration threshold corresponding to the gas pipeline.

[0011] The pipeline maintenance effect judgment module is used to record the gas pipeline with abnormal problems as the target gas pipeline after the maintenance is completed, and start the drones corresponding to the target gas pipelines in each urban area again to collect the pipeline maintenance completion information corresponding to the target gas pipelines in each urban area, and judge whether the maintenance effect of the target gas pipelines in each urban area meets the standards. If not, continue to provide positioning feedback.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention can meet the personalized inspection needs of gas pipelines by assigning specific inspection tasks to drones according to the dilapidated conditions of each gas pipeline in each urban area, help to timely discover potential safety hazards, improve the inspection efficiency of drones, reduce safety risks, and thus extend the life of the pipeline system, avoiding serious impact on the safety and stability of urban gas supply.

[0013] (2) The present invention evaluates the problems existing in the gas pipeline by combining the abnormal appearance of the gas pipeline, the abnormal leakage of the pipeline and the abnormal overheating of the pipeline, thereby improving the comprehensiveness of the gas pipeline safety assessment, improving the accuracy of the pipeline life prediction and risk assessment, and reducing the danger of urban gas supply.

[0014] (3) After the manual maintenance of the gas pipeline with problems is completed, the present invention conducts another inspection of the gas pipeline after the maintenance is completed, so as to ensure the maintenance effect of the gas pipeline, confirm whether the maintenance has completely solved the problem, help accurately evaluate the maintenance quality, and provide a reliable basis for long-term pipeline operation trend analysis and life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the system module structure connection of the present invention.

[0017] Figure 2 The present invention is a flow chart for judging whether the maintenance effect of the gas pipeline meets the standards.

[0018] Figure 3 This is a statistical flow chart of the number of serious leakage points of the present invention. DETAILED DESCRIPTION

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

[0020] See also Figure 1 As shown, the present invention provides a swarm drone urban pipeline inspection system, including: a gas pipeline dilapidation analysis module, a drone inspection task allocation module, a gas pipeline abnormality feedback module, a database and a pipeline maintenance effect judgment module.

[0021] The gas pipeline dilapidation analysis module is connected to the drone inspection task allocation module, the drone inspection task allocation module is connected to the gas pipeline abnormality feedback module, the gas pipeline abnormality feedback module is connected to the pipeline maintenance effect judgment module, and both the drone inspection task allocation module and the gas pipeline abnormality feedback module are connected to the database.

[0022] The gas pipeline dilapidation analysis module is used to extract the pipeline location, service life, each repair report time point and historical repair number of each gas pipeline in each urban area in the target city, and analyze the corresponding dilapidation degree of each gas pipeline in each urban area.

[0023] It should be noted that the pipeline location, service life, each repair reporting time point and historical repair reporting number of each gas pipeline in each urban area of ​​the target city are extracted from the gas pipeline management system of each urban area.

[0024] In a specific embodiment of the present invention, the specific process of analyzing the degree of dilapidation of each gas pipeline in each urban area is as follows: A1, the service life and the number of historical repair reports of each gas pipeline in each urban area of ​​the target city are recorded as N ij and ε ij , where i represents the number of the urban area, i=1,2,...,n, and j represents the number of the gas pipeline, j=1,2,...,m.

[0025] A2. Compare the repair time points of each gas pipeline in each urban area of ​​the target city to obtain the repair intervals of each gas pipeline in each urban area, and extract the minimum repair interval from them, which is recorded as T ij .

[0026] A3. Calculate the repair frequency β of each gas pipeline in each urban area ij , Among them, ε′ and T′ represent the historical number of repair reports and the repair interval length of the reference respectively.

[0027] A4. Calculate the dilapidated degree χ of each gas pipeline in each urban area ij , Among them, N′ and β′ represent the service life and repair frequency of the set reference respectively.

[0028] The drone inspection task allocation module is used to confirm the drone inspection type corresponding to each gas pipeline in each urban area, and allocate inspection tasks to the drones corresponding to each gas pipeline in each urban area. When the drones corresponding to each gas pipeline in each urban area receive the corresponding inspection task instructions and pipeline locations, they automatically calibrate the GPS positioning and prepare for takeoff.

[0029] In a specific embodiment of the present invention, the method for confirming the drone inspection type corresponding to each gas pipeline in each urban area is: comparing the degree of dilapidation corresponding to each gas pipeline in each urban area with the set reference degree of dilapidation; if the degree of dilapidation corresponding to a gas pipeline in a certain urban area is greater than the set reference degree of dilapidation, the drone inspection type corresponding to the gas pipeline in the urban area is recorded as a key inspection; otherwise, the drone inspection type corresponding to the gas pipeline in the urban area is recorded as a normal inspection, thereby obtaining the drone inspection type corresponding to each gas pipeline in each urban area.

[0030] In a specific embodiment of the present invention, the specific method of assigning inspection tasks to the drones corresponding to each gas pipeline in each urban area is: extracting the cruising speed, cruising altitude and inspection frequency within a cycle corresponding to the key inspection type and the normal inspection type from the database, and combining the drone inspection type corresponding to each gas pipeline in each urban area to obtain the cruising speed, cruising altitude and inspection frequency within a cycle of the drones corresponding to each gas pipeline in each urban area, and use them as the inspection tasks of the drones corresponding to each gas pipeline in each urban area.

[0031] The embodiment of the present invention can meet the personalized inspection needs of gas pipelines by assigning specific inspection tasks to drones according to the dilapidated conditions of each gas pipeline in each urban area, help to timely discover potential safety hazards, improve the inspection efficiency of drones, reduce safety risks, and thus extend the life of the pipeline system, avoiding serious impact on the safety and stability of urban gas supply.

[0032] The gas pipeline abnormality feedback module is used to collect pipeline inspection information corresponding to each inspection of each gas pipeline in each urban area during the current cycle, confirm the abnormal problems existing in each gas pipeline in each urban area, and locate and feedback the gas pipeline with abnormal problems.

[0033] In a specific embodiment of the present invention, the pipeline inspection information includes pipeline image information, pipeline leakage information and pipeline overheating information, wherein the pipeline image information includes the number of cracks, the length of each crack, the number of rusted areas and the area of ​​each rusted area, the pipeline leakage information is the gas concentration of each monitoring point, and the pipeline overheating information is the surface temperature of each pipeline section.

[0034] It should be noted that the number of cracks, the length of each crack, the number of rusted areas and the area of ​​each rusted area are collected in the following ways: 1) Image collection: The high-resolution industrial camera carried by the drone is used to collect gas pipeline images; 2) Crack number and length detection: The collected images are grayed out, and the color images are converted into grayscale images to reduce the amount of data and highlight the characteristics of the cracks. Advanced edge detection technologies such as the Canny edge detection algorithm are used. The algorithm can determine the edges of the cracks by finding places where the pixel intensity changes dramatically in the image. Morphological operations such as corrosion and expansion operations are used to separate the cracks from the background. Then, the connected domain analysis algorithm is used to mark adjacent crack pixels as a connected area, thereby realizing the statistics of the number of cracks. For the measurement of crack length, it can be used. According to the proportional relationship between pixels and actual length (this proportional relationship needs to be determined through prior calibration, for example, the number of pixels corresponding to the known pipe diameter can be used to infer), the pixel length of each crack in the image is calculated, and then the actual length is obtained. 3) Calculation of the number and area of ​​rusted areas: Since the color of the rusted part is different from the color of the normal surface of the pipe, the color space analysis can be used to extract the rusted area, and the initially extracted rusted area can be segmented and marked. The segmented rusted area can be marked by using a regional segmentation algorithm such as the watershed algorithm. The number of statistical marks is the number of rusted areas. For the area calculation of each rusted area, the actual rusted area can be calculated by counting the number of pixels in the area and then calculating the actual rusted area based on the proportional relationship between pixels and actual area (which also needs prior calibration).

[0035] It should also be noted that the gas concentration at each monitoring point is collected by a gas concentration detector carried by a drone, and the surface temperature of each pipeline section is collected by an infrared thermal imager carried by a drone.

[0036] In a specific embodiment of the present invention, the specific process of confirming the abnormal problems existing in each gas pipeline in each urban area is as follows: B1, extracting the number of cracks, the length of each crack, the number of rusted areas, the rusted areas, the gas concentration at each monitoring point and the surface temperature of each pipeline section from the pipeline inspection information corresponding to each inspection of each gas pipeline in each urban area in the current cycle, and calculating the pipeline appearance abnormality index δ of each gas pipeline in each urban area in the current cycle respectively. ij 、Pipeline leakage abnormality index θ ij and pipeline overheating abnormality index

[0037] In a specific embodiment of the present invention, the specific process of calculating the pipeline appearance abnormality index of each gas pipeline in each urban area in the current cycle is as follows: C1. Record the number of cracks corresponding to each inspection of each gas pipeline in each urban area in the current cycle as Wherein, r represents the number of each inspection, r=1,2,...,q.

[0038] C2. Extract the maximum value from the crack lengths corresponding to each inspection of each gas pipeline in each urban area during the current cycle and record it as L ijr .

[0039] C3. Calculate the cracking degree of each gas pipeline in each urban area in the current cycle in, and L′ represent the set permissible crack number and crack length respectively, e represents the natural constant, and q represents the number of inspections.

[0040] C4. Based on the number of rusted areas and the rusted areas corresponding to each inspection of each gas pipeline in each urban area in the current cycle, calculate the pipeline corrosion degree of each gas pipeline in each urban area in the current cycle.

[0041] It should be noted that the specific process of calculating the pipeline corrosion degree of each gas pipeline in each urban area in the current cycle is: the number of rusted parts corresponding to each inspection of each gas pipeline in each urban area in the current cycle is recorded as σ ijr .

[0042] The maximum value is extracted from each corrosion area corresponding to each inspection of each gas pipeline in each urban area in the current cycle, and is recorded as S ijr .

[0043] Calculate the corrosion degree of each gas pipeline in each urban area in the current cycle Among them, σ′ and S′ represent the number of permitted corrosion sites and the corrosion area of ​​the pipeline respectively.

[0044] C5. Calculate the pipeline appearance abnormality index δ of each gas pipeline in each urban area in the current cycle ij , Among them, a1 and a2 represent the weights of the pipeline appearance abnormality assessment corresponding to the set pipeline cracking degree and pipeline corrosion degree, respectively, and a1+a2=1.

[0045] In a specific embodiment of the present invention, a1 is set to 0.5, and a2 is set to 0.5. When actually calculating the pipeline appearance abnormality index, both the pipeline cracking degree and the pipeline corrosion degree are very important. In different scenarios, the importance of the two may be emphasized. Ideally, both factors should be considered comprehensively and given reasonable weights to accurately calculate the pipeline appearance abnormality index.

[0046] In a specific embodiment of the present invention, the specific process of calculating the pipeline leakage anomaly index of each gas pipeline in each urban area in the current cycle is: D1. Compare the gas concentration of each monitoring point corresponding to each inspection of each gas pipeline in each urban area in the current cycle with the gas safety concentration threshold corresponding to the gas pipeline stored in the database. If the gas concentration of a monitoring point corresponding to a certain inspection is greater than the gas safety concentration threshold corresponding to the gas pipeline, the monitoring point is recorded as a gas leakage point, and the gas leakage points corresponding to each inspection of each gas pipeline in each urban area in the current cycle are obtained.

[0047] D2. Record the same gas leakage points of each gas pipeline in each urban area in the current cycle as comprehensive leakage points, count the number of occurrences of each comprehensive leakage point of each gas pipeline in each urban area in the current cycle, and record it as M ijx , where x represents the number of the comprehensive leakage point, x=1,2,...,y.

[0048] D3. Calculate the occurrence frequency of each comprehensive leakage point of each gas pipeline in each urban area in the current cycle φ ijx ,

[0049] See also Figure 3 As shown, D4, the occurrence frequency of each comprehensive leakage point of each gas pipeline in each urban area in the current cycle is compared with the set permitted leakage point occurrence frequency. If the occurrence frequency of a comprehensive leakage point of a gas pipeline in a certain urban area in the current cycle is greater than the set permitted leakage point occurrence frequency, the comprehensive leakage point of the gas pipeline in the urban area in the current cycle is recorded as a serious leakage point. Otherwise, the comprehensive leakage point of the gas pipeline in the urban area in the current cycle is recorded as a non-serious leakage point. The number of serious leakage points of each gas pipeline in each urban area in the current cycle is counted and recorded as ζ ij .

[0050] D5. Calculate the pipeline leakage anomaly index θ of each gas pipeline in each urban area in the current cycle ij , Among them, K represents the proportion of serious leakage points within the set permission, and y represents the number of comprehensive leakage points.

[0051] In a specific embodiment of the present invention, the specific process of calculating the pipeline overheating abnormality index of each gas pipeline in each urban area in the current cycle is as follows: E1. Compare the surface temperature of each pipeline segment corresponding to each inspection of each gas pipeline in each urban area in the current cycle with the surface temperature threshold of the gas pipeline under safe conditions stored in the database. If the surface temperature of a pipeline segment corresponding to a certain inspection is greater than the surface temperature threshold of the gas pipeline under safe conditions, the pipeline segment corresponding to the inspection is recorded as an overheated pipeline segment. The number of overheated pipeline segments corresponding to each inspection of each gas pipeline in each urban area in the current cycle is counted and recorded as τ ijr .

[0052] E2. Calculate the pipeline overheating anomaly index corresponding to each inspection of each gas pipeline in each urban area during the current cycle Wherein, τ′ represents the number of superheated pipe sections for reference.

[0053] E3. Calculate the average of the pipeline overheating abnormality index corresponding to each inspection of each gas pipeline in each urban area in the current cycle to obtain the pipeline overheating abnormality index of each gas pipeline in each urban area in the current cycle.

[0054] B2. Construct an abnormal problem assessment model for each gas pipeline in each urban area, and output the existing abnormal problems. The abnormal problem assessment model is expressed as: Among them, P1, P2 and P3 represent the evaluation conditions of each abnormal problem, P1 represents δ ij >δ′, P2 represents θ ij >θ′, P3 indicates Among them, δ′, θ′ and They respectively represent the set reference pipeline appearance abnormality index, pipeline leakage abnormality index and pipeline overheating abnormality index.

[0055] The embodiments of the present invention evaluate the problems existing in the gas pipeline by combining the abnormal pipeline appearance, pipeline leakage and pipeline overheating of the gas pipeline, thereby improving the comprehensiveness of the gas pipeline safety assessment, improving the accuracy of pipeline life prediction and risk assessment, and reducing the danger of urban gas supply.

[0056] The database is used to store the cruising speed, cruising altitude and inspection frequency within a period corresponding to the key inspection type and the normal inspection type, store the surface temperature threshold of the gas pipeline under a safe condition, and store the gas safety concentration threshold corresponding to the gas pipeline. The data sources in the database of this embodiment are shown in Table 1.

[0057] Table 1 Data sources in the database

[0058]

[0059] The pipeline maintenance effect judgment module is used to record the gas pipeline with abnormal problems as the target gas pipeline after the maintenance is completed, and to start the drones corresponding to the target gas pipelines in each urban area again, to collect the pipeline maintenance completion information corresponding to the target gas pipelines in each urban area, and to judge whether the maintenance effect of the target gas pipelines in each urban area meets the standards. If not, positioning feedback will continue.

[0060] It should be noted that the pipeline maintenance completion information is consistent with the pipeline inspection information mentioned above, including: pipeline image information, pipeline leakage information and pipeline overheating information, wherein the pipeline image information includes the number of cracks, the length of each crack, the number of rusted areas and the area of ​​each rusted area, the pipeline leakage information is the gas concentration of each monitoring point, and the pipeline overheating information is the surface temperature of each pipeline section.

[0061] In a specific embodiment of the present invention, the specific process of judging whether the inspection effect of each target gas pipeline in each urban area meets the standard is as follows: F1. Based on the pipeline inspection completion information corresponding to each target gas pipeline in each urban area, the pipeline appearance abnormality index corresponding to each target gas pipeline in each urban area is calculated in the same way as the calculation method of the pipeline appearance abnormality index, pipeline leakage abnormality index and pipeline overheating abnormality index of each gas pipeline in each urban area in the current cycle. Pipeline leakage abnormality index and pipeline overheating abnormality index Wherein, g represents the serial number of the target gas pipeline, g=1,2,...,p.

[0062] F2. Calculate the maintenance effect evaluation index ψ of each target gas pipeline in each urban area ig , Among them, λ1, λ2 and λ3 represent the weights of the maintenance effect evaluation corresponding to the set pipeline appearance abnormality index, pipeline leakage abnormality index and pipeline overheating abnormality index, respectively, and λ1+λ2+λ3=1.

[0063] In a specific embodiment of the present invention, the setting value of λ1 is 0.35, the setting value of λ2 is 0.35, and the setting value of λ3 is 0.3. The importance of these three indexes varies in different evaluation stages and for different types of gas pipelines. But in general, from the perspective of safety and functionality, the pipeline leakage anomaly index is usually the most important in the long-term maintenance effect evaluation, while in the short-term evaluation, the pipeline appearance anomaly index and the pipeline leakage anomaly index are both more critical.

[0064] See also Figure 2As shown, F3, the maintenance effect evaluation index of each target gas pipeline in each urban area is compared with the set reference maintenance effect evaluation index. If the maintenance effect evaluation index of a target gas pipeline in a certain urban area is greater than or equal to the set reference maintenance effect evaluation index, it indicates that the maintenance effect of the target gas pipeline in the urban area is up to standard; otherwise, it indicates that the maintenance effect of the target gas pipeline in the urban area is not up to standard.

[0065] The embodiment of the present invention ensures the maintenance effect of the gas pipeline and confirms whether the maintenance has completely solved the problem, helps to accurately evaluate the maintenance quality, and provides a reliable basis for long-term pipeline operation trend analysis and life prediction.

[0066] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. The swarm drone urban pipeline inspection system is characterized by: include: The gas pipeline dilapidation analysis module is used to extract the pipeline location, service life, repair time points and historical repair times of each gas pipeline in each urban area of ​​the target city, and analyze the dilapidation degree of each gas pipeline in each urban area; The drone inspection task allocation module is used to confirm the drone inspection type corresponding to each gas pipeline in each urban area, and allocate inspection tasks to the drones corresponding to each gas pipeline in each urban area. When the drones corresponding to each gas pipeline in each urban area receive the corresponding inspection task instructions and pipeline locations, they automatically calibrate the GPS positioning and prepare for takeoff; The gas pipeline abnormality feedback module is used to collect pipeline inspection information corresponding to each inspection of each gas pipeline in each urban area in the current cycle, confirm the abnormal problems of each gas pipeline in each urban area, and locate and feedback the gas pipeline with abnormal problems; A database is used to store the cruising speed, cruising altitude and inspection frequency within a period corresponding to the key inspection type and the normal inspection type, store the surface temperature threshold of the gas pipeline under safe conditions, and store the gas safety concentration threshold corresponding to the gas pipeline; The pipeline maintenance effect judgment module is used to record the gas pipeline with abnormal problems as the target gas pipeline after the maintenance is completed, and start the drones corresponding to the target gas pipelines in each urban area again to collect the pipeline maintenance completion information corresponding to the target gas pipelines in each urban area, and judge whether the maintenance effect of the target gas pipelines in each urban area meets the standards. If not, continue to provide positioning feedback.

2. The swarm drone urban pipeline inspection system according to claim 1 is characterized by: The specific process of analyzing the degree of dilapidation of each gas pipeline in each urban area is as follows: A1. Record the service life and historical repair times of each gas pipeline in each urban area of ​​the target city as N ij and ε ij , where i represents the number of the urban area, i=1,2,...,n, and j represents the number of the gas pipeline, j=1,2,...,m; A2. Compare the repair time points of each gas pipeline in each urban area of ​​the target city to obtain the repair intervals of each gas pipeline in each urban area, and extract the minimum repair interval from them, which is recorded as T ij ; A3. Calculate the repair frequency β of each gas pipeline in each urban area ij , Among them, ε′ and T′ represent the historical number of repair reports and the repair interval length of the reference respectively; A4. Calculate the dilapidated degree χ of each gas pipeline in each urban area ij , Among them, N′ and β′ represent the service life and repair frequency of the set reference respectively.

3. The swarm drone urban pipeline inspection system according to claim 2 is characterized by: The method for confirming the drone inspection type corresponding to each gas pipeline in each urban area is: comparing the degree of dilapidation corresponding to each gas pipeline in each urban area with the set reference degree of dilapidation; if the degree of dilapidation corresponding to a gas pipeline in a certain urban area is greater than the set reference degree of dilapidation, the drone inspection type corresponding to the gas pipeline in the urban area is recorded as a key inspection; otherwise, the drone inspection type corresponding to the gas pipeline in the urban area is recorded as a normal inspection, thereby obtaining the drone inspection type corresponding to each gas pipeline in each urban area.

4. The swarm drone urban pipeline inspection system according to claim 3 is characterized by: The specific method of assigning inspection tasks to the drones corresponding to each gas pipeline in each urban area is as follows: extracting the cruising speed, cruising altitude and inspection frequency within a cycle corresponding to the key inspection type and the normal inspection type from the database, and combining the drone inspection type corresponding to each gas pipeline in each urban area to obtain the cruising speed, cruising altitude and inspection frequency within a cycle of the drones corresponding to each gas pipeline in each urban area, and use them as the inspection tasks of the drones corresponding to each gas pipeline in each urban area.

5. The swarm drone urban pipeline inspection system according to claim 2 is characterized by: The pipeline inspection information includes pipeline image information, pipeline leakage information and pipeline overheating information, wherein the pipeline image information includes the number of cracks, the length of each crack, the number of rusted areas and the area of ​​each rusted area, the pipeline leakage information is the gas concentration of each monitoring point, and the pipeline overheating information is the surface temperature of each pipeline section.

6. The swarm drone urban pipeline inspection system according to claim 5 is characterized by: The specific process of confirming the abnormal problems existing in each gas pipeline in each urban area is as follows: B1. Extract the number of cracks, length of each crack, number of rusted areas, rusted areas, gas concentration at each monitoring point and surface temperature of each pipeline section from the pipeline inspection information corresponding to each inspection of each gas pipeline in each urban area in the current cycle, and calculate the pipeline appearance abnormality index δ of each gas pipeline in each urban area in the current cycle. ij , pipeline leakage abnormal index θ ij and pipeline overheating abnormality index B2. Construct an abnormal problem assessment model for each gas pipeline in each urban area, and output the existing abnormal problems. The abnormal problem assessment model is expressed as: Among them, P1, P2 and P3 represent the evaluation conditions of each abnormal problem, P1 represents δ ij >δ′, P2 represents θ ij >θ′, P3 indicates Among them, δ′, θ′ and They respectively represent the set reference pipeline appearance abnormality index, pipeline leakage abnormality index and pipeline overheating abnormality index.

7. The swarm drone urban pipeline inspection system according to claim 6 is characterized by: The specific process of calculating the pipeline appearance abnormality index of each gas pipeline in each urban area in the current cycle is: C1. Record the number of cracks corresponding to each inspection of each gas pipeline in each urban area in the current cycle as Wherein, r represents the number of each inspection, r = 1, 2, ..., q; C2. Extract the maximum value from the crack lengths corresponding to each inspection of each gas pipeline in each urban area during the current cycle and record it as L ijr ; C3. Calculate the cracking degree of each gas pipeline in each urban area in the current cycle in, and L′ represent the number of cracks and length of cracks allowed, respectively, e represents the natural constant, and q represents the number of inspections; C4. Based on the number of rusted areas and the rusted areas corresponding to each inspection of each gas pipeline in each urban area in the current cycle, calculate the pipeline corrosion degree of each gas pipeline in each urban area in the current cycle. C5. Calculate the pipeline appearance anomaly index δ of each gas pipeline in each urban area in the current cycle ij , Wherein, a1 and a2 represent the weights of the pipeline appearance abnormality assessment corresponding to the set pipeline cracking degree and pipeline corrosion degree, respectively, and a1+a2=1.

8. The swarm drone urban pipeline inspection system according to claim 7 is characterized by: The specific process of calculating the pipeline leakage anomaly index of each gas pipeline in each urban area in the current cycle is: D1. Compare the gas concentration of each monitoring point corresponding to each inspection of each gas pipeline in each urban area in the current cycle with the gas safety concentration threshold corresponding to the gas pipeline stored in the database. If the gas concentration of a monitoring point corresponding to a certain inspection is greater than the gas safety concentration threshold corresponding to the gas pipeline, then the monitoring point is recorded as a gas leakage point, and the gas leakage points corresponding to each inspection of each gas pipeline in each urban area in the current cycle are obtained; D2. Record the same gas leakage points of each gas pipeline in each urban area in the current cycle as comprehensive leakage points, count the number of occurrences of each comprehensive leakage point of each gas pipeline in each urban area in the current cycle, and record it as M ijx , where x represents the number of the comprehensive leakage point, x=1,2,...,y; D3. Calculate the occurrence frequency of each comprehensive leakage point of each gas pipeline in each urban area in the current cycle φ ijx , D4. Compare the occurrence frequency of each comprehensive leakage point of each gas pipeline in each urban area in the current cycle with the set permitted leakage point occurrence frequency. If the occurrence frequency of a comprehensive leakage point of a gas pipeline in a certain urban area in the current cycle is greater than the set permitted leakage point occurrence frequency, then the comprehensive leakage point of the gas pipeline in the urban area in the current cycle is recorded as a serious leakage point. Otherwise, the comprehensive leakage point of the gas pipeline in the urban area in the current cycle is recorded as a non-serious leakage point. The number of serious leakage points of each gas pipeline in each urban area in the current cycle is counted and recorded as ζ ij ; D5. Calculate the pipeline leakage anomaly index θ of each gas pipeline in each urban area in the current cycle ij , Among them, K represents the proportion of serious leakage points within the set permission, and y represents the number of comprehensive leakage points.

9. The swarm drone urban pipeline inspection system according to claim 8 is characterized by: The specific process of calculating the pipeline overheating abnormality index of each gas pipeline in each urban area in the current cycle is: E1. Compare the surface temperature of each pipeline section corresponding to each inspection of each gas pipeline in each urban area in the current cycle with the surface temperature threshold of the gas pipeline under safe conditions stored in the database. If the surface temperature of a pipeline section corresponding to a certain inspection is greater than the surface temperature threshold of the gas pipeline under safe conditions, the pipeline section corresponding to the inspection is recorded as an overheated pipeline section. The number of overheated pipeline sections corresponding to each inspection of each gas pipeline in each urban area in the current cycle is counted and recorded as τ. ijr ; E2. Calculate the pipeline overheating anomaly index corresponding to each inspection of each gas pipeline in each urban area during the current cycle Among them, τ′ represents the number of superheated pipe sections set as reference; E3. Calculate the average of the pipeline overheating abnormality index corresponding to each inspection of each gas pipeline in each urban area in the current cycle to obtain the pipeline overheating abnormality index of each gas pipeline in each urban area in the current cycle.

10. The swarm drone urban pipeline inspection system according to claim 9 is characterized by: The specific process of judging whether the inspection and maintenance effect of each target gas pipeline in each urban area meets the standard is as follows: F1. Based on the pipeline maintenance completion information corresponding to each target gas pipeline in each urban area, the pipeline appearance abnormality index corresponding to each target gas pipeline in each urban area is calculated in the same way as the calculation method of the pipeline appearance abnormality index, pipeline leakage abnormality index and pipeline overheating abnormality index of each gas pipeline in each urban area in the current cycle. Pipeline leakage abnormality index and pipeline overheating abnormality index Wherein, g represents the number of the target gas pipeline, g = 1, 2, ..., p; F2. Calculate the maintenance effect evaluation index ψ of each target gas pipeline in each urban area ig , Among them, λ1, λ2 and λ3 represent the weights of the maintenance effect evaluation corresponding to the set pipeline appearance abnormality index, pipeline leakage abnormality index and pipeline overheating abnormality index, respectively, λ1+λ2+λ3=1; F3. Compare the maintenance effect evaluation index of each target gas pipeline in each urban area with the set reference maintenance effect evaluation index. If the maintenance effect evaluation index of a target gas pipeline in an urban area is greater than or equal to the set reference maintenance effect evaluation index, it indicates that the maintenance effect of the target gas pipeline in the urban area meets the standard; otherwise, it indicates that the maintenance effect of the target gas pipeline in the urban area does not meet the standard.

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